Innovative Knowledge Management: Concepts for Organizational Creativity and Collaborative Design (Premier Reference Source)

Innovative Knowledge Management:

Concepts for Organizational Creativity and Collaborative Design Alan Eardley Staffordshire University, UK Lorna Uden Staffordshire University, UK

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Published in the United States of America by Information Science Reference (an imprint of IGI Global) 701 E. Chocolate Avenue Hershey PA 17033 Tel: 717-533-8845 Fax: 717-533-8661 E-mail: [email protected] Web site: http://www.igi-global.com Copyright © 2011 by IGI Global. All rights reserved. No part of this publication may be reproduced, stored or distributed in any form or by any means, electronic or mechanical, including photocopying, without written permission from the publisher. Product or company names used in this set are for identification purposes only. Inclusion of the names of the products or companies does not indicate a claim of ownership by IGI Global of the trademark or registered trademark. Library of Congress Cataloging-in-Publication Data Innovative knowledge management : concepts for organizational creativity and collaborative design / Alan Eardley and Lorna Uden, editors. p. cm. Includes bibliographical references and index. Summary: "This book provides a valuable resource for promoting current academic discourse on innovation in knowledge-intensive organizations and contexts"--Provided by publisher. ISBN 978-1-60566-701-0 (hardcover) -- ISBN 978-1-60566-702-7 (ebook) 1. Knowledge management. 2. Creative ability in business. 3. Technological innovations. I. Eardley, Alan. II. Uden, Lorna, 1946HD30.2.I5365 2011 658.4'038--dc22 2010041635

British Cataloguing in Publication Data A Cataloguing in Publication record for this book is available from the British Library. All work contributed to this book is new, previously-unpublished material. The views expressed in this book are those of the authors, but not necessarily of the publisher.

Table of Contents

Foreword . ..........................................................................................................................................xvii Preface . ................................................................................................................................................ xx Acknowledgment............................................................................................................................. xxxvi Section 1 Knowledge Management and Innovation Chapter 1 Universities as Knowledge-Intensive Learning Organizations................................................................ 1 Constantin Bratianu, Academy of Economic Studies, Romania Chapter 2 Key Characteristics Relevant for Selecting Knowledge Management Software Tools......................... 18 Hanlie Smuts, University of South Africa and Mobile Telephone Networks (Pty) Ltd, South Africa Alta van der Merwe, University of South Africa and Meraka Institute, CSIR, South Africa Marianne Loock, University of South Africa, South Africa Chapter 3 Knowledge-Based Diffusion in Practice: A Case Study Experience..................................................... 40 Hilary Berger, University of Wales Institute Cardiff, UK Paul Beynon-Davies, Cardiff University, UK Chapter 4 Deploying Knowledge Management in R&D Workspaces................................................................... 56 Won-Chen Chang, National Cheng Kung University, Taiwan Sheng-Tung Li, National Cheng Kung University, Taiwan Chapter 5 Innovation in New Technology and Knowledge Management: Comparative Case Studies of its Evolution during a Quarter Century of Change..................................................................................... 77 Sean Tung-Xiung Wu, Shih Hsin University, Taiwan

Section 2 Applications of Knowledge Management Chapter 6 A Survey of Epistemology and its Implications for an Organizational Information and Knowledge Management Model............................................................................................................................... 95 Ah-Lian Kor, Leeds Metropolitan University, UK Graham Orange, Leeds Metropolitan University, UKa Chapter 7 An Ontology-Based Expert System for Financial Statements Analysis.............................................. 125 Li-Yen Shue, National Kaoshiung First University of Science and Technology, Taiwan Ching-Wen Chen, National Kaoshiung First University of Science and Technology, Taiwan Chao-Hen Hsueh, National Kaoshiung First University of Science and Technology, Taiwan Chapter 8 Knowledge Democracy as the New Mantra in Product Innovation: A Framework of Processes and Competencies........................................................................................................... 141 Angelo Corallo, University of Salento, Italy Marco De Maggio, University of Salento, Italy Alessandro Margherita, University of Salento, Italy Chapter 9 Knowledge Management under Institutional Pressures: The Case of the Smartcard in France.......... 157 Rémy Magnier-Watanabe, University of Tsukuba, Japan Dai Senoo, Tokyo Institute of Technology, Japan Chapter 10 Does Knowledge Management Really Work? A Case Study in the Breast Cancer Screening Domain................................................................................................................................ 177 V. Baskaran, Ryerson University, Canada R.N.G. Naguib, Coventry University, UK A. Guergachi, Ryerson University, Canada R.K. Bali, Coventry University, UK H. Arochen, Coventry University, UK Chapter 11 Knowledge Management: The Key to Delivering Superior Healthcare Solutions.............................. 190 Nilmini Wickramasinghe, RMIT University, Australia Chapter 12 The Use of ‘Web 2.0’ and Social Software in Support of Professional Learning Communities......... 204 Alan Eardley, Staffordshire University, UK Lorna Uden, Staffordshire University, UK

Chapter 13 Knowledge Sharing in the Learning Process: Experience with Problem-Based Learning.................. 215 Lorna Uden, Staffordshire University, UK Alan Eardley, Staffordshire University, UK Chapter 14 Culturally-Bound Innovation in Romanian Teaching and Research Hospitals................................... 230 Mihaela Cornelia Dan, Academy of Economic Studies, Romania Simona Vasilache, Academy of Economic Studies, Romania Alina Mihaela Dima, Academy of Economic Studies, Romania Section 3 Creativity and Collaboration in Organizations Chapter 15 Exploiting KM in Support of Innovation and Change......................................................................... 242 Peter A.C. Smith, The Leadership Alliance Inc., UK Elayne Coakes, University of Westminster, UK Chapter 16 Knowledge Management Profile: An Innovative Approach to Map Knowledge Management Practice........................................................................................................................... 253 Zoltán Gaál, University of Pannonia, Hungary Lajos Szabó, University of Pannonia, Hungary Nóra Obermayer-Kovács, University of Pannonia, Hungary Zoltán Kovács, University of Pannonia, Hungary Anikó Csepregi, University of Pannonia, Hungary Chapter 17 Recognizing Innovation through Social Network Analysis: The Case of the Virtual eBMS Project....................................................................................................................................... 264 Grippa Francesca, University of Salento, Italy Elia Gianluca, University of Salento, Italy Chapter 18 Complexity and Clarity: The Knowledge Strategy Dilemma – Some Help from MaKE................... 286 Peter Sharp, Regents Business School London, UK Alan Eardley, Staffordshire University, UK Hanifa Shah, Staffordshire University, UK

Chapter 19 Knowledge Management and Innovation............................................................................................ 300 Lorna Uden, Staffordshire University, UK Marja Naaranoja, Vaasa University of Applied Sciences, Finland Chapter 20 Holonic Management: Innovation and Creative Entrepreneurship..................................................... 319 Akira Kamoshida, Tokyo Institute of Technology & Nagoya University of Commerce and Business, Japan Compilation of References ............................................................................................................... 334 About the Contributors .................................................................................................................... 375 Index.................................................................................................................................................... 384

Detailed Table of Contents

Foreword . ..........................................................................................................................................xvii Preface . ................................................................................................................................................ xx Acknowledgment............................................................................................................................. xxxvi Section 1 Knowledge Management and Innovation Chapter 1 Universities as Knowledge-Intensive Learning Organizations................................................................ 1 Constantin Bratianu, Academy of Economic Studies, Romania The purpose of this chapter is to critically analyze the universities as knowledge intensive learning organizations. It is axiomatic that universities are knowledge organizations since by their own nature universities create, acquire, and transfer knowledge in complex ways. They are knowledge intensive organizations since the density of knowledge field and the dynamics of knowledge processing are much greater than many other organizations. Since learning is one of the major processes within any university, people may consider universities as being by definition learning organizations. This idea induced by a semantic halo effect may lead to a major error. Although a university is an organization based on learning processes, it is not necessary a learning organization. This paper performs a functional analysis of the specific knowledge processes in order to identify the necessary conditions for a generic university to become a learning organization. Chapter 2 Key Characteristics Relevant for Selecting Knowledge Management Software Tools......................... 18 Hanlie Smuts, University of South Africa and Mobile Telephone Networks (Pty) Ltd, South Africa Alta van der Merwe, University of South Africa and Meraka Institute, CSIR, South Africa Marianne Loock, University of South Africa, South Africa The shift to innovation and knowledge as the primary source of value results in the new economy being led by those who manage knowledge effectively. Today’s organizations are creating and leveraging

knowledge, data, and information at an unprecedented pace—a phenomenon that makes the use of technology not an option, but a necessity. Software tools in knowledge management (KM) are a collection of technologies and are not necessarily acquired as a single software solution. Furthermore, these KM software tools have the advantage of using the organization’s existing information technology infrastructure. Organizations and business decision makers spend a great deal of resources and make significant investments in the latest technology, systems, and infrastructure to support KM. It is imperative that these investments are validated properly, made wisely, and that the most appropriate technologies and software tools are selected or combined to facilitate KM, knowledge creation, and continuous innovation. In this chapter, a set of characteristics are proposed that should support decision makers in the selection of software tools for knowledge creation. These characteristics were derived from both in-depth interviews and existing theory in publications. Chapter 3 Knowledge-Based Diffusion in Practice: A Case Study Experience..................................................... 40 Hilary Berger, University of Wales Institute Cardiff, UK Paul Beynon-Davies, Cardiff University, UK This chapter uses a case study to consider how development methods shape information systems practice and how organizations adapt, deploy, and use such knowledge in situ. The authors explore how an information system development method (ISDM) acting as a de-contextualized “knowledge bundle” is diffused and infused within an organization through the process of contextualization. The case study looks at a regional government project responsible for the distribution of European Community (EC) monies through agricultural grants and subsidies. A new IT/IS system was designed and developed to improve the administration and management of the EC’s agricultural policy across the region. A longitudinal research project was conducted over three years and was situated within the project environment. It involved a sustained period of fieldwork (nine months of intensive observations), and data was collected through 126 semi-structured interviews, shadowing of key participants, and informal discussions and conversations. Secondary data involved an in-depth and systematic analysis of published literature, project documentation, and artifacts. The authors consider how the structure and culture of organizations affect implementation and processes of diffusion and infusion. Chapter 4 Deploying Knowledge Management in R&D Workspaces................................................................... 56 Won-Chen Chang, National Cheng Kung University, Taiwan Sheng-Tung Li, National Cheng Kung University, Taiwan The active and effective management of valuable knowledge is widely believed to be a core competency for solidifying the competitive advantage of an organization. Whether knowledge management (KM) is a new idea or just a recycled concept per se both managerial and academic campuses have sought a vast array of KM strategies, solutions, frameworks, processes, barriers and enablers, IT tools and measurements over the past decade. Although there are many KM studies for both public and private sectors, most of them focus on the practice of international companies and western experiences, relatively few cases are reported on KM deployment and implementation in the Chinese community, especially for knowledge intensive research and development (R&D) institutes whose missions are to

serve traditional industries. To reveal some of the accomplishments gained in the Asia-Pacific region, this chapter presents and discusses the lessons learned from a particular case study in fostering the KM initiative and system in a research-oriented institute serving the metal industry. Chapter 5 Innovation in New Technology and Knowledge Management: Comparative Case Studies of its Evolution during a Quarter Century of Change..................................................................................... 77 Sean Tung-Xiung Wu, Shih Hsin University, Taiwan The research on which this chapter is based monitors the evolution of IT innovations and their effect on human emotions, including longitudinal influential factors, and examines some of the resulting syndromes, which are termed Computer Fear Syndrome (CFS) and User Alienation Syndrome (UAS). The research involves an analysis of the empirical data derived from several case studies and concludes with a funnel model that explains appropriate management action and puts forward new ideas for developing knowledge management systems in a variety of organizations that may alleviate or prevent such syndromes in the work place. Section 2 Applications of Knowledge Management Chapter 6 A Survey of Epistemology and its Implications for an Organizational Information and Knowledge Management Model............................................................................................................................... 95 Ah-Lian Kor, Leeds Metropolitan University, UK Graham Orange, Leeds Metropolitan University, UKa This is a theoretical chapter which aims to integrate various epistemologies from the philosophical, knowledge management, cognitive science, and educational perspectives. From a survey of knowledge-related literature, this chapter collates diverse views of knowledge. This is followed by categorising as well as ascribing attributes (effability, codifiability, perceptual/conceptual, social/personal) to the different types of knowledge. The authors develop a novel Organisational Information and Knowledge Management Model which seeks to clarify the distinctions between information and knowledge by introducing novel information and knowledge conversions (information-nothing, information-information, information-knowledge, knowledge-information, knowledge-knowledge) and providing mechanisms for individual knowledge creation and information sharing (between individual-individual, individual-group, group-group) as well as Communities of Practice within an organisation. Chapter 7 An Ontology-Based Expert System for Financial Statements Analysis.............................................. 125 Li-Yen Shue, National Kaoshiung First University of Science and Technology, Taiwan Ching-Wen Chen, National Kaoshiung First University of Science and Technology, Taiwan Chao-Hen Hsueh, National Kaoshiung First University of Science and Technology, Taiwan

Financial statements provide the main source of information for all parties who are interested in the performance of a company, including its managers, creditors, and equity investors. Although each of these parties may have different perspectives when viewing financial statements, all parties are concerned with the financial quality of an enterprise, which requires carefully analyzing financial statements to estimate and predict future conditions and performance. When analyzing financial statements, due to the complexity of the task, even professional analysts may be subject to constraints of subjective views, physical and mental fatigue, or possible environmental factors, and are not able to provide consistent appraisals. As a result, researchers and practitioners have resorted to expert systems to imitate the decision processes and inferencing logics of financial experts. Chapter 8 Knowledge Democracy as the New Mantra in Product Innovation: A Framework of Processes and Competencies........................................................................................................... 141 Angelo Corallo, University of Salento, Italy Marco De Maggio, University of Salento, Italy Alessandro Margherita, University of Salento, Italy In this chapter we carry out a critical analysis of “knowledge democracy” as a new mantra or buzz-word in product innovation leadership. A new paradigm has revolutionized the traditional process of invention, which was previously associated with a hierarchical dissemination of new ideas and competitive hoarding of knowledge assets. This chapter contends that at this environment has been replaced by a collaboration economy (based on so-called “wikinomics”) in which democracy governs the process of knowledge creation and its strategic application. Leadership in product innovation does not rely on the innate internal qualities of organizations, but on the collaborative contribution of stakeholders in many of the activities that make up the NPD lifecycle. The authors suggest a new approach to mitigate factors that can otherwise reduce the value of the NPD process. The chapter then examines how to promote such open collaboration through the development of a new managerial mindset, the acquisition of new distinctive competences, the development of new organizational models, and the management of new collaborative technologies. The authors’ proposed framework of processes and competencies offers the potential for organizations to meet these needs. Chapter 9 Knowledge Management under Institutional Pressures: The Case of the Smartcard in France.......... 157 Rémy Magnier-Watanabe, University of Tsukuba, Japan Dai Senoo, Tokyo Institute of Technology, Japan This chapter explores how knowledge management, an enabler of change due to its knowledge creation capability, is subject to several forces that shape its processes and outcomes. A qualitative analysis based on data from a case study of the first major rollout of smartcard technology in France shows how institutional isomorphic pressures affect not only knowledge management processes but also resulting innovations. Government impetus, legal authorities, and cultural expectations in French society produced coercive isomorphic pressures on the credit card industry, while existing credit card solutions, systems, and standards played the role of mimetic pressures, and professional networks and network externalities acted as normative pressures. The study suggests that a systems perspective which ac-

knowledges these institutional isomorphic pressures can lead to greater strategic alignment and can provide a basis for meaningful differentiation and competitive advantage. Chapter 10 Does Knowledge Management Really Work? A Case Study in the Breast Cancer Screening Domain................................................................................................................................ 177 V. Baskaran, Ryerson University, Canada R.N.G. Naguib, Coventry University, UK A. Guergachi, Ryerson University, Canada R.K. Bali, Coventry University, UK H. Arochen, Coventry University, UK Contemporary organizations, including those involved with healthcare, are constantly under pressure to produce and implement new strategies for delivering better products and/or services. Knowledge Management (KM) has been one of the paradigms successfully applied in such business environs. However, a lack of proper application of KM principles and its components have reduced the confidence of new adopters of this paradigm. KM-based healthcare projects are moving forward, and innovation is the driving force behind such initiatives. This chapter sets the scene by outlining the KM’s core elements, facets and how they can be appropriately applied within an innovative, real-time healthcare project. It further enumerates a case study which targets the screening attendance issue for the NHS’ breast screening program. The case study not only discusses the need of a balanced approach to address both the technological and humanistic aspects of KM, but also answers the question “Does knowledge management really work?” A questionnaire-based study was conducted with the General Physicians (GPs) on the KM’s aspects and its relationship to the interventions proposed in the study. The study provided ample proof that a balanced approach will definitely increase the efficacy of such initiatives. Such studies can increase the confidence of future KM adopters in healthcare domain. This chapter provides credibility for such balanced KM-based initiatives and highlights the importance of a focused approach on the various facets of KM to maximize benefits. Chapter 11 Knowledge Management: The Key to Delivering Superior Healthcare Solutions.............................. 190 Nilmini Wickramasinghe, RMIT University, Australia The proliferation of ICT (information communication technologies) throughout the business environment has lead to exponentially increasing amounts of data and information generation. Although these technologies were implemented to enhance and facilitate superior decision making, the result is information chaos and information overload; the productivity paradox (O’Brien, 2005; Laudon & Laudon, 2004; Jessup & Valacich, 2005; Haag et al. 2004). Knowledge management (KM) is a modern management technique designed to make sense of this information chaos by applying strategies, structures and techniques to apparently unrelated and seemingly irrelevant data elements and information in order to extract germane knowledge to aid superior decision making. Critical to knowledge management is the application of ICT. However it is the configuration of these technologies that is important to support the techniques of knowledge management. This chapter discusses how the process oriented knowledge

generation framework of Boyd and the use of sophisticated ICT can enable the design of a networkcentric healthcare perspective that enables effective and efficient healthcare operations. Chapter 12 The Use of ‘Web 2.0’ and Social Software in Support of Professional Learning Communities......... 204 Alan Eardley, Staffordshire University, UK Lorna Uden, Staffordshire University, UK This chapter examines the ‘happy convergence’ of two emerging social and technological trends. The first is the evolution of educational processes and methods from a traditional didactic approach towards a paradigm that seeks to empower the learner and enable a more involving learning experience to take place. This paradigm includes such approaches as student-centred learning, collaborative learning and problem-based learning. The second is the development of IT-based systems that enable the democratic involvement of end-users in their development and use and that encourage computer-mediated collaboration between individuals and groups having a common interest in a domain. Initially, at least, the main purpose of such software was for social networking and leisure purposes, but the chapter identifies a number of instances of its use in practice for professional education purposes. The chapter then highlights some examples of professional learning communities in practice in UK educational institutions. It concludes by speculating on and discussing some possible future trends in the use of social software for professional learning and by summarising the phenomenon and identifying the factors that distinguish it from other approaches to learning. Chapter 13 Knowledge Sharing in the Learning Process: Experience with Problem-Based Learning.................. 215 Lorna Uden, Staffordshire University, UK Alan Eardley, Staffordshire University, UK Knowledge is the most important resource of an organisation. The exchange of knowledge and knowledge management enhance organisational learning that in turn leads to innovation. Central to knowledge management is the concept of knowledge sharing. The future of knowledge sharing is not technical, but social. Knowledge sharing is fundamental to learning among students. This paper begins with a brief review of knowledge sharing, followed by the importance of knowledge sharing for learning, especially in problem-based learning. The authors then describe how successful knowledge sharing can be achieved for students to share knowledge in problem-based learning. The paper concludes with implications for effective knowledge sharing for student learning. Chapter 14 Culturally-Bound Innovation in Romanian Teaching and Research Hospitals................................... 230 Mihaela Cornelia Dan, Academy of Economic Studies, Romania Simona Vasilache, Academy of Economic Studies, Romania Alina Mihaela Dima, Academy of Economic Studies, Romania This chapter discusses innovation in the Romanian healthcare sector, from the point of view of organizational learning, which is influenced by the components of organizational culture. Starting from the

premise that hospital organizational culture differs from other types of organizations, we investigated the perceptions of a mixed sample of doctors and nurses from an internal medicine clinic of a large teaching and research hospital. The Dimensions of the Learning Organization Questionnaire and items selected from a questionnaire developed by the authors were used in order to study how the two groups perceived organizational culture and, subsequently, innovation, as both a component and a result of it. The results of the study show differences in perception between physicians and nurses, consistent with the ones presented in literature, and account for which facets of hospital organizational culture affect learning easiness versus which factors are negatively correlated with it. Section 3 Creativity and Collaboration in Organizations Chapter 15 Exploiting KM in Support of Innovation and Change......................................................................... 242 Peter A.C. Smith, The Leadership Alliance Inc., UK Elayne Coakes, University of Westminster, UK This chapter emphasizes the importance of formally promoting close social interaction and open knowledge sharing to achieve superior innovation capability. It does so by discussing the advantages of developing Communities of Innovation and citing a case study that exemplifies these concepts. This chapter addresses the challenges and opportunities faced by businesses in today’s complex and often unpredictable business environments. For success, an organization must be able to combine and recombine their resources in novel ways, eliminating or reconfiguring resources that are no longer relevant, and acquiring new resources. An organization’s capability to change by manipulating resources continuously and rapidly—to innovate—is a competitive advantage that is not readily imitated by competitors. Innovation is critical to an organization’s viability since it enables the development and introduction of new products and services and thus enables an organization to maintain, or improve, its current business position. The chapter reviews the numerous theories of change and change management in the literature based on practice and precept. However, research shows that competitive advantage is increasingly located by authorities in an organization’s intellectual resources including the skill base, business systems and intellectual property of its employees: its Human Capital. Organizational innovation depends on the individual and collective know-how of employees, and innovation is characterised by an iterative process of people working together, sharing insights, and building on the creative ideas of one another. The chapter emphasizes that an organization’s intellectual resources have significant potential to realize innovation and change capabilities, but that the impact of these capabilities largely depends on the means of an organization to foster close community social interaction and open knowledge sharing, and to leverage its informal leadership as a precursor to and part of any related Knowledge Management (KM) initiative.

Chapter 16 Knowledge Management Profile – An Innovative Approach to Map Knowledge Management Practice........................................................................................................................... 253 Zoltán Gaál, University of Pannonia, Hungary Lajos Szabó, University of Pannonia, Hungary Nóra Obermayer-Kovács, University of Pannonia, Hungary Zoltán Kovács, University of Pannonia, Hungary Anikó Csepregi, University of Pannonia, Hungary For knowledge-intensive organizations, it is important to carry out an objective assessment of their current position in the area of knowledge management activities and processes. Uncertainty presents a barrier to the introduction of suitable activities for improving knowledge management. We believe that the results of the research will be significant to practice and will provide substantial support for leaders and managers. Moreover the right knowledge management activities can help push thinking beyond the everyday in a way that spurs innovative creativity. To ensure success and long-term existence of any organizations effective application of organizational knowledge and knowledge management practice is of critical importance. Besides simply assessing the benefits inherent in knowledge management, the organizations must learn to recognize and manage the different areas of their knowledge management practice. Our innovative solution, the “Knowledge Management Profile” is devoted to the formulation of a new knowledge management maturity model, which is believed to be of vital importance in the quest of the successful knowledge management practice. Chapter 17 Recognizing Innovation through Social Network Analysis: The Case of the Virtual eBMS Project....................................................................................................................................... 264 Grippa Francesca, University of Salento, Italy Elia Gianluca, University of Salento, Italy Advances in communication technologies have enabled organizations to develop and operate decentralized organizational structures by supporting coordination among workers in different locations. Such developments have lessened formality in control structures and replaced formal channels of communication with less formal social networks. The chapter describes the development and application of a ‘Social Network Scorecard’ (SNS) managerial tool to monitor social interchanges and relationships within and across organizations in order to assess the effectiveness of knowledge networks. In this chapter, a project team made up of individuals from academia and industry collaboratively implemented an integrated technological platform for KM, e-Learning, e-Business, and project management disciplines in a higher education environment. This VeBMS platform, consisting of a collaborative working environment within the University of Salento, Italy, was used as a ‘test bed’ to evaluate the validity of the scorecard in practice. The chapter describes how the SNS tool can help in monitoring the evolution of an organizational community, recognizing creative roles and initiatives, and tracing the connections between such initiatives and innovative outcomes. Looking at trends at individual, team, inter-organizational, and organizational levels, researchers identified the most innovative phases within the team’s life cycle using network indicators like density and degree centrality. The SNS provided

feedback on the effectiveness of the team and helped discover the phases in which the team acted in a manner conducive to innovation. The Virtual eBMS project team followed the typical structure of an innovative knowledge network where learning networks and innovation networks co-exist with a more sparse interest network. Chapter 18 Complexity and Clarity: The Knowledge Strategy Dilemma – Some Help from MaKE................... 286 Peter Sharp, Regents Business School London, UK Alan Eardley, Staffordshire University, UK Hanifa Shah, Staffordshire University, UK Organisations face a great problem. How can they create a knowledge management (KM) strategy that takes account of the complexity of knowledge issues in their organisation and be able to clearly communicate it? This issue, called here the Knowledge Strategy Dilemma, is the main theme of this chapter and is vital for KM success in practice. The authors argue that literature reveals that the Dilemma is one that can be tackled. They also argue that whilst the literature reveals approaches that help address different parts of the Dilemma, the best approach to address it in a coherent way is a KM method called MaKE. MaKE is presented and two of its principles—Traceability and Transparency— are explained. Also visual tools that help implement these principles in practice are critically discussed along with feedback from industry. The principles, when applied, are helpful in tackling the Dilemma with some success. Also, the authors argue that different forms of communication (including face-to-face meetings with visual aids) should be used to address the Dilemma. The question that remains is: are organisations willing to devote the time to do these things in practice?. Chapter 19 Knowledge Management and Innovation............................................................................................ 300 Lorna Uden, Staffordshire University, UK Marja Naaranoja, Vaasa University of Applied Sciences, Finland In today’s society, innovation and knowledge management are no longer luxury items. Instead, they are necessities and a means of economic development and competitiveness. Knowledge and innovation are inseparable. Knowledge management competencies and capacities are essential to any organisation that aspires to be innovative. Innovation and knowledge management are closely related. This paper discusses the importance of knowledge management in innovation for organisations. It describes how innovations can be achieved through the role of knowledge management using a case study involving the renovation and building of a school project in Finland. The case study shows how knowledge creation and sharing were used to help innovation using vision building. Chapter 20 Holonic Management: Innovation and Creative Entrepreneurship..................................................... 319 Akira Kamoshida, Tokyo Institute of Technology & Nagoya University of Commerce and Business, Japan

The aim of an innovative management is to intentionally create a “chaos edge” and to foster and organize the ideas which are born. Chaos edge is a term usually used in complexity studies, but it is also highly applicable to management. In this paper, the management concept used to create innovation is referred to as “Holonic management.” Holonic management requires the following three elements: 1) cultivating the soil from which innovation shoots can grow, 2) introducing an appropriate competition principle, and 3) preparing a strict evaluation and proper support system. Constructing the field of chaos edge in holonic management can activate an internal environment to create ideas, which result in the internal cooperative work possible to generate innovation. The “Heretic management” finds the innovation shoot created by a minor group within a corporation and allows it to grow without fear of failure. This is not just the most effective tool. It is also the method for the realization of knowledge management. Compilation of References ............................................................................................................... 334 About the Contributors .................................................................................................................... 375 Index.................................................................................................................................................... 384

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Foreword

Knowledge Management as a discipline, despite being 20 years old or so, is still in its infancy. And although there have been many pronouncements of its death, I would argue that it is still very much alive and thriving. What you may find surprising though, is that, if you spent some time in the online KM discussion forums, you would discover extensive, heated debate on the nature of KM. First, people still cannot agree what constitutes knowledge and I don’t believe they ever will. For many, knowledge can only exist in the human mind - anything in written form is information but others argue that knowledge can exist in two forms: implicit knowledge, that exists only in the human mind and explicit knowledge that is recorded, such as in a book or a digital format. These two mindsets are the root cause of much confusion and argument. And even when this difference is recognised, there is still great debate as to “What is knowledge?” In addition, the relationship of data to information and knowledge and even wisdom is hotly argued and the validity of the so called data-information-knowledge-wisdom hierarchical model (D-I-K-W) that is much hallowed in some circles has been called into question. Even the SECI model of Nonaka and Takeuchi—long a staple of KM academics and practitioners—is dismissed by many and has been held responsible by some for the failure of a large number of KM projects. Yet another hot debate is the role of incentives and rewards in motivating people to share their knowledge. Most people still believe that you need rewards and incentives, whilst the works of Alfie Kohn and Dan Pink show that research demonstrates that tangible rewards in the main do not work and, worse, do great harm. As for a precise definition of KM itself, there is even less agreement. You can find hundreds of definitions on the web. They have a lot in common but they are also very different. And the definitions you are drawn to vary depending on whether you are an academic, a KM practitioner, an HR manager, a technologist, or a hard nosed business manager. Definitions are also coloured by the industry you are in. Someone in the oil industry may have a very different view of KM than a software developer at Google. The subject is rich. The subject is broad. The subject is diverse. There is wide disagreement as to the nature of knowledge, what knowledge management is and how you best go about it. But personally, I don’t think it matters too much. I am quite capable of working with several definitions of knowledge inside my head, and I would recommend that anyone working in the KM field define what KM means to them in terms of their specific business and business objectives. This lack of a clear definition and at times ambiguity is what I think makes the field an exciting and fulfilling one to work in, though I doubt every one would agree. What I think is interesting is that KM as a discipline has emerged and is evolving and developing in the age of the World Wide Web. In the past, the ownership, forming, and shaping of a new discipline was

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restricted to a relatively small number academics and high profile early practitioners and evangelists. This is not true today. With the web, regardless of knowledge or experience, anyone can contribute to the debate and the evolution of the discipline. The shaping of KM is a more open, democratic process, and there is much to be learnt about the evolution of knowledge and KM itself in observing the conversations, dialogue, and debate that are taking place world wide. But despite all this argument and debate and a dip in enthusiasm for KM in the mid 90s, I believe that KM today is reviving and thriving. There are many KM societies and networks world-wide. There are a growing number of conferences and an ever increasing number of on-line forums and KM educational courses. And the number of people with "knowledge" in their title and a responsibility for managing knowledge in some way grows daily. It is still a hard fact however that most KM projects have failed or have not lived up to their expectations. I don’t believe that this is inherent in KM tools or methodologies but due more to the fact that KM projects are often poorly conceived and implemented. I think that for KM to be successful, it needs to do three things. 1.

2.

3.

It needs to focus intensely on the critical business issues that need to be addressed within an organisation and not on visionary concepts such as creating a knowledge sharing culture or a knowledge driven organization. Such concepts deflect us from the real issue of solving business problems, mitigating business risks and identifying and exploiting new business opportunities and are too often a one way street to frustration and ultimate failure. It should place more emphasis on working with and obtaining buy-in from senior managers in the organization, not only by developing a business case but recognizing that managers are human and can be swayed by other motivations other than a traditional ROI analysis. It needs to obtain the buy-in from people in the organization by working with them, engaging and involving them much earlier in the project life cycle then most traditionally managed projects. Unlike other systems, people cannot be coerced into using a "KM system” —they need to have ownership.

I am often asked “How do you do KM?” My response is that “You don't do KM! We should respond to business problems and develop business opportunities using KM tools.” I also don’t believe there should such things as KM initiatives. Again, we should not “do KM”. There is no such thing as a KM strategy. There are only business problems, business challenges and opportunities, business strategies, and business projects. To my mind, the problem with KM initiatives and strategies is that they conceptualize problems and make it far too easy for us to take our eye off the business, and this is one of the key reasons why so many KM projects fail. It is also rare that a business issue is purely a KM one. We usually need more than just KM tools and techniques to fully address a business problem or opportunity. We should use KM tools and methodologies to help respond to business problems and opportunities. If we must have a KM strategy it should be in response to clear business goals and tie in to the top level business objectives of the organization or our organizational unit. The business purpose and outcomes should come first! I also believe that there are no benefits to KM as such. As KM is about improved communication, learning and knowledge sharing it can be applied to any human endeavor. So asking what are the benefits

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of KM is a meaningless question as the answer is “what ever we want them to be!” We need to start by asking “What do we want to achieve in terms of business outcomes and how can KM thinking, KM tools and KM techniques help?” Not everyone will agree with my views. And that’s fine. That’s the nature of KM. What I like about this book is that it includes contributions from academics, researchers, managers and practitioners in a wide variety of areas relating to KM and innovation. Much of what they have to say is in disagreement with each other or represents alternative view points. This is good. This is how we take the discipline forward. So let me finish by saying something I say again and again about KM. There are no recipes for KM. There are no prescriptions for KM. There is no substitute for thinking for yourself about KM. Read the book, reflect, think hard and join the conversation—both the dialogue and the debate! Help shape KM for the 21st century. David Gurteen Gurteen Knowledge, UK David Gurteen has over 30 years’ experience working in high technology industries. He was a professional software development manager and in the late 80s worked for Lotus Development, ensuring that Lotus products were designed for the global marketplace. Today he works as an independent knowledge management advisor, facilitator and speaker, helping people to innovate and to work together more effectively. He is the founder of the Gurteen Knowledge Community—a global learning network of over 17,000 people in 160 countries. He publishes a monthly Knowledge Letter, now in its 11th year, and the Gurteen Knowledge Website—a resource website that contains book reviews, articles, people profiles, event calendars, inspirational quotations, an integral weblog and more on subjects that include knowledge management, learning, creativity and innovation. He is known for his Gurteen Knowledge Cafés and the Knowledge Café Masterclasses that he runs regularly in London and in other cities around the world. In June 2010, He won the Ark Group’s lifetime achievement award for services to KM.

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Preface

INTRODUCTION The purpose of this book is to collect and to present the results of theoretical and empirical research into various aspects of Knowledge Management (KM), innovation and, especially, the conjunction of KM and innovation. To this end, chapters are included from both academics and practitioners, and the contributions represent diverse views and versions of KM and innovation. It is this richness and diversity that makes the topics so fascinating and, it can be contended, so important to the future work of academics and practitioners in a variety of fields alike. It is possible to conclude that both topics—KM and innovation—are different aspects of the same phenomenon, which is the use of “human capital” to generate new ideas and to promote creativity. It is axiomatic that advances and innovations in science, technology, industry, education, and the arts all begin at one source: the knowledge of the people on whom businesses and organizations of all sorts rely for their new products, services, technologies, and systems. The potential audience for this book is therefore very broad. The term “Knowledge Management” (KM) is most frequently used to describe the range of practices and activities that are used in a variety of organizations to identify, create, represent, store, disseminate, and encourage the adoption of relevant human insights and experiences. Such insights and experiences (i.e. the knowledge) may be embodied in individuals’ minds or embedded in a group’s or an organization’s artifacts, processes, and practices. KM research has for some time embraced the fields of business administration and management, information systems and technology, learning and psychology, and library and information sciences (Alavi & Leidner, 1999). More recently, other contributions to KM research have included applications of KM in public health and public policy. Most large commercial companies and many non-profit organizations have dedicated considerable efforts and resources to KM, often as a part of their business strategies, IT strategies, or even human resource management (HRM) strategies (Addicott et al. 2006). Consulting companies have grown up to provide advice and expertise on KM to these organizations at strategic and operational levels. KM efforts to date have typically focused on organizational objectives such as improved performance, competitive advantage, innovation, and quality improvement. KM initiatives often overlap with existing directions with organizations such as organizational learning (OL), Total Quality Management (TQM), and even Business Process Re-engineering (BPR). KM may be distinguished from these disciplines (and others) by a greater focus on managing knowledge as a strategic asset and on enabling the sharing of knowledge. KM efforts can therefore help individual workers and groups to share valuable organizational learnings, to reduce redundant work (e.g. avoiding reinventing the wheel), to reduce training time for new employees, to retain intellectual capital when employees leave and to adapt continually to changing environments (Thompson & Walsham, 2004).

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There are currently a number of schools of thought or movements that have had an influence on Knowledge Management and its relationship to innovation, the following being among the more important: •

•

•

•

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The information theory movement, usually associated with the frequently-cited work (Chun, 2003) by Davenport and Prusak (1998), Nonaka (1991), and later with Takeuchi (1995). This work has been an important influence on KM and includes later developments by Probst (1998), Von Krogh (1998), and Malhotra (2000) which cite the process of KM as an important enabler of innovation; The collaboration school of KM that focuses on CoPs and collaborative ICTs, originating from research at Lotus (and later IBM) by Wenger, McDermott, and Snyder (2002), that views knowledge as a commodity that relates directly to interpersonal contact and the communication of new ideas; The intellectual capital movement with Edvinsson and Malone (1997), Sveiby (1997), and later developed by Bontis (2002), which views knowledge as valuable corporate resources and assets that can provide competitive advantage through innovation; The social network analysis school, typified by Krebs (2008) and influenced by the work of Borgatti with Cross (2003) and with Carboni (2007), that concerns the theoretical mapping and measuring of the relationships and flows of information between people, groups, and organizations, which can be an important source of invention and innovation; Narrative approaches associated with Snowden (2000a, 2000b), Boisot and Canals (2004), Spender (2007), and others. Variations of this approach are described by Snowden (2004), Boje (2001), and others as a form of KM.

INNOVATION AND INVENTION Innovation is often equated with creativity and is generally understood to be the successful introduction of a new artefact, method, or process. The ideas behind the development can come from a deliberate process of deductive development from a knowledge base or from an intuitive and even accidental ‘bright idea’ in which it may be difficult to identify the role of knowledge. However, there generally appears to be a fundamental link between knowledge and innovation. Luecke and Katz (2003) believe that “... innovation is the embodiment, combination, or synthesis of knowledge in original, relevant, valued new products, processes, or services”. Amabile, Conti, Coon, Lazenby, and Herron (1996) suggest that innovation is related to creativity (as all innovation begins with creative ideas) but is not necessarily identical to it. Action is required to develop the creative ideas to make some tangible and lasting contribution, and it is this contribution that is defined as innovation. We define innovation as the successful implementation of creative ideas within an organization (Amabile et al., 1996). In this view, creativity (whether by individuals and teams) is a suitable starting point for innovation, but creativity may not always lead to successful innovation. For innovation to occur, then, something more than the creative generation of an idea or insight is required, as the insight must be enacted to make a real difference. Innovation is also described as a part of a management process that may need to draw on other organizational resources that creativity does not. Davila, Epstein, and Shelton (2006) believe that “Innovation, like many business functions, is a management process that requires specific tools, rules, and discipline”. This appears to place emphasis on the general organizational processes and procedures for generating, evaluating, developing, and acting upon creative insights to produce significant organizational improvements. Creativity may therefore be seen as the basis for innovation and innovation as the successful implementation of creative ideas (Amabile et al., 1996). Davila et al. (2006) appear to confirm that it is

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this ‘bringing ideas to life’ in an organizational context that makes innovation the distinct undertaking it is. Many of the contributions to this book contend that this undertaking or organizational process requires knowledge as an input to the innovation process, uses knowledge to make the process efficient and also produces knowledge as an outcome of the process in the form of experience and organizational learning. The various contributions to this book tend to confirm this view in a variety of organizational contexts. Fagerberg (2004) notes that “An important distinction is normally made between invention and innovation. Invention is the first occurrence of an idea for a new product or process, while innovation is the first attempt to carry it out into practice”. Innovation, in contrast, occurs when an invention or an idea is used to change how society functions, how organizations are structured, or how people live their lives. Innovation is also distinct from improvement in that it permeates society and can cause reorganization, and from problem-solving in that it should have more lasting and far-reaching effects. The term ‘innovation’ therefore refers to a new way of doing something or achieving new ends, perhaps by radical and revolutionary or by more gradual and incremental means. This may be through changes in modes of thinking, through new products and/or processes, or the way in which organizations are structured and managed. A distinction is sometimes made between inventions, which are ideas put into practice, and innovations, which are inventions applied successfully over time (McKeown, 2008). On these terms an invention may be completely new (e.g. based on a “step-change” in thinking or technology) and yet not be an innovation if it does not have lasting influence (e.g. start a trend) or contribute substantially to a body of knowledge. In many fields, an invention must be substantially different from its predecessors to be classed as innovative and must have a measureable and valuable effect. In business the innovation may have to increase the value of a product or service in the eyes of the consumer or producer. The goal of invention is therefore to effect a positive change—to make something better—and innovation leading to increased productivity is the fundamental source of increasing economic wealth. Although innovation is considered to be the output of the process of invention, in a KM/innovation context it is usual to focus on the whole process, from the origination of an idea, through its transformation into something useful, to its implementation and subsequent effect on the environment. While innovations typically add value or produce benefits, they can have disruptive or destructive effects as innovations replace outdated or obsolete products, organizational forms, and social practices. It is therefore possible that organizations that fail to innovate effectively may be superseded by those that do. Conversely, as invention and innovation may harm the organization that bears the cost and efforts of the process, innovation projects may involve a degree of risk. A key challenge in innovation projects is to achieving a balance between process and product innovations—where process innovations tend to involve a business model which may develop shareholder satisfaction through improved efficiencies, while product innovations develop customer support however at the risk of costly research and development (R&D) that can erode profitability and therefore shareholder return. Many of the contributions to this book, by definition, describe current applications of KM and innovation, but which are founded on established KM models, processes and technologies that have developed over time. It may therefore be useful to describe the generations through which the subject of KM has progressed in reaching its present state.

‘GENERATIONS’ OF KM AND INNOVATION The original generation of thought on KM and innovation focused on the mental process of understanding, problem-solving, and generating new ideas. This may therefore be termed the ‘Psychological’ generation

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of KM. Early work on creative mental processes by Michael Polanyi challenged the previous “scientific” view that innovation (at least in science and technology) was a completely logical and deductive process—a cold, logical progression from one stage of the development of a new idea to another. In his seminal work “Personal Knowledge: Towards a Post Critical Philosophy” Polayni (1967) neatly summarizes the thesis of his later life as a philosopher (he was originally a chemist) that creative or innovative acts often arise from “unscientific” stimuli such as guesses, hunches, and emotional commitments. This view challenged the commonly accepted position that science is value-free, and it is possible to see creative tension between Polanyi’s approach to knowledge and enquiry and that of Thomas Kuhn, who later held that typical scientists (and, by extension, technologists) are not independent and creative thinkers. On the contrary, they accept and react to what they have been taught (especially in the form of accepted theories) and apply their knowledge to solving the problems that such theories suggest. In this view scientific people are problem-solvers who aim to prove (or disprove) what they already know in advance: “The man who is striving to solve a problem defined by existing knowledge and technique is not just looking around. He knows what he wants to achieve, and he designs his instruments and directs his thoughts accordingly” (Kuhn, 1970). It is perhaps characteristic of the richness of the phenomenon—the conjunction of KM and innovation—that these original points are not as mutually exclusive as they may at first seem. Kuhn’s view recognises the inevitability of scientific revolution—“the tradition-shattering complements to the tradition-bound activity of normal science” (Kuhn, 1970) —through “paradigm shifts”, which occur when the developed solutions to existing problems show the theories to be incorrect or inadequate to explain the facts. Polanyi saw creativity as a more individual or internal activity, but recognised that the tacit knowledge of many individuals can be collected and combined to form a new model or theory (Polanyi, 1967). Some authorities therefore call this the “first generation” of KM. A significant development to the discipline of KM occurred in the early 1990s (e.g. Nonaka, 1991) which developed some aspects of the work of Polanyi into a conversion process or cycle that, it was claimed, could be used to change tacit knowledge (i.e., peoples’ creative ideas and thoughts) into explicit knowledge, based on more readily accessible and usable organizational artefacts such as repositories and knowledge bases. This approach is typified by work by Probst which, building on systems theory, follows a process that starts with knowledge identification, followed by knowledge acquisition, knowledge development, knowledge distribution, usage of knowledge and knowledge retention in codified form (Probst et al. 1997). Such models were well accepted by software developers, who were quick to develop products such as knowledge bases and document management systems to exploit the growing interest. This has been termed second generation KM (Schütt, 2003). In discussing such models based on system thinking, Stacey (2001) comments, “This reflects an underlying way of thinking in which knowledge is reified, treated like a ‘thing’ that can be possessed, that corporations can own”. In his view, “knowledge itself cannot be stored, nor can intellectual capital be measured and certainly neither of them can be managed” (Stacey, 2001). His comments appear to apply to knowledge when it is regarded as a single commodity, rather like information. Subsequent approaches to KM seek to identify and explore the components that make up the phenomenon of knowledge and examine their role in putting knowledge into action in organizations. The second generation of KM developed from the first through a movement that stressed the uses of knowledge in organizations, going beyond the mental processes of the individual knowledge worker and recognising the complexity of modern, knowledge-creating organizations. This may therefore be termed the organizational generation of KM. Nonaka and Takeuchi (1995) held that companies are more like living organisms than machines, and most of them viewed knowledge as a static input to the

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corporate machine. Later work by Nonaka and Takeuchi (1995) appears to integrate Nonaka’s cyclic approach (known as SECI) with systems theory to provide a more systematised, a view that implied that knowledge could be moved from one form to another in a process rather like information systems design and development. Nonaka and Takeuchi therefore view knowledge as a renewable and changing resource and hold that knowledge workers are the agents for effecting that change. Knowledge-creating companies, they believe, should focus their KM activities primarily on the task of innovation rather than knowledge hoarding. Schütt observes that “Knowledge, in this sense, is not so much a thing or a higher (quality) level of information, but more a kind of capability to put data into context” (Schütt, 2003). Snowden separates knowledge into five components that are capable of being analysed and assessed, the first letters of which form the mnemonic ASHEN (Snowden 2000), which is intended for use in optimising knowledge management. The framework is based on artefacts, or useful information stored in the form of things that are documented (i.e., explicit knowledge assets in Nonaka’s view); skills or the ability of individuals and groups to acquire, manipulate and put to use high-level information in knowledge contexts; heuristics or the rules of thumb used by experts to manipulate information to make decisions in complex situations; experience or the cumulative ability of individuals and groups to apply skills and employ heuristics and to identify and develop artefacts to achieve beneficial results based on the use of knowledge in practice; natural talent or the innate ability of certain individuals (the most expert knowledge workers) to manipulate knowledge and to achieve desired results from its use. This raises the issue of knowing not just what knowledge is codified or documented in an organization, but what knowledge should and could be codified or documented. It may be contended that second generation KM practices promoted the collection of knowledge resources without a clear need, the later approach implies that the organization needs to have a clear judgement of what knowledge is likely to be needed. An analogy can be drawn with production supply chain practices; to be cost effective, some knowledge provision has to follow just–in-time rules (acquisition upon need), and other provision has to be just-in-case (acquisition in advance of need). In this view explicit (i.e., existing, documented) knowledge resources follow usual organizational information management practices and might end up in a document or content management system on the organization’s intranet. The innovation in KM that is a part of this approach is the way the things are handled that cannot be codified. These tacit resources are linked to single experts or sometimes groups of experts, who interact with the KM system(s) in a number of ways. Communities of Practice (CoP) combine groups and individuals who have an interest to share knowledge about a relevant area of expertise (usually on a voluntary basis). Usually a CoP maintains a best practice database or a log of lessons learned. Physical conferences or virtual meetings and the use of web forums and blogs help in harvesting innovative ideas and sharing knowledge. Such technologies are not, of course, ephemeral, as they may continue to exist and find use as knowledge artefacts. Debriefing sessions or after action reviews (originating in a military context) identify and preserve the learnings from significant experiences (e.g., projects and innovations) and help to spread knowledge-based skills and develop heuristics and rules of thumb for later use by others. Teaching sessions by key knowledge owners can have a similar effect. Storytelling or narratives are used as an alternative (and perhaps more natural) way to identify and discuss critical knowledge and to disseminate it. These techniques (and others) can be used to prepare and promote interventions leading to cultural change (Snowden, 2001). This marks a significant departure from many of the precepts of the previous generation of KM and leads to an explicit connection between KM and organizational learning (OL). Senge (1990) originally identified OL as a sustainable source of competitive advantage in business, discussing holistic systems thinking as a fifth discipline of business. This can enable an organization to develop a superior capacity to learn, to retain, and to benefit from that learning through new ideas and new products and services that

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competitors find difficult to imitate. By definition, this process is sustainable and can provide a renewable source of competitive advantage, placing the links between KM, OL, innovation, and competitive advantage at the very heart this generation of KM. Second generation KM may therefore be defined as a management discipline that focuses on organizational learning to encourage business innovation as a renewable source competitive advantage. Third generation KM followed on naturally from this set of precepts and seeks to appreciate and understand what the effects of the process of managing knowledge can have on an organization and its workers. “In a complex domain we manage to recognise, disrupt, reinforce and seed the emergence of patterns; we allow the interaction of identities to create coherence and meaning” (Snowden, 2002). Third generation KM frameworks such as Snowden’s Cynefin model tend to recognise the conjoint complexity of organizations and knowledge, seeking to make sense of the relationship between them in complicated and complex work processes and organizational structures. In Snowden’s view, the third generation of KM will require the “clear separation of context, narrative and content management” and seeks to challenge the orthodoxy of scientific management on which the second generation of KM was based. Therefore, in the third generation of KM, complex adaptive systems theory is used to create a model that seeks to utilise the self-organising capabilities of informal communities and identifies a natural flow model of knowledge creation, disruption, and utilisation. This implies a new and important role for the knowledge worker using new generation KM technologies and informal networking communities to acquire knowledge and promote innovation. To some extent, this marks an evolution of second generation KM rather than a revolution (some authors therefore do not recognise the existence of the later generation). Others term the third generation of KM the ecological’ generation, as it involves the interaction of knowledge workers, knowledge assets, and organizational and environmental factors interacting together as a complex adaptive system.

KM, INNOVATION AND TECHNOLOGY Tapscott (2006) sees a clear link between the role of the knowledge worker and innovation in interacting with peers and with organizational KM resources, but believes that the nature of the interaction has become more advanced. He describes social media tools on the World Wide Web that can initiate and enable more powerful forms of collaboration than were possible in the second generation of KM. He points out that knowledge workers routinely engage in peer-to-peer knowledge sharing across organizational boundaries, forming networks of expertise in a more complex way than would be possible with traditional CoPs. They do this by using a variety of Web 2.0 social networking technology applied in an organizational context, calling them Enterprise 2.0 technologies. The key technologies in Enterprise 2.0 are wikis, social networking systems, blogs, search engines, mashups, portals, Web/videoconferences, bulletin boards, and discussion forums. Using this technology, enterprise social networking enables corporate knowledge workers to use a network that allows exchanges that can lead to new knowledge creation. Enterprise 2.0 therefore appears to provide a collaborative platform that enables organizational and trans-organizational knowledge to be exchanged and consolidated. The connectivity of wikis, portals and forums makes the exchange of knowledge possible, and the use of search engines and content management systems makes the acquisition of knowledge (from inside and outside of the organization) more easily possible. Enterprise 2.0 therefore has the power to change the way an organization collects new knowledge and how it stores it, changing the way organizations approach Knowledge Management in

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the third generation of KM. When compared to previous models of industrial competition (e.g. Porter 1985) that assume that organizations will seek to gain advantage from knowledge that competitors do not have and cannot access (except, of course, for strategic partnerships that are negotiated and conducted at a high level in the organization), there are concerns over copyright and intellectual property law being challenged in practice, inhibiting organizations’ KM practices. In fact, the approach may not be contrary to conventional models of industry competition, as Porter maintains that companies will change the basis of competition, seeking to gain whatever advantage they can (temporarily or permanently) from their knowledge relationships. In this view, businesses must collaborate in order to survive. There are instances of alliances of public (i.e. government) and private (i.e. commercial) concerns collaborating to develop innovations with no discernable competitive commercial outcome for any party, with the open-source Linux operating system and the Human Genome Project as oft-cited examples. These may be seen as examples of innovation for the long term public good although, needless to say, there are always likely to be opportunities for the commercial exploitation of developments arising from such fundamental innovations in technology and science. Denning (2000) envisions two basic different approaches or mindsets relating to KM and innovation. The first of these he calls the Napoleonic or engineering approach (Dyson 1998), which refers to the application of scientific discovery to practical invention. It assumes the existence of a controllable path—a process based on a series of linked tasks—from the generation of the idea to its exploitation. In Denning’s terms, the approach represents an effort to reduce all knowledge to a set of mechanistic propositions which he attributes to “…a continuing itch for reductionist simplicity” (Denning 2000), which he feels can lead to attempts to micro-manage, to over-control, and to rely on unwarranted hierarchical organizations, procedures, and rules that may ultimately stifle the processes of creativity and innovation in a rapidly-changing environment. The opposite of this is what Denning sees as the Tolstoyan or ecological approach (Dyson, 1998), which is based on the creative chaos and freedom on which creativity apparently thrives and which seeks to exploit the connections between things and people that are features of the collaboration and social networking schools of KM. Both Denning (2000) and Dyson (1998) maintain that humans naturally grasp the natural connectedness of things and are able to exploit these connections in new and innovative ways without formal rules or controls. This process is said to be more rapid and more reliable than rigid mechanistic processes of management, which tend to rely on analytical (rather then creative) thinking. As organizational initiatives such as business process re-engineering and redesign appear to conform to this paradigm, it may be difficult for organizations to design their way out of this problem without a fundamental paradigm shift.

2009 - EUROPEAN YEAR OF CREATIVITY AND INNOVATION The European Commission decided that Europe needed to boost its capacity for creativity and innovation for both social and economic reasons, so it declared 2009 as the European Year of Creativity and Innovation (EYCI 2009). In doing so, it recognised the need for better use of knowledge and more rapid innovation, and acknowledged that a new emphasis was needed to broaden the creative skills of whole populations. The initiative instils a need for managers and national leaders to embrace this change of paradigm as an opportunity and to prepare the way for a more culturally diverse, innovative society based on the creative use of knowledge. The initiative therefore aims to raise public awareness of the importance of creativity and innovation for generating economic development, in order to contribute to economic prosperity as well as to improve social and individual well-being. The activities of EYCI

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2009 are aimed at a range of different social groups including educators, public and private sector policy makers and the general public in a process that is called the knowledge triangle.

ORGANIZATION OF THE BOOK The contributors to this book include academics, researchers, managers and practitioners in a variety of areas relating to KM and innovation. They come from the USA and a number of countries in Europe and Asia, giving a global coverage and interest to the book. Their contributions represent their individual views on a number of different topics and, in some cases, different views of the same topic. This variety of interest and experience produces a diverse and culturally rich view of the phenomena that make the book a relevant and timely publication in the areas of KM and innovation. This very diversity, however, makes it important to organize the book in a way that makes sense of the diversity and provides a logical link between the various contributions. Section 1 of the book therefore focuses on the phenomenon of the learning organization and the role of KM in the OL process and in the first chapter, Bratianu examines, in a Romanian context, the role of universities as knowledge-intensive learning organizations, observing that such bodies are knowledgeintensive, since the density of knowledge and the dynamics of knowledge processing are more important than other types of organizations. He notes the common assumption that since learning is one of the major processes within universities, they are by definition learning organizations. Bratianu’s point is that this assumption may be an error, and although a particular university may meet the accepted definition of knowledge intensive based on learning processes, it is not necessarily a learning organization. The chapter performs a functional analysis of the specific knowledge processes before identifying the necessary conditions for a generic university to become a learning organization. This is a useful prerequisite to successful innovation in universities, academic institutes, or indeed any knowledge-intensive organizations. From South Africa, Smut, van der Merwe and Loock examine KM through an innovative learning solution, linking the growth and earnings of knowledge-intense companies to their increasing efforts to extract wealth from individual and organizational know-how as a replacement for plant, machinery or other conventional factors of production, which the authors (following Becker) no longer see as providing strong differentiation. In this view, the shift to a knowledge-oriented economy has favoured organizations that can turn knowledge into new products and services faster than their competitors can (Kanter, 1997). Also, an organization’s (and, by extension, a nation’s) knowledge is seen as a primary source the source of its wealth. In this chapter the authors quote Porter, who regards the application of knowledge to those tasks, products, and services that exist as productivity and to those that do not yet exist as innovation. They observe that of all commodities, only knowledge can achieve these two apparently conflicting objectives. This chapter provides an overview of the application of KM in a mobile telecommunications company operating in 21 countries in Africa and the Middle East. The concept of a corporate university is described, with specific reference to an innovative technology solution for one of the learning components of this enterprise. Berger and Beynon-Davies examine knowledge-based diffusion in practice through a case study. The authors encourage the reader to consider how the use of particular development methods shapes information systems (IS) practice and how organizations adapt and use knowledge in support of such projects. They conceptualize an IS development method as a de-contextualized knowledge bundle, in

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which relevant knowledge (e.g. experience of similar projects, or of the use of certain IS development methods) is diffused between organizations and infused within organizations through the processes of contextualization. The authors particularly consider the way in which the structure and culture of organizations affects the processes of diffusion and infusion. Their discussion is grounded in case-study material collected from an ethnographic study of a large-scale IS development project. In this project, an agile IS development method promoted by external vendors experienced initial problems in organizational deploymentdue to its poor acceptance in that particular context. Gradually, benefiting from developing experience and therefore increasing knowledge, the IS development method was adapted to its context and was finally used successfully by the project team. In this chapter the authors identify and consider the factors that allowed this transformation to occur, focussing on the role of knowledge in the process of IS innovation. Chang and Li examine the factors that are important in fostering the deployment of KM in research and development (R&D) workspaces in Taiwan. Their discussion begins with a discussion of whether knowledge management (KM) is a new idea or just a recycled concept, and they comment on the number of claimed solutions to a variety of problems in the form of KM strategies, frameworks, processes, barriers and enablers, IT tools and measurements. The chapter points out that, although many KM studies exist in both public and private sectors, most of these studies focus on western experiences and relatively few cases are reported on KM deployment and implementation in the Asia-Pacific region. They especially make a case for studying knowledge-intensive R&D institutes whose missions are to serve traditional industries. To discuss some of the successes of KM deployment in this region, this chapter presents and discusses the lessons learned from a case study in fostering a KM initiative and system development in an R&D institute serving the metal industry, and it recommends a five-stage approach to KM deployment that may find applications in similar environments in other parts of the world. Tung-Xiung indicates that there may be problems with continued innovation and the use of new technology through a series of comparative longitudinal case studies recorded over more then two decades in China. The research on which the chapter is based has monitored the evolution of technological innovation, its effect on human factors, and discusses some of the resulting syndromes, such as Computer Fear Syndrome (CFS) and User Alienation Syndrome (UAS). The research involves an interesting analysis of the empirical data derived from these case studies and concludes with a proposed Funnel Model that suggests appropriate courses of management action and puts forward new ideas for developing KM systems in a variety of organizations that may prevent such syndromes from occurring or alleviate their effects in typical organizations. Orange and Ah-lian Kor present theoretical work that seeks to integrate various epistemologies from the philosophical, KM, cognitive science, and educational perspectives. Surveying the knowledge-related literature, the authors collate a number of apparently diverse views of knowledge and then categorise and ascribe attributes to the different types of knowledge in order to make them more useful in practice. They develop a new Organizational Information and Knowledge Management Model which seeks to clarify the distinctions between information and knowledge by introducing a novel conversion framework accompanied by a proposal for mechanisms that will improve individual knowledge creation and information sharing that will benefit Communities of Practice and organizations. Finally, Shue addresses the issue of extracting knowledge from financial statements, which could become important sources for investigating achievements on the primary business activities of planning, financing, investing, and operating in a variety of corporate types. He maintains that better knowledge of planning activities could assist managers in focusing their efforts and identifying business opportunities and obstacles to strategy. Accurate knowledge of financial activities may assist a company in acquiring

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and managing its financial resources. Knowledge of investment opportunities including land, buildings, equipment, legal rights, inventories, and all components necessary for the competitive operation of a company, while operational knowledge represents the execution of the business plan requires knowledge from a variety of sources. As a result, according to Shue, financial statements provide the main source of information for all parties who are interested in the performances of a company, including its managers, creditors, equity investors, and others. Although each of these parties may have different perspective in viewing financial statements, one major concern to all parties is the financial quality of an enterprise, which requires knowledge that the author says can be derived from a careful analysis of financial statements. Section 2 of the book moves on to examine the role of KM in the process of envisioning, inventing, and developing new products and explores the role of innovation in that process. From the same institution in Italy, De Maggio and Margherita carry out a critical analysis of “knowledge democracy” as the new mantra or buzz-word in product innovation leadership. The chapter begins by discussing this phenomenon, which identifies participation in KM, co-operation, and the co-creation of innovative value as the new paradigm of business. The authors show how this new paradigm has revolutionized the traditional process of invention, which was previously associated with a hierarchical dissemination of new ideas and competitive hoarding of knowledge assets. They contend that this competitive environment has been replaced by a collaboration economy (a.k.a. wikinomics) in which democracy governs the process of knowledge creation and its strategic application. The chapter introduces terms such as “peering” (i.e., eliminating management hierarchies in favor of meritocracies based on the quality of ideas and inventions), “openness” (i.e., participation and involvement of any stakeholder who has something to contribute), “free sharing” (e.g., sharing of intellectual property to facilitate participation and cross-fertilization), and “total action” (i.e., the increasing use of virtual and distributed resources). The authors show that leadership in product innovation does not rely on the innate internal qualities of organizations, but on the collaborative contribution of stakeholders, including customers, partners and co-developers, to many of the activities that make up the NPD lifecycle. This new approach can enable advantageous product customization, improving a number of the factors that can reduce the value of the NPD process (e.g., time to market, cost reduction, investment risk). The chapter examines the factors that are needed to promote such open collaboration, from the development of a new managerial mindset, the acquisition of new distinctive competences, the development of new organizational models, and the management of new collaborative technologies. Within this setting, the adoption of knowledge democracy often implies the introduction of new interactive NPD processes, probably in a period of turbulent change, which can present organizations with significant change management problems. This creates a need to identify new models of process integration and to develop new organizational forms that are able to exploit the potential of knowledge democracy in NPD. The authors’ proposed framework of processes and competencies offers the potential for organizations to meet these needs. Magnier-Watanabe and Senoo explore how KM, as an enabler of change, using its capability to create knowledge is subject to internal and external forces that shape the KM processes and the ways that knowledge is used. The chapter is a qualitative analysis based on a case study of the first major roll-out of Smartcard technology in France. The resulting analysis shows how institutional pressures affected not only the KM process but also the resulting innovation. The external factors that can impinge on the process include government initiatives, legal authorities, and cultural expectations. While this example is from French society, but it may be implied that such coercive pressures on the credit card industry are global in nature. In the case study of the introduction of a credit card (viewed as the development of an

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innovative financial product), existing credit card systems and solutions acted as what the authors call “mimetic pressures” (i.e., they promoted copying from existing successes) and professional networks and network externalities acted as normative pressures (i.e., they tended to encourage the formation or adoption of norms or standards). The authors suggest that by acknowledging these institutional pressures and viewing these forces from a systems perspective, organizations can achieve improved strategic alignment and can provide a sounder basis for differentiation in their market places and sustaining competitive advantage. Section 3 of the book focuses on common application areas of KM and considerable arenas for innovation—healthcare and education. Contributions to his section of the book are less numerous than some of the other application areas (e.g. NPD or OL), but are nevertheless important due to the innovative nature of the sector and the public service that can be offered by innovations in this area. Baskaran, Naguib, Guergachi, Bali, and Arochena, in a joint Canadian/UK study, observe that contemporary healthcare organizations, like organizations in business sectors, are constantly under pressure to develop new strategies for delivering better services and that KM has been successfully applied in a business environment. However, they argue that failure to apply proper KM principles has reduced the confidence of new adopters of KM. This chapter suggests how KM can be appropriately applied within an innovative healthcare project and offers a case study which describes attempts to improve attendance at breast screening clinics. The case study discusses the need for a balance between the technological and human aspects of KM, and assesses the success of the use of KM in this application. A survey was conducted of doctors and clinical staff that appears to provide proof that a balanced approach will definitely increase the success of such initiatives. The outcomes of this project can increase the confidence of future KM adopters in healthcare generally, provides useful guidelines for conducting balanced KM initiatives and highlights the importance of taking a focused approach KM development, allowing innovative uses of KM in healthcare. Wickramasinghe also pursues the theme of KM innovations in healthcare, but from an Australian perspective. He identifies the contribution of ICT systems to the increasing amounts of data and information that many organizations have to manage. He points out that although these systems and technologies were implemented to enable superior decision-making, the result has often been a state of information overload, referred to as the productivity paradox. He goes on to discuss KM as a way of making sense of this chaos by applying strategies and techniques to apparently unrelated (and perhaps irrelevant) data and information in order to extract the necessary knowledge to aid decision making. He contends that it is the configuration of these technologies that is important to support the techniques of KM, discusses the process-oriented knowledge generation framework of Boyd, and recommends ways in which the role of technology can enable the design of a network-centric healthcare operation that is an innovative and important contribution to healthcare applications of KM. Eardley and Uden from the UK examine the innovative convergence of two emerging social and technological trends: the evolution of educational processes and methods from a traditional didactic approach towards a new learner-based paradigm and the development of software systems that enable the democratic involvement of learners and computer-mediated collaboration between individuals and groups with a common interest in learning. Originally the main purpose of such Web 2.0 software was social networking and leisure, but the chapter identifies a number of instances of its use in practice for professional education purposes. The chapter then highlights some examples of professional learning communities in practice in UK educational institutions and concludes by discussing possible future trends in the use of social software for supporting professional learning communities.

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Again, in the next chapter, Uden and Eardley examine a different aspect of education: Problembased Learning (PBL). This chapter begins with a brief review of knowledge sharing, followed by the importance of knowledge sharing for learning, especially in problem-based learning. The authors draw on material demonstrating that knowledge is often the most important resource and asset of many organizations. They show that the exchange of knowledge and KM often enhances OL, which in turn leads to the potential for innovation in those organizations. The authors contend that knowledge sharing is central to the concept of knowledge management and that the future of knowledge sharing is not technical, but social. They then demonstrate how successful knowledge sharing can be achieved for students in a PBL environment. The chapter concludes by identifying the most important implications for effective knowledge sharing in PBL. Vasilache, Dan, and Dima discuss the issue of innovation in the Romanian healthcare sector from the point of view of organizational learning (OL). They contend that the extent to which OL can promote innovation is influenced by the dominant organizational culture. Adopting the premise that hospital organizational culture in hospitals exhibits singular features when compared to other organizations, the authors of this chapter surveyed medical and nursing staff from a clinic within a large teaching and research hospital. A questionnaire developed by the authors was used to study the perceptions of the two sub-cultures (physicians and nurses) regarding the relationship between organizational culture and innovation. The results of their study confirm the differences in perception between physicians and nurses previously found in the literature and identify the factors which promote OL and innovation in a typical clinical environment, comparing these to the factors that correlate with them negatively. The recognition of cultural factors in this context and the resulting recommendations may be transferable to hospitals in other cultures. Section 4 of the book includes some interesting submissions on the role of KM in the process of innovation and some examinations of how invention and innovation may be achieved by the use of knowledge. A similar theme to that addressed by Vasilache et al., but from a resource-based perspective, is pursued by Smith and Coakes, who discuss the instability of the business environment and observe that organizations must constantly strive to match or exceed the rate of this change in order to maintain or improve their competitive position. This change, they observe, must be managed effectively and consistently and factors such as organizational competence must be in place to allow the maintenance and development of innovative processes and products that meet business needs. To address the opportunities and threats presented by such complex and unpredictable environments, the authors show that organizations must combine and recombine their resources in novel and innovative ways, reconfiguring or eliminating obsolete resources and acquiring appropriate new resources. They show that innovation accompanied by repeated and rapid resource manipulation can achieve competitive advantages that are not easily imitated by rivals. This capability may be critical to an organization’s business performance, as it enables the development of new products and services that enable it to improve its competitive position. The authors explore the numerous theories of change in the literature, but observe that competitive advantage is increasingly located by authorities in an organization’s intellectual resources and its human capital. This includes the capability to innovate by mixing human skills with knowledge. In their view innovation is “characterized by an iterative process of people working together, sharing insights, and building on the creative ideas of one another”. This chapter emphasizes the role that an organization’s intellectual resources have in enabling superior innovation and change capabilities. Whereas Chang and Li recommend a process, Smith and Coakes conclude that an organization’s ability to use resources to foster close social interaction and open knowledge sharing in the workplace and to leverage its informal

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leadership is a vital part of a related KM initiative. It is possible that both views are valid and that their conclusions and recommendations can complement one another. Gaál, Szabó, Obermayer-Kovács, Kovács, and Csepregi offer an innovative approach to KM by suggesting a framework, based on KM profiling, for mapping KM practice in organizations. This approach involves an objective assessment process that can be used by knowledge-intensive organizations for gauging their current position with regard to KM activities and processes. The chapter shows that uncertainty can present a barrier to the introduction of suitable activities for improving KM processes in most organizations. The authors believe that the research can be offer significant practical advantages and will provide substantial support for leaders and managers in establishing KM. Moreover, the right KM activities can help to stimulate creative thinking and provide a spur to inventions and innovations. The authors show that to ensure success and the long-term benefits from effective applications of OL and KM practice is of critical importance to many organizations. Besides simply evaluating the benefits that are inherent in KM, the work indicates that organizations must learn to recognize and manage the different areas of their KM practices. The authors’ innovative solution, their Knowledge Management Profile, involves the formulation of a new KM maturity model that, it is suggested, may be of vital importance in improving KM practice. The issue of recognizing innovation through social network analysis is addressed in Grippa and Elia through their case study of their Virtual eBMS (VeBMS) project at an Italian university. The authors contend that advances in communication technologies have enabled organizations to develop and operate decentralized organizational structures by supporting coordination among workers in different locations. Such developments have led to a lessening of the degree of formality in control structures and a replacement, to a certain extent, of formal channels of communication with less formal social networks. The authors offer the observation that managers need to match such changes with new processes and tools to continuously monitor the less-obvious social interchanges and relationships within and across their organizations to manage and assess the effectiveness of this innovative type of knowledge network. They describe the development and application of a ‘Social Network Scorecard’ (SNS) tool that can be used to monitor how an interdisciplinary and inter-organizational project team (made up of individuals from academia and industry) was able to collaborate on the implementation of a technological platform that allowed the integration of KM, e-Learning, e-Business, and project management disciplines in a higher education environment. The VeBMS platform consists of a collaborative working environment that supports a range of knowledge-sharing and learning processes within the University of Salento, Italy and was used as a ‘test bed’ to evaluate the validity of the scorecard in practice. The chapter describes how the SNS tool can help monitor the evolution of an organizational community, recognizing creative roles and initiatives and tracing the connections between such initiatives and innovative outcomes. Looking at trends at individual, team and (inter-)organizational levels, we identified the most innovative phases within the team’s life cycle, using network indicators like density and degree centrality. The SNS provided feedback on the effectiveness of the team working to create the integrated platform and helped discover the phases in which the team acted in a manner conducive to innovation. They recognized in the Virtual eBMS project team the typical structure of an innovative knowledge network where learning networks and innovation networks co-exist with a more sparse interest network. Sharp, Eardley, and Shah observe that organizations face the problem of creating a KM strategy that takes account of the complexity of their knowledge issues while being able to communicate them clearly. This issue, called here the Knowledge Strategy Dilemma, is the main theme of this chapter, and the authors maintain that a solution to the dilemma is vital for KM to succeed in practice. They argue,

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however, that the literature shows that the dilemma can be tackled, although the literature includes approaches that help address different parts of the dilemma, there is a lack of an overall or integrated solution. The authors suggest that the best approach to address it in a coherent way is by using a KM method called MaKE, which they have developed for this purpose. The MaKE method is presented and two of its most important principles (traceability and transparency) are explained. Also a set of visual tools that help implement these principles in practice are critically discussed along with some indications of feedback from industry. The authors conclude that these principles, when applied, are capable of some success, and that different forms of communication (including face-to-face meetings with visual aids) are vital in addressing the dilemma. Uden and Naaranoja demonstrate that innovation and knowledge management can no longer be considered to be luxury items. Rather, they should be regarded as strategic necessities and an important source of competitiveness and economic development. As resources, say the authors, knowledge and innovation cannot be separated and knowledge management is essential to innovative organisations. This chapter relates innovation closely to knowledge management and discusses the importance of knowledge management in innovation for organisations. It describes how innovation can be achieved through knowledge management through the use of a case study involving a school renovation and building project in Finland. The case study shows how the process of knowledge creation and sharing was used in practice to help innovation through ‘vision building’. Kamoshida shows that the aim of innovative management is to intentionally create a chaos edge and to foster the innovations that are created as a result. The term “chaos edge” is usually used in complexity studies, but Kamoshida observes that it is also highly applicable to management, where innovation results from creative competition. In this chapter, the management concept used to create such innovation is called “holonic management”. Holonic management requires the elements of cultivation, competition, evaluation, and support. According to Kamoshida, constructing a chaos edge in a holonic management system can foster an internal environment in which ideas are created within a framework of cooperative work. The author’s idea of “heretical management” takes the innovations that are created by a minor group within a corporation and allows them to grow without fear of failure. Kamoshida suggests that this is not just the most effective tool but is a sound method of knowledge management. It is our belief that the twenty chapters in this book, from a wide range of authors, makes a significant contribution to the body of literature on the important topics of KM and innovation and their conjunction. The concept of knowledge management and the capture and use of knowledge in a variety of organizations are topics that have been explored in a number of recent publications. Much of this recent work is theoretical or generic in its approach and applies to the process of KM, rather than to its application. This book explores a specific aspect of knowledge management: the exploitation of knowledge in support of innovation and change. It is hoped that this book will help to create and inform useful thought and debate in this important area of knowledge. Alan Eardley Staffordshire University, UK Lorna Uden Staffordshire University, UK

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REFERENCES Amabile, T. M., Conti, R., Coon, H., Lazenby, J., & Herron, M. (1996). Assessing the work environment for creativity. Academy of Management Journal, 39(5), 1154-1184. Boisot, M., & Canals, A. (2004). Data, information and knowledge: Have we got it right? Journal of Evolutionary Economics, 14, 43- 67. Boje, D.M. (2001). Narrative methods for organizational and communication research. Thousand Oaks, CA: Sage. Bontis, N. (2002). World congress of intellectual capital readings. Boston: Elsevier. Borgatti, S.P., & Cross, R. (2003). A relational view of information seeking and learning in social networks. Management Science, 49(4), 432-445. Borgatti, S.P, & Carboni, I. (2007). Measuring individual knowledge in organizations. Organizational Research Methods, 10(3), 449-462. Byrd, J. (2003). The innovation equation: Building creativity & risk taking in your organization. San Fransisco: Jossey-Bass. Chesbrough, H. W. (2003). Open innovation: The new imperative for creating and profiting from technology. Boston: Harvard Business School Press. Chun, C. W. (2003). Perspectives on managing knowledge in organizations. Cataloging and Classification Monthly, 37(1-2). Davenport, T. H., & Prusak, L. (1998). Working knowledge: How organizations manage what they know. Cambridge, MA: Harvard Business School Press. Davila, T., Epstein, M. J., & Shelton, R. (2006). Making innovation work: How to manage it, measure it, and profit from it. Upper Saddle River, NJ: Wharton School Publishing. Denning, S. (2000). The springboard: How storytelling ignites action in knowledge-era organizations. London: Butterworth. Denning, S. (2005) The Leader's Guide to Storytelling. Jossey-Bass, San Francisco. Dyson, F. (1998). Imagined Worlds. Cambridge, MA: Harvard University Press. Edvinsson, L., & Malone, M. S. (1997). Intellectual capital: Realizing your company's true value by finding its hidden brainpower. New York:Harper Collins. Fagerberg, J. (2004). Innovation: A guide to the literature. In Fagerberg, J., Mowery, D. C., & Nelson, R. R. (Eds.), The Oxford Handbook of Innovations. Oxford: Oxford University Press. Kuhn, T. S. (1970). The structure of scientific revolutions (2nd Ed.). Chicago: University of Chicago Press. Krebs, V. (2008). Social capital: The key to success in the 21st century organization. [Special edition on social networks]. IHRIM Journal, 12(5), 38.

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Luecke, R., & Katz, R. (2003). Managing creativity and innovation. Boston: Harvard Business School Press. Malhotra, Y. (2000). Knowledge assets in the global economy: Assessment of national intellectual capital. Journal of Global Information Management, 8(3), 5-15. McKeown, M. (2008). The truth about innovation. London: Pearson/Financial Times. Nonaka, I. (1991). The knowledge creating company. Harvard Business Review, 69, 96-104. Nonaka, I., & Takeuchi, H. (1995). The knowledge-creating company: How Japanese companies create the dynamics of innovation. New York: Oxford University Press. Polanyi, M. (1997). Personal knowledge: Towards a post-critical philosophy. Chicago: University Of Chicago Press. Probst, G. (1998). Practical knowledge management: A model that works. Prism, 2, 17-29. Snowden, D. (2004). Narrative patterns: The perils and possibilities of using story in organizations. In E. Lesser & L. Prusak (Eds.), Creating value with knowledge. Oxford: Oxford University Press. Spender, J. C. (2008). Organizational learning and knowledge management: Whence and whither? Management Learning, 39(2), 159-176. Sveiby, K. E. (1997). The new organizational wealth: Managing and measuring knowledge-based assets. San Francisco: Berrett-Koehler. Thompson, M. P. A., & Walsham, G. (2004). Placing knowledge management in context. Journal of Management Studies, 41(5), 725-747. Von Krogh, G. (1998). Care in knowledge creation. California Management Review, 40(3), 133-153. Wenger, E., McDermott, R., & Snyder, R. (2002). Cultivating communities of practice: A guide to managing knowledge. Boston: Harvard Business School Press.

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Acknowledgment

The authors would like to acknowledge the contribution of the many people without whose help and patience this book would not have been possible. It is not possible to name them all, so the absence of a particular mention should not imply a lack of appreciation. First we would like to thank the authors from many countries and academic and business areas who have contributed their time and expertise to create a series of chapters that will, it is hoped, provide a varied and informative range of views on the conjoint topics of knowledge management and innovation. Without their work, there would not be a book to add to the body of knowledge in this interesting and important area. Our special thanks go to the editorial team at IGI Global, especially Christine Bufton and Hannah Abelbeck, both of whom have shown great patience and support throughout the lengthy process of editing of the book. Thanks to their care and diligence, any faults in the book are purely the work of the editors. On the personal side, Lorna would like to thank her family for supporting her. She would also like to give thanks to God for His Grace throughout the period. Alan Eardley Staffordshire University, UK Lorna Uden Staffordshire University, UK

Section 1

Knowledge Management and Innovation

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Chapter 1

Universities as KnowledgeIntensive Learning Organizations Constantin Bratianu Academy of Economic Studies, Romania

ABSTRACT The purpose of this chapter is to critically analyze the universities as knowledge intensive learning organizations. It is axiomatic that universities are knowledge organizations since by their own nature universities create, acquire, and transfer knowledge in complex ways. They are knowledge intensive organizations since the density of knowledge field and the dynamics of knowledge processing are much greater than many other organizations. Since learning is one of the major processes within any university, people may consider universities as being by definition learning organizations. This idea induced by a semantic halo effect may lead to a major error. Although a university is an organization based on learning processes, it is not necessary a learning organization. This paper performs a functional analysis of the specific knowledge processes in order to identify the necessary conditions for a generic university to become a learning organization.

INTRODUCTION Universities are among the oldest institutions in Europe, solving creatively the paradox of continuity for many centuries. The paradox is generated by the mission of the university which integrates conflicting tasks ranging from knowledge preservation to knowledge creation: ‘Their survival, often in the same locations, even in the DOI: 10.4018/978-1-60566-701-0.ch001

same buildings, with many of the same activities, may on one level be proof of their conservatism. I believe that on another level it is also proof of the ability of the university to anticipate, to generate or incorporate new knowledge and new ways of thinking – sometimes hesitantly, sometimes slowly, but always with its essential intellectual values and mission intact’(Mayor, 1997, p. 143). Based on a minimum set of functional characteristics, experts in the history of higher education consider the first European universities those cre-

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Universities as Knowledge-Intensive Learning Organizations

ated in Bologna, Paris and Montpellier, followed by those developed at Oxford and Salamanca (Rüegg, 2004). The venerable Bologna University dates from 1088, and the famous Oxford University dates from 1187. However, the mission and the functional matrix of those initial institutions of higher education differ considerably from the present day universities. Main activities associated with these universities were collecting knowledge, preserving it and transfer it to the new generations of students. Knowledge generation was not a part of their mission. A professor was mostly a scholar and not a researcher. Knowledge was considered as a complete set of concepts and ideas about the world, and it was quite static in time. Thus, the purpose of professors was only to transfer this knowledge body to the students. We may say that these first institutions have been designed to acquire and process knowledge, and to deliver value for society in terms of mental representations. The second generation of universities have been established mostly by religious and political powers aiming at developing professional elites to serve their social institutions (Harayama, 1997; Jongbloed et al., 1999). Their main functional structures were designed for professional oriented knowledge processing. In 1810, the University of Berlin was founded on a new paradigm developed by Wilhelm von Humboldt. In this new perspective, a university should approach knowledge scientifically (Gibbons, 1997; Marga, 2005; Mehallis, 1997). It should produce knowledge, not re-produce it. ‘According to Humboldt’s conception, research progress contributes to the elaboration of a system of values that has an influence beyond the walls of academic institutions.’(Harayama, 1997, p. 9). The new Humboldtian paradigm is founded on the unity and the complementary role of teaching and research functions: ‘The subjects to be taught are composed not only of already consolidated knowledge, but also of those elements that remain to be discovered. Therefore, the teaching

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and learning process through the activities of research.’(Harayama, 1997, p. 13). Knowledge generation proved to be a natural constituent of the modern university, its contribution being taken into account in any evaluation metric and any system of ranking universities (Aguillo, Ortega & Fernandez, 2008; Cheng & Liu, 2008). Thus, we may say that universities are entities dedicated to create, preserve and transfer knowledge. Some authors discuss now about a third mission of the university which is that of creating services for society. This is a rather debatable issue since: ‘There is a part of the academic community that is already processing the first academic revolution, i.e. the evolution from teaching to research. Similar resistances found in the first revolution appeared over the last thirty years during the process of applying the second revolution: from teaching and research to services.’(Montesinos et al., 2008, p. 259). The mission of the university, as resulted from its historical evolution, is to create, preserve and transfer knowledge to students and to society. Since all of these mission components involve knowledge creation and knowledge transformation processes, the university is a knowledge intensive organization. Also, universities are by their nature learning based organizations. They deliver knowledge to the students through teaching processes. Students acquire knowledge through learning processes, from their professors and from other different knowledge resources. Since learning is a fundamental process within any university, people may consider universities as being learning organizations. This would be a major mistake, since the transition from individual to collective learning and from collective to organizational learning requires some critical functional conditions that are not fulfilled by most of the universities. The purpose of this chapter is to critically asses and analyse the functional processes within a generic university, and then to

Universities as Knowledge-Intensive Learning Organizations

develop a theoretical investigation concerning the necessary conditions for such a university to become a learning organization. The chapter will be structured as follows: (1) conceptual background; (2) functional analysis of the knowledge processes within a university; (3) university management and leadership; (4) future research directions; (5) conclusion.

BACKGROUND It is an axiom that a university is a knowledge organization. Knowledge is the basic resource used by professors and the main outcome used by students. However, it is extremely difficult to show and to measure knowledge as an outcome since it is intangible and it can be found in the mind of students. Knowledge is the result of processing information and other knowledge forms. Since it is a concept with a complex semantic, it is difficult to be defined. Some authors prefer to work with operational definitions, which are good enough to be used, but remain fuzzy and incomplete. One of the mostly used one is the following: ‘Knowledge is 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 and is applied in the mind of knower. In organization, it often becomes embedded not only in documents or repositories, but also in organizational routines, processes, practices, and norms.’ (Davenport & Prusak, 2000, p. 5). Nonaka and Takeuchi (1995) prefer to discuss about explicit knowledge and tacit knowledge. Explicit knowledge ‘can be articulated in formal language including grammatical statements, mathematical expressions, specifications, manuals and so forth’ (Nonaka & Takeuchi, 1995, p. VIII). Tacit knowledge ‘is personal knowledge embedded in individual experience and involves intangible

factors such as personal belief, perspective, and the value system’ (Nonaka & Takeuchi, 1995, p. VIII). Japanese companies have a different view of knowledge by comparison with the western companies. For the Japanese companies explicit knowledge plays a minor role in the organizational life being only the visible part of an iceberg. The major role is played by the tacit knowledge which is highly personal and hard to formalise: ‘Subjective insights, intuitions, and hunches fall into this category of knowledge. Furthermore, tacit knowledge is deeply rooted in an individual’s action and experiences, as well as in the ideals, values, or emotions he or she embraces.’(Nonaka & Takeuchi, 1995, p. 8). It is interesting to remark the fact that knowledge in the western perspective is mostly rational knowledge, while in the eastern perspective knowledge means both rational and emotional knowledge (Baumard, 1999; Debowski, 2006; Eucker, 2007; McElroy, 2003; Styhre, 2004). Some other authors prefer to work with metaphors. They use metaphors in order to give meaning to knowledge (Andriessen, 2008; 2006; Bratianu, 2008a). Metaphors are mental models (Senge, 1990) people use in order to better understand the real world. They are cognitive approximations developed throughout our education in family, schools, community, church and university (Bratianu, 2007a). A metaphor is not just a semantic similarity between two concepts, but an instrument to develop a new cognitive approximation using a well known concept. It helps in providing a perspective for the new concept, emphasizing certain key characteristics and ignoring others. The known concept is considered the semantic source domain, and the knowledge concept is considered the semantic target domain. It is of interest to mention the fact that in the West dominant metaphors of knowledge are based on the idea of knowledge as stuff. In the East dominant metaphors of knowledge are based on the idea of knowledge as feelings. Organizational knowledge is like a field of forces, highly nonlinear and strongly dynamic

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Universities as Knowledge-Intensive Learning Organizations

(Bratianu & Andriessen, 2008). Any variation of this field generates fluxes or flows, and these fluxes generate processes. When such a process yields a new knowledge configuration leading to a better understanding and decision making, we consider it a learning process. At the individual level, learning produces mental events (Naeve, Sicilia & Lytras, 2008) and relatively permanent changes in the behavioural potential (Maier, Prange & Rosenstiel, 2003) in a given context. Lytras and Pouloudi (2006) frame their research in terms of a learning flow, which ‘corresponds to the archetype of human behaviour that through action and feedback promotes the understanding and adoption to the environment. The contextual character of learning is of critical importance’(p. 67). Individual learning incorporates analysis, decision making, knowledge structuring and storing, action and reflection. It is a highly nonlinear and dynamic process (Baron, 2000; Bratianu & Murakawa, 2004; Senge, 1990; Sternberg, 2005; Vance et al, 2007). Organizational learning has been conceived as a collective process done by integrating all individual constituents and changing the individual pattern of behaviour to an organizational one. Integration is a highly nonlinear process which aggregates individual contributions based on their restructuring flexibility, and synergy generation. Argyris and Schon (1978; 1991; 1996) developed a multi-dimensional analysis of organizational learning known as single and double loop models. They consider that organizations learn through the agency of individual members, by aggregating nonlinearly their contributions. In the single loop model of learning, outcomes are continuously checked against some reference parameters, and errors will be corrected through the feedback action. This is a continuous improvement process which greatly contributed to the TQM success (Hedberg & Wolf, 2003; Oakland, 2003). In the double loop model of learning, the governing vari-

4

ables can be changed, which means a deeper level of transformation. According to Argyris (1999), ‘Governing variables are the preferred states that individuals strive to satisfy when they are acting. These governing variables are not the underlying beliefs or values people espouse. They are variable that can be inferred, by observing the actions of individuals acting as agents for the organization, to drive and guide their actions.’ (p. 68). From organizational learning researchers directed their investigation efforts and imagination toward learning organization. These two concepts should not be used interchangeably since they represent different semantic entities. Organizational learning means activities and processes of learning in the organization, while learning organization refers to a certain type of organizations (Ortenblad, 2001). According to Senge (1990), the basic meaning of a learning organization is: ‘an organization that is continually expanding its capacity to create its future. For such an organization, it is not enough merely to survive’ (p. 14). Senge distinguishes between adaptive and generative learning. Adaptive learning is for survival and quality improvement. It is a reactive process stimulated by the external environment dynamics. Generative learning is stimulated by the internal environment dynamics, and it is concerned mainly with developing new perspectives, options, and exploring new possibilities for future structuring. Reinterpreting the learning organization, Stewart (2001) considers that Senge’s theory received a large attention from the business environment since it embraces ‘many of the vital qualities for today’s organisations, i.e. teamwork, empowerment, participation, flexibility and responsiveness’ (p. 143). Organizational learning can be greatly enhanced if the university develop a significant absorptive capacity, defined by Cohen and Levinthal (1990) as being: ‘the ability of a firm to recognize

Universities as Knowledge-Intensive Learning Organizations

the value of new, external information, assimilate it, and apply it to commercial ends’ (p. 128). For a generic university, these commercial ends can be substituted with its mission objectives. The absorptive capacity is the ability to exploit external knowledge, as an open system with respect to the knowledge field and well defined mechanisms for integrating internally and externally knowledge generation. Thus, prior related knowledge constitutes a functional prerequisite to recognize the value of new knowledge, assimilate it, and use it in the framework established by the university vision and mission. This concept of absorptive capacity can be best understood and developed through an examination of the functional structure of the knowledge processes, which will be discussed in the next section. Also, this concept is closely related to other two concepts: dynamic capabilities (Teece et al., 1997; Eisenhardt & Martin, 2000), and corporate universities (Meisner, 1998; Stumpf, 1998) which will be addressed in the next sections of the present chapter. The semantic halo effect of the learning processes within a university makes people think that universities are learning organizations. This is not the case with many of them due to some organizational learning barriers. Actually, a paradox might be formulated from this perspective: ‘Although a university is an organization based on learning processes, it is not necessary a learning organization’ (Bratianu, 2007b). A given university can become a learning organization if and only if there is at least a strong integrator to assure the transition from individual learning to team and organizational learning. Also, it would be important to advance from adaptive to generative learning. Most universities are far away from being learning organizations due to some mental and functional barriers. Identifying and evaluating these barriers would help in designing adequate solutions to transform these universities in successful learning organizations, able to compete on the new global market of higher education.

FUNCTIONAL ANALYSIS OF KNOWLEDGE PROCESSES The core competences for a generic university are: teaching and learning, performing research, and performing service to society. The degree to which each of these core competences is developed differs from one university to another, according to its mission, its structure, its management and its capacity to compete. Some universities concentrate their energies on developing only teaching programs, others develop teaching and research programs, and the world class universities focus on research through their doctoral programs and research grants (Shattock, 2003). The functional structure of a generic university is illustrated in Figure 1. Teaching and learning activities for students are structured into universities programs at the level of Bachelor and Master. Doctoral programs integrate both learning and research activities. These universities programs are organized under the authority of schools or faculties, according to the existing legislation. In Europe, the Bologna process developed in the last ten years contributed essentially to a new structure of the higher education process in many countries: Bachelor program, Master program and Doctoral program. Although there are some differences in the periods of time needed to successfully accomplished each of this program in different countries, this new functional structure became the main characteristic of the continental European universities. Bachelor and Master programs are mainly based on knowledge transfer processes, while the doctoral program is based mainly on knowledge generation through research. In Table 1 there is a matrix presentation of all types of knowledge processes associated to university programs, and the university management. They are not clear cut entities and their names might have some overlapping meanings. However, they represent the main manifestations of the organizational knowledge field, and we have to understand them in order to analyse the learn-

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Universities as Knowledge-Intensive Learning Organizations

Figure 1. Functional structure of knowledge processes

ing competencies of the university. It is also important to remark the fact that many authors ignore the management contribution to the knowledge dynamics within a university, although this management plays the fundamental role in developing the university as an intensive knowledge learning organization.

journals, research reports, video conferences etc. It is a conscious and oriented managerial process to buy or to get through exchange new knowledge from the external knowledge environment. In this category enter also new links to virtual libraries of other universities and research centres, as well as open access to internet knowledge portals. All major publishing houses and professional associations editing scientific journals have created huge data bases from which universities may perform any particular forms of acquisitions. In learning organizations this type of knowledge process is conceived at the organizational level, and run by

Knowledge Acquisition This is a generic process aiming at developing the organizational knowledge field through embedded knowledge in books, software programs, scientific Table 1. Knowledge processes at organizational level Knowledge processes Acquisition

Bachelor program

Master program

x

x

Socialization

x

x

Externalization

x

Combination Internalization

Doctoral program

Research program

Management

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

Dissemination

x

x

x

x

x

Storage

x

x

x

x

x

Retrieval

x

x

x

x

x

Generation

6

x

Universities as Knowledge-Intensive Learning Organizations

professional knowledge managers. In non-learning organizations this knowledge process is still conceived and run at individual level, with low efficiency and decreased motivation. Acquisition is ultimately an intelligent investment with long term benefits. It is strongly related to the absorptive capacity of the university which refers not only to the acquisition or assimilation of information by an organization but also to the organization’s ability to exploit it. This organizational absorptive capacity depends on the absorptive capacities of its individual members, but it is not, however, simply the sum of them (Cohen & Levinthal, 1990). Being a highly nonlinear process, many managers just cannot understand how to equate short term costs with long term benefits, and how to stimulate students, professors and researchers to navigate into these new learning environments of exceptional values. As a consequence, they are not able to find the necessary financial resources in order to increase the acquisition role in developing the learning university.

Knowledge Generation There are several categories of knowledge generation processes, ranging from restructuring knowledge to creating new knowledge. Since Nonaka (1991; 1994), Nonaka and Takeuchi (1995), and Nonaka and Konno (1998) developed a series of theories concerning knowledge creation based on reciprocal transformations of tacit knowledge into explicit knowledge, and their transfer in a social environment, this process of knowledge generation refers mostly to research outcomes. Scientific discoveries and innovations in all fields of activities resulted from an organizational structure of the university, as a specific outcome of the research projects constitute important contributions to the knowledge universe. World class universities are those universities able to bring important contributions to the progress of science and technology through major results obtained in research activities, published in the main stream of scientific journals. World class universities are

intensive knowledge learning organizations since they generate knowledge more than any other kind of organizations. Their knowledge management played the major role in developing such a perspective and in making continuous and significant investments in strengthening knowledge generation as a core competence able to produce a sustainable competitive advantage. Knowledge generation and its embedding into published papers in the most prestigious scientific journals constitute the main criteria for universities rankings. For instance, the famous ‘Shanghai ranking’ of the world universities performed by the Institute of Higher Education of Shanghai Jiao Tong University is based on the following indicators: (1) Alumni of an institution winning Nobel Prizes and Fields Medals (10%); (2) Staff of an institution winning Nobel Prizes and Fields Medals (20%); (3) Highly cited researchers in 21 broad subject categories (20%); (4) Articles published in Nature and Science (20%); (5) Articles in Science Citation Index-expanded, Social Science Citation Index (20%); (6) Academic performance with respect to the size of an institution (10%), (http://www.arwu.org/rank/2007/ARWU2007_ Top100.htm). These indicators may discourage at first glance any attempt of relating knowledge generation to learning organization and the significance of world class universities. However, if we consider the simple fact that winning a Nobel Prize by any professor means years of research at organizational level, best equipped laboratories, tradition and a special organizational culture able to stimulate young researchers and doctoral students for performance, then we conclude that such indicators measure the knowledge generation competence of the university. Research universities have developed strong doctoral programs and their knowledge management demonstrated a successful leadership. Just to have an idea of what means to be a world class university we present in Table 2 the first ten universities ranked by the Institute of Higher Education of Shanghai Jiao Tong University for 2007.

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Universities as Knowledge-Intensive Learning Organizations

Socialization According to Nonaka, Toyama and Byosiere (2003) ‘Socialization is the process of bringing together tacit knowledge through shared experiences’(p. 495). Since tacit knowledge is context dependent and very difficult to express, the key to socialization is to share the same experience through joint activities. Socialization is strongly linked with the cooperation and team working, values which are specific for Japanese education (Ohmae, 1982). In Western cultures is encouraged individual work and competition between individuals, which means a reduced level of socialization. Considering the type of programs shown in Table 1, is easy to understand that Bachelor and Master Programs have little socialization in their structure, with the exception of those fields of knowledge where internship programs are developed. For instance, in the medical education internship programs in hospitals are contributing to sharing tacit knowledge. By contrast to them, individual homework developed in American universities reduces drastically the socialization process. Learning universities, like learning companies, must find new ways of developing socialization as a part of university education. Socialization is

difficult to manage because it is the conversion of tacit knowledge. It is necessary to cultivate the cultural values of trust, friendship, care and love in order to overcome more easily the individualism barriers. Then, the knowledge management must design learning activities for groups of students, and for internship modules. Only transcending individual boundaries socialization can be effective in tacit knowledge transfer.

Externalization Externalization means to extract valuable parts of the tacit knowledge and to express them into a rational form, easy to be transferred and understood by other individuals. Externalization is a pure individual process and it is the key element for knowledge creation. ‘When tacit knowledge is made explicit, knowledge becomes crystallized, at which point it can be shared by others and can be made the basis for new knowledge’(Nonaka, Toyama & Byosiere, 2003, p. 495). The success of externalization comes from an efficient use of metaphors, analogies and models. Metaphors help understanding new concepts intuitively by using semantic domains of known concepts. They are able to connect concepts which apparently are

Table 2. The top ten world universities for 2007 (http://www.arwu.org/rank/2007/ARWU2007_Top100. htm, retrieved on 29 October 2008) Institution

World rank

8

Country

National rank

1

Harvard University

USA

1

2

Stanford University

USA

2

3

University of California at Berkeley

USA

3

4

Cambridge University

UK

1

5

Massachusetts Institute of Technology (MIT)

USA

4

6

California Institute of Technology

USA

5

7

Columbia University

USA

6

8

Princeton University

USA

7

9

University of Chicago

USA

8

10

Oxford University

UK

2

Universities as Knowledge-Intensive Learning Organizations

uncorrelated, and to highlight some common features which can then be further clarified by using analogies. These analogies help one understand the unknown through the known, and yield a link between an image and a rational model. Modelling and simulation are very useful teaching instruments in the learning environment. In universities, externalization processes are extremely important for researchers to formulate their results and to interpret them in new perspectives. Thus, externalization plays an important role especially in doctoral programs, where research is essential and individual experience of students is not so large. Also, in teaching activities associated to Bachelor and Master programs externalization is an important process for those professors who have a rich experience in the discipline field, and who can use it for teaching. Unlike undergraduate students, those attending MBA or EMBA programs have already a great deal of professional experience. For them, the structure of the program must contain more activities adequate to promoting externalization activities. However, in order to develop this process as an organizational capability, and to make use of all the tacit knowledge in the university it is necessary to stimulate continuously people to make the effort of self-analysis and self-interrogation. The university becomes a learning organization if its knowledge management understands the importance and the benefits of encouraging externalization in all academic areas and research activities.

Combination Combination is a process by which discrete pieces of knowledge are aggregated into a set of rational knowledge, and then transfer to some other people. The aggregated product may be considered as a new organizational knowledge. For example, let us consider the annual report of the university. It is the practical result of integrating knowledge

coming from each department and school, and then interpreted at the organizational level. This report is written, distributed to all stakeholders and discussed. The knowledge contained into this report has been obtained through the combination of different components, and it brings new knowledge about the university successes in the past year. Creative use of computerized communication networks and large scale databases can improve this combination process. From learning perspective, combination is a pivotal process since all the knowledge transfer from professors toward students is realized through it. In the Nonaka knowledge cycle combination is the only rational and explicit process having a social dimension. Socialization, on the other hand is a non-rational and implicit process having a social dimension. Learning universities develop combination by replacing the classical professor – students transfer process with creating learning environments where students are active knowledge agents. Learning universities break down the virtual walls between classical disciplines and invite professors, researchers and students to an inter- and multidisciplinary approach. It is known that knowledge variety is stimulating creation of new knowledge, and that combination is used in all intensive creativity activities like focus groups and brainstorming. Knowledge management in such a learning university will find always ways to stimulate combination through networking, e-learning and virtual campuses. Combination increases the knowledge entropy and stability of the system through dissemination, and stimulates organizational innovation at all levels. Combination integrates formal and informal communicating networks, and contributes to improving the decision making process. Combination can be used a vehicle process in problem-based learning, to increase students contribution to the learning process and to develop their critical thinking (Eardley & Uden, 2008).

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Universities as Knowledge-Intensive Learning Organizations

Internalization This is the final process in the Nonaka knowledge cycle. Internalization means transforming explicit knowledge into tacit knowledge at the individual level. According to Nonaka, Toyama & Byosiere (2003) ‘Internalization is the process of embodying explicit knowledge as tacit knowledge. It is closely related to learning-by-doing’ (p. 497). From a practical point of view, internalization contains two aspects. First, explicit knowledge is embodied in action and actualizes concepts about strategy, innovation, or improvement (i.e. training programs for employees). Second, explicit knowledge can be embodied through simulations or experiments in order to stimulate learning-by-doing mechanisms. In classical teaching of the non-learning universities internalization is assumed by professors, such that there is no organizational effort of stimulating and improving it. Knowledge management in the learning universities uses motivation as a leverage mechanism, and de-construct all bottlenecks by implementing high speed knowledge fluxes and user friendly systems. It is important to stress the fact that internalization is a highly nonlinear and personal process. It can be improved only by creating an adequate motivational field and stimulating individual innovation. Considering the overall perspective of the Nonaka cycle, ‘Knowledge is created through a continuous and dynamic interaction between tacit and explicit knowledge. This interaction is shaped through the SECI process, that is, through the shifts from one mode of knowledge conversion to the next: socialization, externalization, combination, and internalization’(Nonaka, Toyama & Byosiere, 2003, p. 497). This ‘Nonaka cycle’ is not confined to a certain organizational level. It is produced from individual to group and organizational levels, and from a very simple configuration to a very large complexity of the social environment. Learning universities must analyse their knowledge cycles

10

and develop strategies to increase the speed and densities of all knowledge fluxes.

Dissemination Dissemination might be considered identical with knowledge transfer. However, it can be distinguished from it since it is an asynchronous and unidirectional process. Dissemination is done using different media systems, from virtual books to web pages. Knowledge is posted for potential consumers, but there is no certain end user. Thus, we speak about dissemination as a specific knowledge transfer from a higher level to a lower level of understanding, when the end users are not synchronous receivers with the knowledge disseminator. This type of processes is frequently used in e-learning, online learning and virtual campuses. Learning universities are aware of these new opportunities and use dissemination for all categories of learners from inside and outside of the university. Dissemination became an important knowledge process in the third mission of the university, i.e. performing service to the community. Dissemination assumes a high level of transparency, new mechanisms for validating the knowledge sources, and improved semantic webs.

Storage and Retrieval Storage is an organizational process by which knowledge can be deposited in a structured way, such that retrieval can be done efficiently. In a classical campus, storage could have been done by organizing library spaces for books and scientific journals. In the new university campuses storage means using intelligently computer facilities and advanced software programs for knowledge retrieval and data mining. These processes become more important for the virtual universities, which should incorporate the features of the learning organizations.

Universities as Knowledge-Intensive Learning Organizations

LEARNING ORGANIZATIONS Beyond the debate between individual learning and organizational learning (Argyris, 1999; Dierkes et al., 2001; Senge, 1990), empirical and theoretical research demonstrated the existence of learning organizations. They are ‘organizations where people continually expand their capacity to create the results they truly desire, where new and expansive patterns of thinking are nurtured, where collective aspirations is set free, and where people are continually learning how to learn together’ (Senge, 1990, p.3). Learning occurs when the organization is able to construct solutions for new problems, or within new environments. Learning organizations have complex and dynamic functional structures able to display two learning loops: a first loop characterized by feedback, and a second loop characterized by feedforward. The first loop is important for connecting outputs to inputs, in order to eliminate any mismatch between the actual product and the target product. Feedback is necessary for production control and its adjustment. However, single loop learning is effective especially in static environments. For dynamic environments and turbulent changes, single loop learning is not enough anymore. A second loop is necessary for changing the system settings and to create conditions for organizational adaptation to the new environment requirements. For an university, the theory of single loop learning emphasizes the production process, which means the levels of individual and group learning incorporated in the Bachelor, Master and Doctoral programs. Improvements can be obtained through individual efforts and an efficient quality management at the organizational levels. However, single loop learning is based only on feedback effects and it aims to improve knowledge processes by corrections with respect to some standard values or norms. Thus, the results of single-loop learning are limited by the level of these standards. Also, single loop learning entails the development of the organization dynamic capabilities (Eisen-

hardt & Martin, 2000; Teece et al., 1997). These dynamic capabilities consist of specific strategic and operational processes that create value for organizations within dynamic external business environment by manipulating efficiently tangible and intangible resources. Dynamic capabilities can be defined as: ‘The firm’s processes that use resources – specifically the processes to integrate, reconfigure, gain and release resources – to match and even create market change. Dynamic capabilities thus are the organizational and strategic routines by which firms achieve new resource configuration as markets emerge, collide, split, evolve, and die.’(Eisenhardt & Martin, 2000, p. 1107). Actually, these dynamic capabilities enable the organization to achieve a dynamic equilibrium between the internal field of forces and the external field of forces. They emphasize the key role of strategic management in ‘appropriately adapting, integrating, and reconfiguring internal and external organizational skills, resources, and functional competences to match the requirement of a changing environment’ (Teece et al., 1997, p. 515). The theory of double loop learning emphasizes the importance of the management process, and of the organizational culture. Values and standards are formulated by the top management, and they can be changed only through a conscious process of knowledge management. This is a powerful integrator, especially in a university where the production process is mainly a learning process (Bratianu, 2008a). According to Bratianu, Jianu & Vasilache (2007), ‘An integrator is a powerful field of forces capable of combining two or more elements into a new entity, based on interdependence and synergy. These elements may have a physical or virtual nature, and they must posses the capacity of interacting in a controlled way’. The interdependence property is necessary for combining all elements into a system. The

11

Universities as Knowledge-Intensive Learning Organizations

synergy property makes it possible to generate an extra energy or power from the working system. It makes the difference between a linear and a nonlinear system. A learning university must have its management as a powerful integrator of all professors and students, able to learn itself and to contribute to the synergy of the whole system. Unfortunately, in many European universities where the top management members are elected professors, based on democratic procedures, there are very few chances to have real managers. They might be excellent researchers and professors, yet not having any managerial experience and talent. It is only the halo effect that a good professor should be in the same time a good manager. Actually, this halo effect has been used consciously in the former socialist countries, where the political regime had no interests in choosing those professors with managerial abilities (Bratianu, 2008b). These professors acting like deans and rectors were passive managers since all the decisions have been taken in a strongly centralized way at the level of Ministry of Education. For these managers conforming to the ministerial decisions has always been the law, while learning could trigger penalties. Conforming processes are generated by the inertial organizational forces and the influence of the external forces. These external forces represent cultural values, legislation, social an economical developments, education and technology, as well as a given political ideology. The most explicit forces are represented by the specific legislation in education. This legislation establishes how much autonomy a university has in performing its mission to society. For instance, in the former socialist countries the legislation gave no autonomy to universities. Their management had only to apply at the organizational level decisions coming from the Ministry of Education. After the political regime change, universities received in a progressive way some degree of liberty and academic autonomy. That means that the university management can decide upon the curriculum structure and content, specific

12

admission and graduation procedures, selecting and promoting faculty staff, as well as electing by democratic votes their representatives for different managerial positions. Although we have to acknowledge a lot of improvements by comparison with the former political regime, the real decision making process is bounded by financial means which are almost completely in the power of the governmental forces. From this point of view, conforming is much safer then learning and many rectors operate in the domain of strictly applying the imposed regulations without any effort oriented toward changing them (see Figure 2). These universities cannot be considered learning organizations, even if there are some innovations done at the level of individuals or groups of students and professors. These innovations will fit the single loop learning model. In order that a university to become a learning organization is necessary that the innovative forces which are the driving forces of change to be larger than the inertial forces, i.e. the learning component to be greater than the conforming component of the management vector. European universities are in a powerful field of external inertial forces, especially due to their traditions and to their integration into national higher education systems. For instance, the Humboldtian university linked the university to the State, since its mission was to develop professionals for the German State needs (Gare, 2005). That is why these universities will not have full autonomy while receiving financial support from the states, and thus their management learning component will not overcome easily the conformity component. ‘Universities are so linked to their countries that the examination of their governance structures cannot leave aside the governance structures of national higher education systems. Apart from particular characteristics, in fact, throughout Europe, universities are steered and coordinated by central states, directly or through the national

Universities as Knowledge-Intensive Learning Organizations

Figure 2. An illustration of the competing learning and conforming processes

university system. That is why we can say that university governaqnce refers both to the single institution and to the national higher education system.’(Lazzaretti & Tavoletti, 2006). Exceptions come from the traditional English universities which subscribed to the principle of ownership of individual institutions rather than to the incorporation of universities into their national systems. Their management vector enters the learning zone, and there is no surprise why these universities developed so earlier the strategic management by comparison with the French and German universities. The learning university must change its governance structure and reduce its governmental conformity component of the management. This change can be done by a radical reform of corporatization of national universities, and develop a governance based on leadership and competitiveness. This reforming happened in Japan starting on April 1st 2004 (Bratianu, 2004). The main idea of this reform is to replace the democratic election system of managerial functions by a corporate selection procedure based on personal competence and visionary leadership, and to substitute the former academic decision making with a corporate like decision making. Thus, universities get more autonomy in their governance in exchange for more competitive management and leadership. Thus, the conforming component

of the management vector is drastically reduced, and conditions set up for innovation. Universities can become now learning universities. There is a new class of universities that make sustainable efforts to become learning organizations based on their market driven vision. They are the corporate universities, those that invest in flexible learning and innovation to manage knowledge (Meister, 1998; Stumpf, 1998). A corporate university can be defined as ‘a strategic umbrella for developing and educating employees, customers, and suppliers to meet an organization’s strategy’ (Meister, 1998, p. 267). In a synthesis, the most important characteristics of a learning university are the following: • • • • • • •

Visionary leadership; Value driven management; Double loop learning; Intensive communication network; Dynamic organizational culture; Developed absorptive capacity; Decentralized decision making process at the levels.

Vision is a virtual state of the organization in a possible future. Leaders can develop such visions and then develop convergent strategies to achieve such targets. Managers are caught mostly in operational activities and tasks, being under the

13

Universities as Knowledge-Intensive Learning Organizations

pressure of efficiency and conformity. ‘To be sure, it is sometimes difficult to act for the future when the demands of the present can be so powerful and the traditions of the past so difficult to challenge. Yet, perhaps this is the most important role of the university president’ (Duderstadt, 2000, p. 258). Value driven management, which is a direct result of the rising creative class must substitute the controlling management coming from the industrial era. The second loop of learning cannot be functional in the rigid structure of the controlling management, and the controlling management cannot be efficient in a dynamic environment. A substantial change has to be done in the traditional university management and this change has to be supported by a new dynamic organizational culture. Learning means absorptive capacity both at the individual and organizational levels. And all of these characteristics of the learning university cannot exist in a centralized decision making process, as it happens in most former socialist countries. It is imperative to decentralize the decision making down to the levels of the schools and departments, and to develop dynamic capabilities able to sustain double loops of learning.

FUTURE RESEARCH DIRECTIONS The topic of intensive knowledge universities as learning organizations is almost in its incipient phase, due to its complexity and the new perspectives of research. However, it is a crucial research field due to the changing role of the university in society and to the new mission formulations. It is almost an axiom that universities are intensive knowledge organizations due to their knowledge creation and knowledge dynamics. However, this intrinsic characteristic cannot be fully developed due to some barriers in their governance coming from tradition and their links to the nation states. Future directions of research could be the following: (1) university as an intelligent organization; (2) university as an entrepreneurial organization

14

through innovation and knowledge creation; (3) university knowledge dynamics and competitiveness; (4) governance and leadership for successful 21st century universities.

CONCLUSION Globalization and business turbulence changed the economic paradigms and the future predictability of our times. In a complex and strongly nonlinear world, these changes triggered many other changes at lower levels and smaller scales which are context dependent. In this category, we may include a series of changes produced in the academic world that imposed new perspectives of understanding and analysis. Although universities are next to Church the oldest social institutions, they must adapt to the new political, technological, social and economical environments. They are knowledge intensive organizations and their missions integrate learning, research and service to community objectives. Due to a semantic halo effect of their learning processes, universities are considered by many people as learning organizations. The purpose of this chapter is to analyse the evolution of university mission, the functional knowledge processes, and to see the necessary conditions for a university to become a learning organization. Focus is on the European universities due to their traditions and to recent changes triggered by the Bologna process. Since continental universities have democratic election systems for their managerial functions, and they are strongly linked to their governmental field of forces, directly or indirectly through the national university systems, their behaviour is still in the conformity zone. In order to become learning organizations they must change the governance system and to increase innovation in the management process.

Universities as Knowledge-Intensive Learning Organizations

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Lytras, D. M., & Pouloudi, A. (2006). Towards the development of a novel taxonomy of knowledge management systems from a learning perspective: An integrated approach to learning and knowledge infrastructures. Journal of Knowledge Management, 10(6), 64–80. doi:10.1108/13673270610709224 Maier, G. W., Prange, C., & Rosenstiel, L. (2003). Psychological perspectives of organizational learning. In Dierkes, M., Antal, A. B., Child, J., & Nonaka, I. (Eds.), Handbook of organizational learning and knowledge (pp. 14–35). Oxford: Oxford University Press. Marga, A. (2005). University reform today (4th ed.). Cluj-Napoca, Romania: Cluj University Press. Mayor, F. (1997). The universal university: The university – the crucible of Europe. CRE-CEPES UNESCO Papers on Higher Education, 111, 143–151. McElroy, M. W. (2003). The new knowledge management: Complexity, learning, and sustainable innovation. Amsterdam: Butterworth Heinemann. Mehallis, M. V. (1997). Teaching versus research: An outmoded debate in the knowledge society. Higher Education in Europe, 22(1), 31–43. doi:10.1080/0379772970220104 Meister, J. (1998). Corporate universities: Lessons in building a world-class work force. New York: McGraw-Hill Trade.

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Ortenblad, A. (2001). On differences between organizational learning and learning organization. The Learning Organization, 8(3), 125–133. doi:10.1108/09696470110391211 Rüegg, W. (2004). A history of university in Europe. Cambridge: Cambridge University Press. doi:10.1017/CBO9780511496868 Senge, P. (1990). The fifth discipline: The art and practice of learning organizations. London: Random House. Shattock, M. (2003). Managing successful universities. Maidenhead, UK: Society for Research into Higher Education & Open University Press. Sternberg, R. J. (2005). Thinking styles. Cambridge: Cambridge University Press. Steward, D. (2001). Reinterpreting the learning organization. The Learning Organization, 8(4), 141–152. doi:10.1108/EUM0000000005607 Stumpf, S. A. (1998). Corporate universities of the future. Career Development International, 3(5), 206–211. doi:10.1108/13620439810229424 Styhre, A. (2004). Rethinking knowledge: A Bergsonian critique of the notion of tacit knowledge. British Journal of Management, 15, 177–188. doi:10.1111/j.1467-8551.2004.00413.x Teece, D. T., Pisano, G., & Shuen,A. (1997). Dynamic capabilities and strategic management. Strategic ManagementJournal,18(7),509–533.doi:10.1002/ (SICI)1097-0266(199708)18:7<509::AIDSMJ882>3.0.CO;2-Z Vance, C., Groves, K. S., Paik, Y., & Kindler, H. (2007). Understanding and measuring linearnonlinear thinking style for enhanced management education and professional practice. Academy of Management Learning & Education, 6(2), 167–185.

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Chapter 2

Key Characteristics Relevant for Selecting Knowledge Management Software Tools Hanlie Smuts University of South Africa & Mobile Telephone Networks (Pty) Ltd, South Africa Alta van der Merwe University of South Africa & Meraka Institute, CSIR, South Africa Marianne Loock University of South Africa, South Africa

ABSTRACT The shift to innovation and knowledge as the primary source of value results in the new economy being led by those who manage knowledge effectively. Today’s organizations are creating and leveraging knowledge, data, and information at an unprecedented pace—a phenomenon that makes the use of technology not an option, but a necessity. Software tools in knowledge management (KM) are a collection of technologies and are not necessarily acquired as a single software solution. Furthermore, these KM software tools have the advantage of using the organization’s existing information technology infrastructure. Organizations and business decision makers spend a great deal of resources and make significant investments in the latest technology, systems, and infrastructure to support KM. It is imperative that these investments are validated properly, made wisely, and that the most appropriate technologies and software tools are selected or combined to facilitate KM, knowledge creation, and continuous innovation. In this chapter, a set of characteristics are proposed that should support decision makers in the selection of software tools for knowledge creation. These characteristics were derived from both in-depth interviews and existing theory in publications. DOI: 10.4018/978-1-60566-701-0.ch002

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Key Characteristics Relevant for Selecting Knowledge Management Software Tools

Introduction Imagine that, in the same way that a disc failure on your personal computer or laptop erases all information in the file folders, all intellectual capital within your organization is erased from the employees’ minds and the organization’s storage media. There is no doubt that the market value of such an organization will be affected severely as decisions in an organization are made based on sufficient, relevant and accurate knowledge. Stewart (1997) supports this notion that the management of knowledge turned out to be the most important economic responsibility of individuals, businesses and nations, as it forms a key component of what is acquired, produced and sold. Knowledge assets are of much greater value than any tangible asset, which includes natural resources, large factories, equipment and land – all of which provided organizations with a competitive edge in the past (Alavi & Leidner, 2001; Davenport & Prusak, 1998). This knowledge asset provides the basis for creating sustainable competitive advantage in the knowledge age (Nonaka, Toyama, & Byosiere, 2001; Vandaie, 2007). Furthermore, as new technologies, innovation, organizational flexibility and new and better forms of leadership propel the growth and earnings of knowledge-intensive companies, so the need to extract wealth from brainpower and knowledge (individual and organizational) becomes increasingly pressing. This importance of knowledge is confirmed by Becker et al (2001) who conclude that machinery and equipment are not the distinguishing aspects any more, but rather the capability to use it resourcefully. An organization that kept its workforce skills and expertise could operate quickly even though it lost all of its equipment. An organization that lost its workforce, while keeping its equipment, would never recover. This shift to knowledge as the primary source of value results in the new economy being led by those who manage knowledge effectively - orga-

nizations that create, find and combine knowledge into new products and services faster than their competitors (Moss-Kanter, 1997). Drucker (Hibbard, 1997, p. 46) states that “We now know that the source of wealth is something specifically human: knowledge. If we apply knowledge to tasks we already know how to do, we call it productivity. If we apply knowledge to tasks that are new and different, we call it innovation. Only knowledge allows us to achieve those two goals.” Today’s organizations are creating and leveraging knowledge, data and information at an unprecedented pace and the extraordinary growth in on-line information makes the use of technology not an option, but a necessity (Folkens & Spiliopoulou, 2004; Lindvall, Rus, Jammalamadaka, & Thakker, 2001). This influence of technology on the maintenance of KM actions is widely accepted, as technology adds value by reducing time, effort and cost in enabling people to share knowledge and information (Chua, 2004). It is especially relevant when it is closely aligned with organizational requirements - the way people work and are supported by and integrated with relevant processes (Hoffmann, Loser, Walter, & Herrman, 1999; Wind & Main, 1998). In addition to the growth in information technology (IT), organizations embark on employee information access projects, like the creation of knowledge bases, intranets, chat rooms, fulltext indexing tools and document management tools as necessitated by KM (Lindvall, Rus, Jammalamadaka, & Thakker, 2001). KM agility and optimal support of technology motivate the need for research in which the focus is on an understanding of the key characteristics of a KM solution by exploring and describing the nature of knowledge. Therefore, this chapter focuses on providing guidelines in the selection of a KM system solution and provides an example where the selection criteria have been applied as a cost saving solution.

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Key Characteristics Relevant for Selecting Knowledge Management Software Tools

Background While some epistemologists spent their lives trying to understand what it means to know something (Clarke & Rollo, 2001; Davenport & Prusak, 1998). Plato first introduced the concept of knowledge as justified, true belief in 400 B.C. (Meno, Phaedo and Theaetus as quoted by Nonaka & Takeuchi, 1995). Advances in knowledge described the achievements of the ancient Greek, Roman, Egyptian and Chinese civilisations and the transforming impact of the industrial revolution was characterised by the application of new knowledge in technology (Clarke & Rollo, 2001; Moteleb & Woodman, 2007). For the purpose of this chapter, a more pragmatic approach has been followed and the following working description of knowledge has been explored (Davenport & Prusak, 1998, p. 5): “Knowledge is a fluid mix of framed experiences, values, contextual information and expert insight that provides a framework for evaluating and incorporating new experiences and information. It originates and is applied in the minds of knowers. In organizations, it often becomes embedded not only in documents or repositories but also in organizational routines, processes, practices and norms.” Knowledge can either be categorized as being explicit or implicit. Explicit knowledge can be defined as knowledge that has been articulated in the form of text, diagrams, product specifications and so on (Clarke & Rollo, 2001). Nonaka (1995) refers to explicit knowledge as formal and systematic, like a computer program. In organizations today, explicit knowledge resides in best practices documents, formalised standards by which goods and services are procured and even within performance agreements that have been documented in line with company and divisional goals and objectives. Implicit knowledge is far less tangible than explicit knowledge and refers to knowledge deeply embedded into an organization’s operat-

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ing practices (Kothuri, 2002). Tacit knowledge, as a dimension of implicit knowledge, includes relationships, norms and values. It is knowledge that cannot be articulated and it is much harder to detail, copy or distribute, to the contrary, the knowing is in the doing in this instance (Clarke & Rollo, 2001). Tacit knowledge can provide competitive advantage to organizations as it is protected from competitors (Hahn & Subramani, 2000; Wessels, Grobbelaar, & McGee, 2003) unless key individuals are lured away, of course (Lindvall, Rus, Jammalamadaka, & Thakker, 2001). The management of explicit and implicit knowledge is a multifaceted subject based on the dimensions of knowledge and therefore there are various and varied definitions for it (Newman & Conrad, 1999). McCullough (2005) concludes that, based on the vast majority of academic research into KM, there is a general difficulty for organizations to explain what they mean when they use the term KM. Sveiby (1997, p. 37) defines the management of knowledge as “the art of creating value by leveraging intangible assets”. Meyer and Botha (2000, p. 278) define it as “the management of corporate processes designed to create, disseminate and protect knowledge in support of sound decisions leading to profit”. Godbout (1999) defines KM by suggesting that it is not knowledge that gives the competitive edge, but the capacity to transform knowledge into competencies and replicate know-how. According to Drucker (Edersheim, 2007), the most direct use of knowledge within an organization is to build its own capabilities, and that the application of knowledge to knowledge is the critical factor in productivity moving forward. Lindvall, Rus et al (2001, p. 3) define KM as “the practice of transforming the intellectual assets of an organization into business value”. For the purpose of this chapter the following definition of KM as suggested by Choo (2000) will be used: “a framework for designing an organization’s goals, structures and processes so that the

Key Characteristics Relevant for Selecting Knowledge Management Software Tools

organization can use what it knows to learn and to create value for its customers and community”. Technology is a key enabler of KM and KM processes as it extends the reach and enhances the speed of knowledge transfer (Chua, 2004; Wilson & Snyder, 1999; Yu, Kim, & Kim, 2004). Technology permits the knowledge of an individual or group to be structured and codified and allows distribution of knowledge across the world (Davenport & Prusak, 1998; Wessels, Grobbelaar, & McGee, 2003). KM technology is a broad concept and organizations apply a wide variety of technologies to the objectives of KM (Davenport & Prusak, 1998; Lindvall, Rus, Jammalamadaka, & Thakker, 2001). Explicit knowledge is found in reports, documents and manuals and can easily be gathered and stored as a knowledge base (Dix, Wilkinson, & Ramduny, 1998; O’Leary, 1998). Organizations use groupware applications to collect, store and share their explicit knowledge, and once this has reached a sufficient level of efficiency, collaborative technologies such as intranet, the internet, extranet, e-mail, video-conferencing and tele-conferencing are used to assist in the growth of tacit knowledge transfer. In order to enable organizations to retrieve captured knowledge, knowledge route maps and directories are developed to create an understanding of the location of knowledge (Alavi & Leidner, 2001; Clarke & Rollo, 2001). Knowledge networks are created using virtual business environments such as chat rooms, team web sites and learning communities with the development of specific applications of technology such as databases, workflow systems, personal productivity applications and enterprise information portals (O’Leary, 1998; Wilson & Snyder, 1999). According to Tsai and Chen (2007, p.258) are “knowledge management systems more than just information systems or IT-enabled tools in support of knowledge management activities. Instead, a knowledge management system must be a socio-technical system as a whole which comprises the knowledge itself (the intellectual

capital of the organization), organizational attributes (intangibles such as trusting culture), policies and procedures, as well as some form of electronic storage and retrieval systems.” Different ways of classifying KM technologies are utilised in the literature and Antonova, et al (2006) categorised technological solutions according to the following KM processes: (1) generation of knowledge, (2) storing, codification and representation of knowledge, (3) knowledge transformation and knowledge use and (4) transfer, sharing, retrieval, access and searching of knowledge. These specific implications of knowledge and KM on KM solutions are important as these different views lead to different perceptions and definitions of KM systems (Asgarkhani, 2004). As such a KM solution enables knowledge creation, it provides the basis for continuous innovation as one innovation leads to another (Nonaka & Takeuchi, 1995).

Key considerations impacting knowledge management Organizations today face the challenge of creating an infrastructure that facilitates knowledge transfer – both explicit and implicit. Explicit knowledge is easy to identify based on the definitions above, but implicit – and specifically tacit – knowledge transfer, remains an area of focus. Organizations have to manage this process and key issues in order to enable the organization to transform tacit knowledge into explicit knowledge and make it available and accessible company-wide (Clarke & Rollo, 2001; Gordon, 1999).

Information Technology Nonaka, Reinmoller and Toyama (2001, p. 829) identify several problems with the current use of software tools as the challenge for IT is to aid a dynamic process of knowledge creation, not a stagnant process of information management

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Key Characteristics Relevant for Selecting Knowledge Management Software Tools

and often emphasises the efficiency of processing existing information rather than creating new knowledge. Furthermore, current IT-based KM mainly focuses on knowledge that has been articulated in some tangible form and fails to deal with implied knowledge such as hunches and gut feelings. Less or no emphasis is placed on new visions and innovation as these KM software tools extract profits through knowledge economies of scale by re-using existing knowledge only (Marwick, 2001). KM that relies only on such packaged tools, cannot gain sustainable competitive advantage due to the rapid dissemination of best practice in IT (Davenport & Prusak, 1998). A long-term view of fostering the knowledgebase competence of an organization is required when selecting KM software tools and IT is needed that aids an effective and efficient knowledgeconversion process while increasing the swiftness and ease of switching from one such process to another (Yu, Kim, & Kim, 2004).

Knowledge Work KM is defined in this chapter as a framework for designing an organization’s goals, structures and processes so that the organization can use what it knows to learn and to create value for its customers and community (Choo, February 2000). In addition to this framework, organizations must take key strategic steps to define and quantify the source and nature of the bodies of knowledge that need to be included in the KM framework (Wilson & Frappaolo, 1999). The organization must protect itself from knowledge leaving the organization in order to optimally use what it knows across all perspectives – vision and strategy, roles and skills, policies and procedures and tools and platforms (Holloway, 2000; Lindvall, Rus, Jammalamadaka, & Thakker, 2001). An understanding of knowledge in organizations, the modes and context of conversion of knowledge and the technologies

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used in this conversion are tactical approaches to knowledge creation. A strategic knowledge creation solution encompasses all of these steps in one seamless and complete procedure for knowledge work (Hoffmann, Loser, Walter, & Herrman, 1999; Kothuri, May 2002; Marwick, 2001; Vequist & Teachout, 2006). According to Naisbitt (1982, as quoted by Nickols, 2000), white-collar workers first outnumbered blue-collar workers in the USA in 1956 where the ratio of manual workers to knowledge workers was 2: 1 in 1920 and 1: 2 by 1980. The number of knowledge workers in the computer industry in the USA was estimated at 72% based on a testimony before a senate sub-committee (Nickols, 2000). This new type of worker requires a different type of management (Edersheim, 2007; Frappaolo, 2006; Garvin, 1998; Westhuizen, 2002) and although knowledge is not new, the recognition of knowledge as a corporate asset, is new (Davenport & Prusak, 1998; Hoffmann, Loser, Walter, & Herrman, 1999; Stewart, 1997). Davenport (1998) concludes that there is currently a greater need than in the past to optimize organizational knowledge and to obtain as much value as possible from it. Table 1 summarises the definition of manual work and knowledge work (Nickols, 2000). A major difference between knowledge work and manual work is that knowledge work is information-based and manual work is materialsbased. A manual work process, regardless of how much skill and knowledge is required of the worker, consists of converting materials from one form to another with tangible results. Knowledge work, on the other hand, consists of converting information from one form to another with frequently intangible results (Nickols, 2000; Stewart, 1997). This difference in work output informs how these workers will be performance managed and how they will be measured (Edersheim, 2007; Krogh, Ichijo, & Nonaka, 2000).

Key Characteristics Relevant for Selecting Knowledge Management Software Tools

The Learning Organization Senge (1990) presented tools and ideas for the learning organization during the early 1990s. He claimed that learning organizations can be built “where people continually expand their capacity to create results they truly desire, where new and expansive patterns of thinking are nurtured, where collective aspiration is set free and where people are continually learning how to learn together” (Senge, 1990, p. 3). He identified five key dimensions for building organizations that can truly learn namely systems thinking, personal mastery, mental models, building shared vision and team learning. The learning organization creates an environment where the behaviours and practices involved in continuous learning, are actively encouraged and facilitated. This process of continuous learning includes the exchange of both explicit and implicit knowledge (Asgarkhani, 2004; Garvin, 1998; Kotelnikov, 2001; Marwick, 2001; Salisbury, 2003; Senge, 1990; Vequist & Teachout, 2006). Compared to a learning organization, a coaching organization goes beyond this exchange and also

focuses on how to unlock the inner power of people in the organization in order to make them innovators and self-leaders (Hoffmann, Loser, Walter, & Herrman, 1999; Kotelnikov, 2001). The next stage entails moving from a teaching organization where both learning and teaching take place to a coaching organization where coaching is added to the learning and teaching dimensions. Kotelnikov (2001) defines unique advantages of such a coaching organization. The first advantage entails ensuring enhanced development of individuals and collective tacit knowledge through cross-coaching conversations. The second advantage is creating enhanced teamwork through facilitating a better understanding among team members and fostering a deeper integration of their activities (Agostini, Albolino, Boselli, Michelis, Paoli, & Dondi, 2003). The third advantage is ensuring improved development of people and the utilisation of their talents by building their personal capabilities (Yu, Kim, & Kim, 2004) and the last advantage is creating better employee empowerment by developing them as self-leaders (Edersheim, 2007).

Table 1. Manual work vs. knowledge work (Nickols, 2000) Manual work

Knowledge work

Materials-based

Information-based

Manual work process consists of converting materials from one form to another

Knowledge work process consists of converting information from one form to another

Tangible results

Often intangible

Works with knowledge, information

Works with and on knowledge, information

Working behaviours are public

Working behaviours are private

Visibility of working is high

Visibility of working is low

Results almost always immediate

Results are not so apparent and rarely immediate

Relatively simple matter to observe linkage between manual worker, tools or equipment being used and materials being processed

Linkage between behaviour and results not apparent

Locus of control over work with manager

Locus of control over work shifted to worker

Political and positional balance of power

Political and professional balance of power

Worker is focus of control

Work is focus of control

Compliance is measure of performance

Contribution is measure of performance

Efficiency, the ability to get things done is key measure

Effectiveness, the ability to get the right thing done is key measure

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Key Characteristics Relevant for Selecting Knowledge Management Software Tools

Knowledge Management Barriers Organizations in the new economy deal with two major management tasks given the dynamics of hyper-competition and globalization: the resulting re-invention of businesses and pressure for innovation, as well as the related re-alignment of corporate activity (Barclay & Murray, 1997; Kothuri, 2002). Further changes in this landscape that organizations need to deal with include global integration (Kotelnikov, 2001; Kothuri, 2002) and geographic distribution associated with the globalisation of markets and growth in organizational scope – organizations have to do more with less and at an accelerating pace of change (Barclay & Murray, 1997; Gordon, 1999). Obstacles for KM reveals three main groups of factors when staff attrition due to downsizing and reengineering, growing knowledge-intensity of products and services and the revolution in IT are considered (Barclay & Murray, 1997; McCullough, 2005). The first factor refers to flaws in the organizational KM process (Murray, 2004), the second factor points to misconceptions of the role of technology in the process (Moteleb & Woodman, 2007) and the third factor is a disregard of

the importance of the human factor in realising a successful knowledge managing and knowledgesharing culture (McCullough, 2005). Van der Westhuizen (2002) describes some of the opponents of successful KM as follows: •

•

The empowered middle manager. A middle manager that forms part of a cross-functional value chain running an autonomous operation as if his/her small section is the whole business, creates internal competition rather than focusing on the external competitors; The knowledge management software vendor. The software vendor becomes an enemy of knowledge management if software products are sold as if it is a solution.

In addition to obstacles of knowledge management that organizations deal with, there are also barriers to sharing knowledge as summarised in Table 2. These barriers are organization specific and include organizational hierarchy, geographical barriers, human nature and personality. Motivating users of a KM system to contribute their knowledge to the system is critical for the success of the overall KM initiative (Muller,

Table 2. Summary of knowledge management barriers Knowledge management barriers Hierarchy (Andrew & Westhuizen, 1999; Kotelnikov, 2001)

• Implicit assumption that wisdom accrues to those with the most impressive organizational titles • Inequality in status among the participants in a knowledge sharing session is a strong inhibitor for tacit knowledge sharing, especially when aggravated by different frameworks of reference

People and Human Nature (Frappaolo, 2006; Godbout, 1999; Krogh, Ichijo, & Nonaka, 2000)

• Knowledge transfer is often a case of who you know versus what you know • Sharing one’s best thinking, data, understanding and opinion with others diminishes one’s personal competitive advantage • Improving by generating new ideas continuously while getting rid of old conventional ideas is difficult due to resistance to change • Use of other people’s knowledge often presents a problem as the notion of ”it-was-not-invented-here” is difficult to break down

Geographical barriers (Kotelnikov, 2001; Marwick, 2001)

• Distance – both physical and time – is a strong inhibitor for tacit knowledge sharing.

Personality (Marwick, 2001; McCullough, 2005; Muller, Spiliopoulou, & Lenz, 2005)

• Strong preference for analysis over intuition discourages employees to offer ideas without hard facts to back it up • Penalties for failure discourage experimentation

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Key Characteristics Relevant for Selecting Knowledge Management Software Tools

Spiliopoulou, & Lenz, 2005). In view of the barriers to sharing knowledge, the motivation of people to share their knowledge remains a challenge (Frappaolo, 2006; Muller, Spiliopoulou, & Lenz, 2005). Any KM initiative in an organization must address and alleviate these barriers to optimize knowledge sharing as it forms the basis of value creation and leveraging of the intangible assets of the organization.

Knowledge management and innovation Organizations today realize that leveraging the already-accumulated corporate intellectual property is by far the lowest-cost way available to increase their competitive standing (Frappaolo, 2006; Koenig, 1998; Stewart, 1997; Tsai & Chen, 2007; Wind & Main, 1998) and to harness innovation (Krogh, Ichijo, & Nonaka, 2000; Leonard & Straus, 1997; Nonaka & Takeuchi, 1995). KM practices make bottom line differences to all types of organizations (Frappaolo, 2006) and promote the methods and technologies that facilitate the efficient creation and exchange of knowledge at an organization wide level (Krogh, Ichijo, & Nonaka, 2000; Lee, Kim, & Yu, 2001; Tsai & Chen, 2007). In such a knowledge-based economy with knowledge creation and innovation as the outcome, the infrastructure supporting KM must not be forgotten, as the components of intellectual capital, namely know-how and experience, must be channeled and made available (Frappaolo, 2006). Knowledge that is accumulated externally is shared widely internally, stored as part of the organization’s knowledge base and utilised again to develop new technologies, products and services stimulating continuous innovation that in turn leads to competitive advantage (Nonaka & Takeuchi, 1995).

Knowledge management solution selection An interpretive case study was concluded at a large telecommunication corporate within the South African context and the issues described in the previous sections of this chapter were considered. Emphasis was placed on an understanding of the key characteristics of a KM solution by exploring and describing the nature of KM. Potential research participants were selected based on their area of expertise and the knowledge work that they perform, by utilising both theoretical and convenient sampling (Whitman & Woszcynski, 2004). These criteria were then applied across different management (job grade) levels and leadership styles in the organization to obtain different perspectives from a global, as well as local context. By applying the criteria as defined in Table 3, research participants (referred to as RP) with different profiles were selected, as depicted in Table 4. In order to ensure that all research participant profile criteria were addressed, as well as different combinations of criteria, eight participants were identified. This selection ensured that different perspectives on the research questions were obtained in order to contribute to the richness of interview data. Based on the findings of this study, a list of key characteristics that a KM solution must comply with was collated. The criteria and rationale used to identify the research participants are summarized in Table 3 and both these components informed the typical profile of the research participants. The main criteria that informed the participant profile were environments where knowledge and knowledge sharing are key priorities, behaviors regarding knowledge sharing and some knowledge on human resource aspects in order to obtain input on the human-computer interface and related issues. Furthermore, research participants with a technical background, who understand systems with broad business process knowledge, as well as a

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Key Characteristics Relevant for Selecting Knowledge Management Software Tools

Table 3. Identification of research participants Criteria

Rationale

Typical participant profile

1

Technical / technology / systems background

Utilise their understanding of systems and systems architecture

Information Systems and Network Group (engineering) participants

2

Human resources / behavioural background

Obtain input in the human computer interface and any issues regarding this interface; capturing of implicit knowledge

Organizational Development (Human Resources) participants

3

Environments where knowledge and knowledge sharing are key for success; environments where these key assets leave the organization’s premises every day

Determine issues regarding knowledge sharing within the whole company and regarding key specialist skills and knowledge

System specialists, business architecture and system architecture specialists

4

Job grade

Obtain input from different levels of work and different operational levels; obtain input from different management and leadership styles

Different levels of participants with regards to job grades e.g. executives, general managers, senior managers, etc.

5

Broad business, people, process and system knowledge

Obtain input on “big picture” issues / requirements regarding business, people and knowledge management

Generalists, participants required to integrate all management aspects in order to deal with their respective departments

systems and business architecture background, informed the profile.

Knowledge Management Solution Classification In a previous section of this chapter, a classification method for KM technologies was referenced consisting of generation of knowledge, storing, codification and representation of knowledge, knowledge transformation and knowledge use and lastly, transfer, sharing, retrieval, access and searching of knowledge (Antonova, Gourova, & Nikolov, 2006). This classification was utilised to

group characteristics identified from the literature and to collate it with the characteristics obtained from the research participant interviews. Some characteristics are relevant to more than one classification dimension and in such instances a primary grouping (■) and a secondary allocation (□) have been defined.

Classification 1: Generation of Knowledge The first classification dimension is generation of knowledge which comprises of activities for

Table 4. Research participant profile Criteria (referTable 5-1(a)) 1

Technical / technology / systems background

2

Human resources / behavioural background

3

Environments where knowledge and knowledge sharing are key for success

4

Job grade (5 = executive; 4 = general manager; 3 = senior manager, 2 = team leader) in global group (G) and in local operation (O)

5

Broad business, people, process and system knowledge

26

RP1

RP2

√

√

RP3

RP4

RP5

√

√

√

RP6

RP7

√

√

RP8

√

√

√

√

√

√

√

√

√

G5

O3

G4

O3

O4

O2

O4

O5

√

√

√

√

√

Key Characteristics Relevant for Selecting Knowledge Management Software Tools

knowledge creation, acquisition and capturing as shown in Table 5. With regard to knowledge content generation, authoring, knowledge creation, knowledge objects and content validation are important. Authoring encompasses sources of explicit knowledge line documents, manuals, proposals, e-mail messages, etc., as well as implicit knowledge. Knowledge creation refers to the generation of new knowledge through thinking or reasoning and knowledge objects encompass an object of structured information, un-structured information, insight, facts, practical and theoretical experience, as well as best practice to be stored and manipulated. Content validation points to the validation and auditing of knowledge objects when they are captured and resolves data and information conflicts. Knowledge discovery allows the generation of knowledge through knowledge harvesting, content evolution and ensuring that this is made easily accessible and available via various distribution bearers. Knowledge harvesting is the process of pro-actively facilitating the harvesting and capturing of ideas. Knowledge, expertise and content

evolution refer to the creation of knowledge by combining new sources of knowledge, optimising feedback loops and by re-applying and re-creating knowledge. Data capturing tools enable the capture of knowledge and consists of characteristics such as externalization, maintenance and update, storing and content capture. This toolset ensures that knowledge in the repository is maintained by providing mechanisms to refresh data and information. Externalization refers to the connection of information source to information source and to creating interrelationships while maintenance and update ensure that knowledge objects in the KM system stays valid and recent. It includes a formal change process for captured knowledge and also provides versioning of content. Storing supports knowledge creation through exploitation, exploration and codification and content capture facilitates the capture of knowledge through mechanisms such as a keyboard, optical character recognition, bar code identification and real-time location sensors.

Table 5. Characteristics for the generation of knowledge Generation of knowledge Dimension

Knowledge content generation

Characteristic

Source Literature

Research participant interview ■

Authoring

■

Knowledge creation

■

Knowledge objects

■

Content validation Knowledge discovery

□

Knowledge harvesting

■

■

Content evolution

■

■

Various distribution bearers Externalisation

□ ■

■

□

□

Maintenance and update Data capturing tools

Storing

■

Content capture

■

Refresh data and information

■

■ primary grouping □ secondary grouping

27

Key Characteristics Relevant for Selecting Knowledge Management Software Tools

Classification 2: Storing, Codification and Representation of Knowledge The second classification dimension is storing, codification and representation of knowledge, which comprises of activities contributing to effective storage, human-readable knowledge and the organization of knowledge, as depicted in Table 6. The storing, codification and representation of knowledge classification dimension focuses on

KM processes and the quantity, quality, accessibility and representation of the acquired knowledge. Several technologies for storage consisting of several relevant characteristics have been identified in the literature and obtained from the research participant interviews. Archiving refers to archiving ability based on certain criteria and business rules specified by knowledge base administrators, while capability is the characteristics indicating the potential to influence action, pro-

Table 6. Characteristics for storing, codification and representation of knowledge Storing, codification and representation of knowledge

Source Literature

Research participant interview

Archiving

■

■

Capability

■

Dimension

Characteristic

Customization Flexibility Technologies for storage

□ □

Distributed architecture Security

■ ■

■

■

■

Hardware platform independent Storing

□

Back-up and housekeeping

■ ■ □

Content upload

□

Content validation

■ ■

■

Customization

■

Date and time stamp

■

Externalization

□

□

Flexibility

■

■

Indexing

■

■

Internalization

□

□

Application scalability

■

Knowledge gap identification

■

Appropriateness

□

Content upload

■

Taxonomy

■

■ primary grouping □ secondary grouping

28

■

Content capture

Classification

Knowledge organization

■

Application scalability Heuristic Human-readable knowledge

□

Key Characteristics Relevant for Selecting Knowledge Management Software Tools

cessing, decision-making and application. Customization points to the configuration and set up of the system reflecting the specific organization or user context (‘personalization’). Flexibility refers to the characteristic regarding the handling of various media. Security is an important characteristic that addresses physical and logical security, since knowledge is such a valuable asset, while storing in this context refers to the commitment of knowledge to the data warehouse, knowledge warehouse, lessons learnt knowledge base or the data mart. Some characteristics like application scalability, back-up and housekeeping, hardware platform independence and distributed architecture ensure that the KM application can be adapted to the size, application and hardware configuration of an organization while ensuring accessibility and proper housekeeping of the physical infrastructure. Human-readable knowledge consists of the characteristic set including heuristic and content capture, upload and validation. Heuristic means that the solution should constantly learn about its users and the knowledge it possesses as it is used. Its ability to provide a knowledge seeker with relevant knowledge should therefore improve over time. Content capture, upload and validation refer to the characteristics that ensure that knowledge is committed to the knowledge repository based on certain rules. Knowledge organization includes classification, customization, externalization, flexibility, indexing, internalization, appropriateness, taxonomy and content upload. Classification handles content management according to the context of the organization, while customization refers to the configuration and set-up of the system reflecting the specific organization or user context. Externalization refers to the connection of information source to information source and creating interrelationships, as well as the integration of organizational interdependencies. Flexibility ensures that knowledge objects of any form as well as different subjects, structures, taxonomies and media can be

included, while indexing means content management according to the context of organization. Corporate taxonomy refers to the definition of how the knowledge is stored, where internalization involves the extraction of knowledge from the external repository and subsequent filtering ensuring greater relevance and appropriateness to the knowledge seeker. Knowledge gap identification is a feature that allows a knowledge user to identify areas of the knowledge repository that is utilized significantly vs. underutilization, as well as to identify areas where more content can be uploaded and populated in the knowledge repository. Two features, namely date and time stamp and application scalability, refer to the tagging of knowledge to track ‘recency’ and the mechanism to add more knowledge areas respectively.

Classification 3: Knowledge Transformation and Knowledge Use Classification dimension three is depicted in Table 7, being knowledge transformation and knowledge use. This refers to the fact that once knowledge has been acquired it cannot be used in its raw form and must be transformed in order to become a valuable knowledge asset. Knowledge transformation ensures that the knowledge conforms to the format of the target repository and consists of two secondary allocated characteristics namely search and retrieval and access to information, encompassing the transformation of end-user collected data and information before it is committed to the knowledge repository. Knowledge reconstruction ensures that knowledge is presented in the particular reasoning method that is used by the KM system, e.g. editing into case formats to support case-based reasoning or a business intelligence dashboard. Knowledge use and retrieval encompasses expert systems, decision support systems, visualisation tools and knowledge simulation. This classification dimension consists of processes of

29

Key Characteristics Relevant for Selecting Knowledge Management Software Tools

applying expertise to knowledge, the ease of learning and teaching how to utilize the KM system through a user-friendly user interface, which is a secondary characteristic allocation in this dimension. Application includes the timely availability of organizational and individual memory and just in time learning, as well as inter-group knowledge access. Cognition refers to the connection of knowledge to process and suggestive, another secondary allocation in this dimension, proposes knowledge associations that the user is not able to make through the user interface.

Classification 4: Transfer, Sharing, Retrieval, Access & Searching of Knowledge The fourth classification dimension is transfer, sharing, retrieval, access and searching of knowledge, which comprises of knowledge access, searching, collaboration and sharing characteristics, as shown in Table 8. With regard to knowledge access and transfer, only primary allocation of characteristics and features consisting of content delivery, access to information, multi-language support, user-friendly user interface and various distribution bearers, were concluded. Access to information is facilitated via a user-friendly user

interface and the delivery of content consisting of the gathering of user-information and delivering appropriate content to meet specific user needs. Collaboration includes person to person as well as team collaboration features encompassing the support of the knowledge sharing process through a social network analysis and collaborative tools, as well as collective insights across operations and different geographical locations. Workflow enablement connects people in different ways supporting increased work performance and productivity. Knowledge sharing includes intermediation - the connection of people to people, i.e. bring together those who are looking for a certain piece of knowledge and those who are able to provide this piece of knowledge – and internalization, the connection of explicit knowledge to people or knowledge seekers. For the search and find dimension accessibility, appropriateness, context-sensitivity, heuristic, suggestive, relevance, search and retrieval, timeliness and responsiveness are important. A multilanguage user interface feature supports search and find. Accessibility provides an effective search and retrieval mechanism for locating relevant information, while appropriateness indicates the

Table 7. Characteristics for knowledge transformation and knowledge use Knowledge transformation and knowledge use Dimension Knowledge transformation Knowledge reconstruction

Knowledge use and retrieval

Characteristic Search and retrieval

Literature

Research participant interview

□

□

Access to information

□

User sensitive

■

Application

■

Cognition

■

■

Suggestive

□

□

■

Expertise applying process

■

■

System learning agility

■

User-friendly user interface

□

■ primary grouping □ secondary grouping

30

Source

Key Characteristics Relevant for Selecting Knowledge Management Software Tools

appropriateness level based on the filtering of multiple inputs for the same knowledge object. Context-sensitivity refers to the feature that the solution should be able to understand the context of the knowledge requirement and tailor responses accordingly. Heuristic indicates that as the solution responds to many requests on a particular subject, it should learn how to assist multiple users in more depth on that subject, while suggestive deduces what the knowledge seeker’s knowledge needs are. Relevance indicates the significance of knowledge objects retrieved, and search and retrieval are primarily concerned with enhancing the interface

between the user and information, knowledge sources, user-friendliness and learning agility. Timeliness and responsiveness refer to the feature that knowledge must be available whenever it is needed with almost immediate retrieval and presentation cycles.

Knowledge Management System Characteristics Application A list of KM system characteristics were extracted from the literature, obtained from research participant interviews and collated and discussed in the previous sections. The characteristics were

Table 8. Characteristics for transfer, sharing, retrieval, access and searching of knowledge Transfer, sharing, retrieval, access and searching of knowledge Dimension

Characteristic Content delivery

Knowledge access and transfer

Source Literature

Research participant interview

■

■

Access to information

■

Multi-language support

■

User-friendly user interface

■

Various distribution bearers

Person to person and team collaboration

Knowledge sharing

■

Collaboration

■

■

User sensitive

□

□

Expertise applying process

□

□

Refresh data and information

□

Workflow enabled

■

Intermediation

■

■

Internalisation

■

■

Accessibility

■

Appropriateness

Search and find

■

Context sensitivity

■

■

Heuristic

□

□

Multi-language support Suggestive

□ ■

■

Search and retrieval

■

■

Timeliness

■

■

Relevance

Responsiveness

■

■

■ primary grouping □ secondary grouping

31

Key Characteristics Relevant for Selecting Knowledge Management Software Tools

grouped using a classification mechanism for technological solutions (Antonova, Gourova, & Nikolov, 2006) according to the KM processes, and primary and secondary groupings were identified. This list of grouped and defined characteristics can be applied in two ways. The first is as a specification of the requirement of a KM system before technology is acquired. The second way is to evaluate existing technologies for compliance to KM solutions, to identify gaps in existing technologies and to assess suitability before purchasing new technology. The set of characteristics compiled based on the nature of knowledge and KM, can be used to evaluate technologies in order to establish whether it will be suitable as KM applications. Such a typical checklist is depicted in Table 9, where one dimension, namely person to person and team collaboration, with the characteristics collaboration, user-sensitivity, expertise applying process, refreshing of data and information and workflow enablement, was used as a requirement of a KM solution. Three technology solutions, namely eGain Knowledge, SharePoint and videoconferencing, were evaluated against these characteristics to establish whether it complies with requirements for a KM solution. From the result of the evaluation reflected in Table 9, a combination of eGain Knowledge and video-conferencing will comply with all the requirements listed for person to person and team collaboration, and a

combination of these two technologies can then facilitate KM according to this example. Merriam-Webster’s on-line dictionary (2007) defines a characteristic as “a distinguishing trait, quality or property” and this broad definition guided the collation of the set of characteristics shown in Table 10 as defined in previous sections of this chapter. Each characteristic is listed showing the distinguishing feature of a KM system, a description of the distinguishing characteristic and an example clarifying the characteristic where appropriate. According to Offsey (1997), KM systems share many basic features although a specific KM system would be informed by the specific organization. The list of characteristics depicted in Table 10 is such a list of common, basic features that knowledge management solutions share. These characteristics may inform the description of a typical knowledge management system architecture from a knowledge management point of view. This architecture description uses multiple, concurrent views as the initial description of a KM architecture, and such an initial architectural prototype can be evolved to become a real system through several iterations.

Future research possibilities An issue accentuated by this research is the evaluation of technologies suitable for knowledge man-

Table 9. Knowledge management system characteristics checklist (illustration only) KMS characteristic checklist Dimension

Person to person and team collaboration

32

Technology eGain Knowledge

Share-point

Video-conferencing

Collaboration

√

√

√

User sensitive

√

Characteristic

Expertise applying process

√

Refresh data and information

√

√

Workflow enabled

√

√

Key Characteristics Relevant for Selecting Knowledge Management Software Tools

Table 10. Knowledge management system characteristics KM Solution Characteristics Characteristi

Description

Example

Accessibility

Knowledge is a condition of access to information via different mechanisms (e.g. web based) and locations.

Role of IT is to provide effective search and retrieval mechanisms for locating relevant information.

Application

Timeous availability of organizational and individual memory, just in time learning. Inter-group knowledge access

Expert systems, rapid application of new knowledge through workflow systems

Appropriateness

Indicates the appropriateness level based on the filtering of multiple inputs for the same knowledge object

Prioritised search results

Archiving

Refers to archiving ability based on certain criteria specified by knowledge base administrators

Archiving of project specific information

Authoring

Encompasses knowledge objects i.e. sources of explicit (e.g. documents, manuals, proposals, email messages) or implicit knowledge (e.g. people)

Supported by standard authoring tools like word processors and database management systems (DBMS)

Capability

Knowledge is the potential to influence action, processing, decision-making, application.

Role of IT is to enhance intellectual capital by supporting development of individual and organizational competencies.

Classification

Handles content management according to context of organization

Corporate taxonomy as knowledge map supported by classifying and indexing tools

Cognition

Refers to connection of knowledge to process

Functions of systems to make decisions based on available knowledge

Collaboration

Support the knowledge sharing process through a social network analysis and collaborative tools; collective insights across operations and different geographical locations; multidimensional collaboration

Facilitate communication between users, collaboration among users and workflow management

Content capture

Enable direct capture of information via front-end or user interface

Bar-code scanning

Content delivery

Personalisation involves gathering of user-information and delivering appropriate content to meet specific user needs aligned to user profile

Electronic bulletin boards, through portals is knowledge distributed as needed by different applications

Content evolution

Knowledge creation, combining new sources of knowledge, optimize feedback loops and re-apply, re-create

Data mining and learning tools

Content upload

Upload documents in various formats into the knowledge repository

Operations manual in.pdf format

Content validation

Validation and approval of content prior to making it available generally

Site administrator or editor

Context sensitivity

Solution should be able to understand the context of the knowledge requirement and tailor response accordingly

Should be able to understand and respond differently between animal reproduction and document reproduction

Creation

Refers to generation of new knowledge through thinking or reasoning

Brainstorming

Customisation

Configuration and set-up of solution aligned to organizational processes, requirements and architecture

Branding

Date and time stamp

Refers to date and time knowledge was committed to knowledge base

-

Distributed architecture

Ensures that the knowledge management application can be adapted to the size, application and architecture configuration of an organization

-

continued on following page

33

Key Characteristics Relevant for Selecting Knowledge Management Software Tools

Table 10. Continued Externalization

Refers to the connection of information source to information source and creating interrelationships; integration of organizational interdependencies

Focuses on explicit knowledge and provides a means to capture and organise this knowledge into a knowledge repository

Flexibility

Solution should be able to handle knowledge of any form as well as different subjects, structures, taxonomies and media

If knowledge seeker wants to learn about gramophone records, it should supply knowledge on the technology as well as purchasing trends and examples of famous recordings

Hardware platform independent

Application should be scalable and applicable to oganisational specific hardware configuration

-

Heuristic

Solution should constantly learn about its users and the knowledge it possesses as it is used i.e. continually refine itself as a user’s pattern of research is tracked by the system. Its ability to provide a knowledge seeker with relevant knowledge should therefore improve over time

If the solution responds to many requests on a particular subject, it should learn how to assist multiple users in more depth on that subject

Indexing

Handles content management according to context of organization, corporate taxonomy

Corporate taxonomy as knowledge map supported by classifying and indexing tools

Intermediation

Refers to the connection of people to people i.e. bring together those who are looking for a certain piece of knowledge and those who are able to provide this piece of knowledge

Primarily positioned in the area of tacit knowledge based on its interpersonal focus

Internalisation

Refers to the connection of explicit knowledge to people or knowledge seekers

Involves extraction of knowledge from the external repository and subsequent filtering ensuring greater relevance to knowledge seeker

Knowledge gap identification

Allows knowledge user to identify areas of the knowledge repository that is utilised significantly vs. underutilisation, as well as to identify areas where more content can be uploaded and populated in the knowledge repository

-

Knowledge harvesting

Pro-active facilitation of harvesting and capturing of ideas, knowledge, expertise

Knowledge harvesting workshops and focus groups, defining tangible knowledge and capturing it

Knowledge objects

Data is facts, raw numbers. Information is processed / interpreted data. Knowledge is personalised information. Knowledge is an object of structured information, un-structured information, insight, facts, practical and theoretical experience, as well as best practice to be stored and manipulated.

KMS will not appear radically different from existing IS, but will be extended toward helping in user assimilation of information. Role of IT involves gathering, storing and transferring knowledge.

Multi-language support

Refers to user specification of preferred language for user interface

-

Process

Knowledge is a process of applying expertise.

Role of IT is to provide link among sources of knowledge to create wider breadth and depth of knowledge flows.

Refresh data and information

Update of knowledge repository as new data and information becomes available

Mobile handset manual

Responsiveness

Knowledge must be available whenever it is needed with almost immediate retrieval and presentation cycles

Different time zone applicable in global companies

Scalability

Refers to independence of solution to size of organization

Major corporate vs. small and medium enterprise (SME)

Search and Retrieval

Primarily concerned with enhancing the interface between the user and information / knowledge sources, user-friendliness and learning agility

Help users better understand the information and knowledge available by providing subject-based browsing and easy navigation

Security

Have to address physical and logical security since knowledge is such a valuable asset

Implemented using inherent mechanisms in each tool or by using specific tools in addition to the existing system

continued on following page 34

Key Characteristics Relevant for Selecting Knowledge Management Software Tools

Table 10. Continued Storing

Support knowledge creation through exploitation, exploration and codification

Technology enabled store or knowledge repository that can support less structured information

Suggestive

Solution should be able to deduce what the knowledge seeker’s knowledge needs are

Suggest knowledge associations the user is not able to do

System learning agility

Refers to how easy it is to transfer the skill of using the system to the users

-

Taxonomy

Organise knowledge repository in a defined way in order to retrieve

Medical term taxonomy

Timeliness

Knowledge is available whenever it is needed.

Eliminates time-wasting distribution of information just in case it might be required

User-friendly user interface

Refers to ease with which user interface may be applied and whether interface usage is intuitive

-

User-sensitive

Solution should be able to organise the knowledge in the way most useful to the specific knowledge seeker

Should give knowledge relevant to knowledge seeker’s current knowledge level, facilitating easier understanding

Various distribution bearers

Refers to utilization of different mechanisms for distributing knowledge or handling knowledge requests

Mobile phone Short Message Service (SMS) notification

Workflow enabled

Connects people in different ways supporting increased work performance and productivity

Notify users when any changes were made to the knowledge repository component that they are interested in

agement or the optimization of an organization’s existing technologies in achieving knowledge management objectives. These ideas could be explored further and a comprehensive checklist and process can be designed to facilitate this in organizations today. Holm, Olla et al. (2006) suggest that a process must be followed in order to create a knowledge management system architecture. The objectives and overall strategy of the knowledge management system must be compiled first after which requirements for individual groups in the organization must be established (Holm, Olla, Moura, & Warhout, 2006; Marwick, 2001). Individual knowledge management tasks can be derived from the requirements that need to be structured in such a way that it provides a successful course of action for the organization (Holm, Olla, et al., 2006; McManus, Wilson, & Snyder, 2004). The next step is to define the services, e.g. capturing tacit expertise and expert directories required as services to integrate processes, people and systems. The final step after the services architecture

has been defined is to delineate the system architecture according to a layered approach building on to already existing infrastructure and services (Holm & Olla, et al., 2006).

Conclusion Various dimensions of knowledge, namely explicit knowledge as well as implicit and tacit knowledge exist. Information becomes knowledge when it is retained as suitable representations of the relevant knowledge and when the value can be increased through analytical thought processes and by transforming knowledge into competencies, replicating know-how in the process. In order to optimally use its know-how, organizations must gain an understanding of the source and nature of knowledge in the organization to create a strategic knowledge solution for knowledge work and to foster continuous innovation. Such a strategic solution is as much about innovation, the knowledge management process, people and culture in

35

Key Characteristics Relevant for Selecting Knowledge Management Software Tools

an organization as it is about the technology that optimally supports it. An understanding of explicit, implicit and tacit knowledge in organizations, the modes and context of conversion of knowledge and the technologies used in this conversion are tactical approaches to knowledge creation. A strategic knowledge-creation solution encompasses all of these steps in one seamless and complete procedure for knowledge work, and these requirements must be considered in the design of a KM system architecture. KM tools are enhancements of existing technologies although true KM technologies differ in several aspects from traditional technologies based on the nature of knowledge and KM as discussed in the previous section. Some of these aspects include an understanding of the context of knowledge, organization of knowledge in the way most useful to the knowledge seeker, capability of the solution to constantly learn about its users and the ability to deduce what the knowledge seeker’s knowledge needs are. Other aspects include access to sources of knowledge rather than the knowledge itself, support in user assimilation of information and providing effective search and retrieval mechanisms in locating relevant information. A variety of software tools are available providing support to KM systems through four main functions, namely the association of people to people, the association of information source to information source, the association of explicit knowledge to knowledge seekers and the association of knowledge to process. KM systems share many basic features although a specific KM system would be informed by the specific organization. The set of characteristics obtained from the literature and from the research participant interviews are such a list of common, basic features that KM solutions share. This characteristic set can be utilised in two ways: the first is as a specification of the requirement of a KM system before technology is acquired, and the second way is to evaluate existing technolo-

36

gies for compliance to KM specific applications or to assess suitability before purchasing new technology. The character of KM is about people, systems and processes in building core competencies through managing knowledge reserves. It supports enhanced learning and understanding through provision of explicit and implicit knowledge and aids the assimilation of information. KM is concerned with knowledge flows and the process of creation, sharing and distributing knowledge through organised access to content. It is inherently linked to the sharing of knowledge between individuals, who are not necessarily collocated, by means of collaborative processes creating new knowledge and aiding innovation. Technology is a key enabler of KM and enhances intellectual capital by supporting the development of individual and organizational competencies. It aids the gathering, storing and transferring of knowledge by providing access to sources of knowledge and knowledge itself through user-friendly capture and effective search and retrieval mechanisms enabling continuous innovation.

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Key Characteristics Relevant for Selecting Knowledge Management Software Tools

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Key Characteristics Relevant for Selecting Knowledge Management Software Tools

Nonaka, I., & Takeuchi, H. (1995). The knowledge creating company. New York: Oxford University Press. Nonaka, I., Toyama, R., & Byosiere, P. (2001). A theory of organizational knowledge creation: Understanding the dynamic process of creating knowledge. In Dierkes, M., Antal, A. B., Child, J., & Nonaka, I. (Eds.), Handbook of organizational learning & knowledge (pp. 491–517). New York: Oxford University Press. O’Leary, D. E. (1998). Enterprise knowledge management. IEEE, March, 54-61. Offsey, S. (1997). Knowledge management: Linking people to knowledge for bottom line results. Journal of Knowledge Management, 1(2), 113–122. doi:10.1108/EUM0000000004586 Salisbury, M. W. (2003). Putting theory into practice to build knowledge management systems. Journal of Knowledge Management, 7(2), 128–141. doi:10.1108/13673270310477333 Senge, P. M. (1990). The fifth discipline: The art and practice of the learning organization. New York: Doubleday. Stewart, T. A. (1997). Intellectual capital: The new wealth of organizations. New York: Bantam Doubleday Dell. Sveiby, K. E. (1997). The new organizational wealth. San Francisco, CA: Berrett-Koehler. Tsai, C. H., & Chen, H. Y. (2007). Assessing knowledge management system success: An empirical study in Taiwan’s high-tech industry. Journal of American Academy of Business, Cambridge, 10(2).

Vandaie, R. (2007). Developing a framework to describe the interaction of social and intellectual capital in organizations. Journal of Knowledge Management Practice, 8(1). Vequist, D. G., & Teachout, M. S. (2006, May). A conceptual system approach for the relationship between collaborative knowledge management and human capital management. Collaborative Technologies and Systems, 2006, 150–156. Wessels, P. L., Grobbelaar, E., & McGee, A. (2003). Information systems in the South African business environment (2nd ed.). Durban, South Africa: LexisNexis Butterworths. Westhuizen, J. V. D. (2002). Building horizontal companies: The job KM has come to finish. Convergence, 3(3), 92–95. Whitman, M. E., & Woszcynski, A. B. (2004). The handbook of information systems research. London: Idea Group Publishing. Wilson, L. T., & Frappaolo, C. (1999). Implicit knowledge management: The new frontier of corporate capability [Electronic version]. Knowledge Harvesting Inc. Retrieved DATE, from http:// www.knowledgeharvesting.org /papers.htm Wilson, L. T., & Snyder, C. A. (1999). Knowledge management and IT: How are they related? IT Professional, 1(2), 73–75. doi:10.1109/6294.774944 Wind, J. Y., & Main, J. (1998). Driving change: How the best companies are preparing for the 21st century. London: Biddles Ltd. Yu, S. H., Kim, Y. G., & Kim, M. Y. (2004, January). Linking organizational knowledge management drivers to knowledge management performance: An exploratory study. Paper presented at the 37th Hawaii International Conference on System Science, Hawaii.

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Chapter 3

Knowledge-Based Diffusion in Practice: A Case Study Experience Hilary Berger University of Wales Institute Cardiff, UK Paul Beynon-Davies Cardiff University, UK

ABSTRACT This chapter uses a case study to consider how development methods shape information systems practice and how organizations adapt, deploy, and use such knowledge in situ. The authors explore how an information system development method (ISDM) acting as a de-contextualized “knowledge bundle” is diffused and infused within an organization through the process of contextualization. The case study looks at a regional government project responsible for the distribution of European Community (EC) monies through agricultural grants and subsidies. A new IT/IS system was designed and developed to improve the administration and management of the EC’s agricultural policy across the region. A longitudinal research project was conducted over three years and was situated within the project environment. It involved a sustained period of fieldwork (nine months of intensive observations), and data was collected through 126 semi-structured interviews, shadowing of key participants, and informal discussions and conversations. Secondary data involved an in-depth and systematic analysis of published literature, project documentation, and artifacts. The authors consider how the structure and culture of organizations affect implementation and processes of diffusion and infusion.

INTRODUCTION Our discussion is grounded in case material collected as part of an ethnographic study of a large-scale information system development project. Within this project an agile development DOI: 10.4018/978-1-60566-701-0.ch003

method promoted by external vendors initially experienced problems in deployment amongst organizational actors. Over time however the development method was adapted and used successfully by project participants. We consider how and why this transformation occurred. The main findings are that the success of ISDMs is influenced by the inherent structure and culture

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of the host organization, the actors involved and the IS development activities. The ISDM adopted was clearly tempered by the relationship between the development approach and the nature of the organization, although the attitudes and behaviors inherent in the organization hindered the diffusion process. Thus, knowledge management crucial to the adoption or rejection of technology is subjective and can be influenced by the various actors involved and the related social system and environment have a significant impact upon the unfolding ISDM process. The lesson here for knowledge based diffusion is that the presence of inherent antecedents and characteristics that present areas of risk may be mitigated through a cultural acclimatization of both the environment and of key stakeholders involved. It is important to acknowledge some of the fundamental principles and practices for knowledge diffusion relative to the work place. Knowledge diffusion is the adaptation of knowledge across a broad range of business contexts, and it allows for the communication of ideas. It is the process of knowledge diffusion that enables the right information to be disseminated to the right person at the right time. Knowledge can be diffused through a diversity of mechanisms (dialogue, discussion, manual or electronic method) and is be broadly categorized as implicit [i.e. can be readily gathered], explicit [i.e. documented] and tacit [i.e. individual ‘know-how’ gained through experience]. Decision-making requires a relevant blend of these knowledge types. However the diffusion of knowledge depends on the interpretation and communication by the various stakeholders involved. It requires understanding and effort that does not result in a loss of meaning or validation of the knowledge itself (Martinez-Brawley & Emilia, 1994). Although humans as social animals may transfer knowledge, whether new or old, through their social interactions within the work place, both IT and IS and the development of information system play an important role in the diffusion process.

Information system development methods (ISDMs) specify an approach for developing an Information Technology System within its larger Information System. Typically, an ISDM encompasses a model of the ISD process, a set of development techniques, a documentation method, a perception of how these would fit into the development process and a philosophy of assumptions about what constitutes information, an IS and the place of IS within organizations (Beynon-Davies, 2002). They play a central role in the creation, adaptation and renewal of an organizations’ IS infrastructure. Thus strategically they have a significant affect on an organizations performance (Beynon-Davies & Williams, 2003). The current turbulent business environment combined with the continual growth, the rapid change, the dynamic nature, and the increasing complexity of organizational knowledge reflects a need for new types of efficiency based on adaptability and innovativeness (Clarke & Staunton, 1989; Hollingsworth, 1991). The implementation of an ISDM can be regarded as an instance of both technological as well as an organizational innovation (Veryard 1987). The diffusion of ISDMs is a key example of knowledge-based diffusion (Newell et al., 2000; Beynon-Davies & Williams, 2003). In the knowledge-based perspective, an ISDM is considered as a de-contextualized ‘knowledge bundle’ that needs to be contextualized and unbundled such that it becomes relevant to an organizations own rationale. ISDMs are typically perceived by practitioners as packets representing ‘best practice’(Beynon-Davies & Williams, 2003). However, literature suggests that organizations rarely implement an ISDM as specified. In some cases no specific ISDM is used at all. However practitioners often utilize existing ISDMs by adapting development practices to respond to the exigencies and situational requirements of the particular organization, and the specific IS/IT development project they are concerned with (Button & Sharrock 1993; Mustonen-Ollila et al., 2004). This may prove problematic for a number of

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reasons. Indeed Jones & King (1998) put forward two instances of IS development projects where the adoption of an agile ISDM became difficult. Although adaptation was ultimately successful in one, in the other it proved counter to the culture of the host organization and failed. This chapter focuses on understanding the diffusion experience of a particular ISDM through examination of a case study. We examine the implementation of a UK government IS/IT development project where a RAD type ISDM approach was unbundled and contextualized (Beynon-Davies & Williams, 2003). We consider the relationship between the shape of the ISDM itself as a ‘knowledge bundle’ and key facets of the organization such as structure and culture (Fichman, 1992; Gharavi et al., 2004). The case study illustrates how the success of agile ISDM [RAD] development approach adopted was influenced by the interplay between the RAD development activities, and how the project was affected by both the structure and culture of the organization and the actors involved (Light & Papazafeiropoulou, 2004). We see how the situational antecedents and characteristics of the potential adopters impacted on the unfolding adoption process (Gharavi et al., 2004; Kishore & McLean, 1998). Use of the actual experiences and commentary from project participants adds meaningful insight and validity to the conclusions drawn. Thus we aim to increase the understanding of how development methods shape the community of practice in the IS development domain, and, in particular, how organizations adapt, deploy and use such knowledge in situ and consequently how technology assessment and implementation can be progressed and advanced (Fichman, 1992). The chapter begins by introducing an understanding of diffusion of innovation, knowledge based diffusion and ‘knowledge bundles’. The case study is described and illuminated through use of a knowledge-based conceptual focus. Then consideration is given to the problems of the ISDM diffusion experienced in practice. Finally we review the lessons from the

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case to enable better understanding of the practical application of ISDMs within organizations. The real-life context, experiences and commentary from individuals directly involved in the project provides meaningful insight and validity to the conclusions drawn.

THEORETICAL BACKGROUND In this section we examine the most commonly accepted definition of diffusion, the role of communications in knowledge diffusion and explore how knowledge is bundled within ISDMs. We look at the diffusion of technological innovation and discuss the adoption process in relation to an organizations culture, information infrastructures and the stakeholders involved which all influence what, and how an innovation is adopted and its eventual success or failure.

Diffusion of Innovation Everett Rogers (1983, 2003) puts forward the most commonly accepted definition of diffusion. He defined it as the process by which an innovation is communicated through certain channels over time among the community of potential users of a social system. His interpretation emphasized that communication links are essential to drive the technology adoption. The acquisition of knowledge to assimilate and deploy the technology also plays a central role. However such a broad focus on the way communication channels, and thought leadership influence adoption does not necessarily ‘illuminate the network mechanisms by which variables and constructs interact and become important during adoption’ (Hovorka & Larsen, 2006, P.160). Diffusion describes the process by which an innovation is taken on board across a population of organizations. It begins with an initial awareness of the innovation, and progresses through a series of stages to formal adoption and full-scale development. Innovation

Knowledge-Based Diffusion in Practice

can de defined as an ‘idea, practice or object that is perceived as new by an individual or other unit of adoption’ (Rogers, 2003, p.11). It is the decisions taken within a company in order to make full use of a new idea that characterizes an innovation diffusion adoption. The idea may well exist elsewhere but it must be new to, and both adopted and implemented within the organization. For large scale operations where Roger’s (2003) Diffusion of Innovation Theory is combined with understanding an organizations culture and information infrastructures, then implementation challenges can be managed (Mustonen-Ollila & Lyytinen, 2004). Beynon-Davies (2002) describes the adoption of an ISDM as a diffusion process in which development approaches are promoted, taken up, adapted and used within organizations. Diffusion theory provides a useful perspective on one of the most persistent and challenging topics in the IT field, namely how to improve technology assessment and implementation (Fichman, 1992). A review of the diffusion literature by Fichman (1999) emphasizes that it is the context in which the adoption occurs that influences what, and how innovation is adopted. Indeed the process of adoption is a function of an individual organizations’ strategic choice and encompasses both the internal (culture, structure) and external boundaries (professional, legal) that relate to the specific circumstances involved (Mustonen-Ollila et al. 2004). Fichman, (1992) suggests that at project level achieving a correct fit between the implementation characteristics and the implementation strategies can determine adoption success (1992, p.197). This view is supported by Gharavi et al., (2004) who explain further that success or failure may well depend, in part, on how well management handles the changes resulting from strategic choice and the measure of fit to the organization, as well as any new environmental conditions.

Diffusion of Technical Innovation The diffusion of technological innovations can be considered either as an overtly rational process or subject to social forces i.e. interpretive (Beynon-Davies & Williams, 2003). Interpretive approaches are concerned with the social construction of technology, and emphasize the way in which technologies are ‘configured’ during the diffusion process by the parties involved. In other words the adoption or rejection of technology is affected by the various actors, or relevant social groups involved such as professional bodies and their conflicting ideas or requirements, as well as the related social system and environment that impact upon the unfolding adoption process (Beynon-Davies & Williams, 2003; Kishore & McLean, 1998). However there is some argument that within a rational perspective emphasis is placed on the innovation itself rather than upon any social influences that impede or facilitate the process of adoption (Light & Papazafeiropoulou, 2004). Thus Rogers’ theory (1983) has been criticized for not taking into account the particularities of other actors and complex information technologies (Iacono & Kling, 1996; Lyytinen & Damsgaard, 2001). In this view, the adoption or rejection of technology is thus subject to many factors and multiple interpretations. It is the situational antecedents and characteristics of the potential adopters that impact on the diffusion of innovation in association with the specific social system and environment of the adopting organization. All of which affect the unfolding adoption process (Gharavi et al., 2004; Kishore & McLean, 1998). Rogers (2003) refers to four general approaches of the diffusion of innovations (innovation decision process theory, individual innovativeness theory, rate of adoption theory and perceived attributes theory) where five attributes are specified upon which an innovation is judged (complexity, trialability, observability, relative advantage and compatibility - Fichman 1999). Further research (Moore & Benbasat,

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1991), has expanded to include voluntariness and result demonstration (Valier et al., 2004); and intention to use put forward by Van Slyke et al., (2002). Literature uses terms such as institutionalization, routinization and incorporation to denote the final stage in the adoption of innovations. However, Kishore & McLean (1998) draw a distinction between success of adoption (when an innovation is successfully adopted and used by most/all of adoption units within a community of potential adopters) and success from adoption (realization of the potential benefits) - The former being a prerequisite of the latter. They suggest that an ‘innovation will become institutionalized, routine and incorporated in the organization when two conditions are met; firstly, when most or all of the individual members of the adopting organization utilize the innovation such that it then becomes an integral part of their regular work-routine and secondly, when the use of the innovation is fully and completely used routinely. Thus when an innovation is implemented across a large number of potential adopters it is said to be diffused, and if it is used by its adopter(s) in a full and complete manner, then it can be said to have infused. Although they do not specifically define success in their context they draw a distinction between diffusion as the breadth of use amongst the community of potential adopters, and infusion as the depth of use of the features and functions from the situational perspectives as two dimensions of the success of adoption of IS innovation in situ.

Knowledge-Based Diffusion and ‘Knowledge Bundles’ As mentioned above the acquisition of knowledge in the diffusion process plays a central role. Knowledge-based diffusion between and within organizations is where a process of complex knowledge, ideas and technical processes are bundled together and packaged in particular ways such that technology suppliers can provide

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solutions for organizational problems (Newell et al., 2000). Beynon-Davies & Williams (2003) propose that an ISDM should be treated as a ‘knowledge bundle’ because they act as devices for communication and adoption of IS development practice. Accordingly technology suppliers are able to represent such bundles as ‘best-practice’ fixes that can be contextualized across a range of contexts. However, the increasing uncertainty and dynamic nature of current business environments necessitates an ISDM that embodies the situational social structure and culture (economic, political, social and technological aspects) in the adopting organization. More specifically an environment that extends adaptability and flexibility, responds to speed and scalability (Baskerville et al., 2001, 2005), and can mitigate the risks of unexpected and perhaps unprecedented business changes (Sharifi & Zhang, 2000). However, Coughlan & Macredie (2002) point out that it is important to recognize that an ISDM may not necessarily map directly onto an organizations’ culture, rationality or the context of its specific users. Nevertheless, when knowledge arrives in an organization it has to be unbundled and contextualized relevant to the specific situation. This process may prove problematic for a number of reasons. For example, an organization may lack an established knowledge-base of people and skills for understanding and applying the ‘technology’, or difficulties of internal networking may prevent people with the requisite knowledge and skills of being involved. In such cases consultants may frequently be called on to manage the knowledge implementation process.

THE ISDM ADOPTED RAD/IAD (AGILE) The aim is to understand the way in which a commercial agile ISDM (a RAD approach called Iterative Application Development - IAD) was adopted and applied within the case study setting

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as an example of knowledge based diffusion in practice. RAD is one of a number of development methodologies that sit under the umbrella of ‘agile’ development. Others include SCRUM (Schwaber & Beedle, 2002), Extreme Programming (Avison & Fitzgerald, 2006), Dynamic System Development Methodology (DSDM, 2001). The term agile development is used to emphasize the lightweight methods and frameworks where high value is placed on human roles and relationships such as developer/client collaboration and team spirit rather than on tools and processes (Cockburn, 2002; Highsmith, 2002; Miller & Larson, 2005). History has shown how the more traditional structured methods of development such as the ‘Waterfall Model’, are no longer effective for the increasingly volatile, and dynamic nature of current business environments. Consequently, more agile approaches to development have evolved to provide a more flexible approach. RAD, an agile development method, is an iterative and incremental development approach that compresses the analysis, design, build and test phases of the development life cycle into short, iterative development cycles. The nature of the iterative development cycles means that RAD can accommodate the growing uncertainty and increasingly volatile nature of current development environments. This provides flexibility

within the development arena that is receptive to change. The key features include user-driven requirements gathering activities, fast and authoritative decision-making, and the co-operation and collaborative of all participants within the development arena. Joint Application Development (JAD) workshops are used as a mechanism for requirements gathering. This involves small integrated teams of developers, key users and other stakeholders working together within tight timescales to prioritize business needs so that delivery deadlines are met. It is this intensive user involvement, consensus and rapidity of authoritative decision-making that are crucial for the achievement of development and delivery schedules and thus critical for project success. In 1994 the DSDM (Dynamic Systems Development Method) Consortium established nine fundamental principles (see Table 1.) that are considered to represent a RAD framework. These principles were aligned to the case study setting to confirm that a RAD/IAD development approach was in fact used and the extent of its application during the 3 year project. Table 1 illustrates the degree to which the agile development approach was applied during the three year project. It clearly shows that the initial measure of ‘fit’ between the IAD approach utilized and DSDM principles within the develop-

Table 1. DSDM principles applied to the case study project DSDM PRINCIPLES Active user involvement Empowered decision-making Integrated testing during lifecycle Frequent delivery of products Iterative and incremental delivery

Case Study Setting Actual involvement during the different development stages i.e. requirements negotiation, system design, testing and so on. Implied through the iterative and flexible nature of the development approach and the development schedules.

Case Study Project Year 1

Year 2

Year 3

Yes/No

Yes

Yes

No

Yes

Yes

Yes

Yes

Yes

No

Yes/No

Yes

No

Yes/No

Yes

Yes

Yes

Yes

Requirements Catalogue

Yes

Yes

Yes

Fitness for business purpose

Consensus of stakeholders

No

Yes

Yes

Stakeholders co-operation/collaboration

Internal and external

No

No

No

Changes are reversible High-level requirements

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ment environment was lower than anticipated by both the Developers and the Clients. However, over the life of the project it is evident that the RAD/IAD type application evolved. This reflects both an acclimatization of the environment to the approach, and a familiarization of the approach within the environment. However an area of apparent difficulty refers to problems experienced with inflexibility of the EC as an external stakeholder but this is not the focus of the case study.

CASE STUDY SETTING The case study concerns the implementation of an integrated IS/IT system for a UK Regional Government Department (the Client organization). The structure and culture of the Client organization is best described as bureaucratic in that it is strongly hierarchical, highly procedural and risk averse. Work is conducted in a highly regulated and control-oriented manner, supported by clear management lines of responsibility within a ‘perceived’ blame culture (Carnell, 2003; Hofstede, 2003; Morgan, 1986; Weber, 1964). The Client organization is responsible for managing the administration and expenditure of a number of European Commission (EC) agricultural grants and subsidies. Management of the grants and subsidy schemes is highly specialized requiring specific sets of skills and domain knowledge by individual business managers adhering to EC legislation, guidance and control. Individual Business Managers were responsible for the business needs and administration of the individual grant and subsidy schemes and were deemed to have ownership of the business processes involved. Thus they were identified as the key knowledge holders and decision makers who held the necessary understanding and detailed business knowledge that was required for the Generic Process Model of the new IS/IT system. Therefore, by tradition the Business Managers had acquired an inherent sense of ownership of their

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key business processes and this generated a level of legitimate power and control borne from their actual working practices. In bureaucratic cultures ‘position power is a predominant form of power’ (Carnell 2003, P. 232) and this is relevant to the behaviour of the business managers within the development arena. This made sharing knowledge across processes and reaching consensus as to the shape of new integrated process model difficult. An internal evaluation of the existing IT system and quality of service revealed poor customer satisfaction and an increasing inability to meet the changing business requirements. The case for a new IT system was put forward and approved. The aim of the new system was to move away from the previous individually driven scheme administration procedures towards a Generic Process Model which integrated the core processes of separate agricultural schemes. Consequently project development was broken into development modules that involved process definitions relevant to the development stages of the Generic Process Model. The business case specified the need for an integrated set of re-designed and standardized processes that facilitate automated data capture, data validation and speedier payments to customers. As previously mentioned development of the new IS/IT System was outsourced to a commercial company (the Developers) who adopted their own in-house commercial agile information systems development method (ISDM) - Iterative Application Development (IAD). This has been described and justified in the above section. It is a de-contextualized ‘knowledge bundle’ which underwent a process of contextualization within the organizational setting. The Developers believed that this was particularly suited to volatile nature of situational environment. The aim was to provide early visibility of the system being developed with the potential to incorporate user feedback, and the flexibility to handle new and changing requirements. A co-operative and collaborative working milieu was anticipated.

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The project environment remained within a central location where both the project participants of the organization and the outsourced Developers were co-located on the same site for the duration of the project. A project structure was established consisting of a Senior Management Board and teams of integrated developers and organizational participants working within a pre-defined reporting structure. These teams consisted of agricultural scheme managers, business managers/ stakeholders, agricultural administrative personnel, and the outsourced IT specialists (analysts, designers, developers, testers). The application of IAD involved a sequence of short, time-boxed iterative development cycles that adhered to a ‘fit for purpose’ philosophy rather than building 100% of business needs. The Developers were provided with a Requirements Catalogue at procurement that formed the basis and scope of their planned development activities. Figure 1 presents aspects of the life-cycle model adopted in the IAD ISDM and applied within the case study project. It can be seen that IAD adopts both notions of iterative development and incremental delivery. The Developers envisaged completing the initial development work during stage 2 and then revising and modifying the system to incorporate new business needs during iterations in stages 3 and

4. However, the iterations did not occur as intended. Stage 1 was completed as planned but towards the end of stage 2 the EC imposed a directive that radically changed major requirements of the system. Thus development on the case study project was halted and a revised project emerged (i.e. version 2, year 2 onwards) to accommodate the new development direction. This case study concentrates on the initial development project over its’ three year duration. Developers imported their customary technique of JAD workshops for requirements gathering purposes. The analysis of case study materials applies perspectives from Rogers (2003) and Van de Ven (1986), which emphasize the issues of antecedents and characteristics, in order to ascertain the extent of the success of adoption and success from adoption of the ISDM in situ, more specifically, the breadth and depth of the knowledge diffusion and management (Kishore & McLean, 1998).

ANALYSIS OF THE CASE STUDY A central tenet of RAD/IAD development is the iterative process that necessitates speedy decisions to support development schedules. However ‘Bureaucracy is the enemy of speed’

Figure 1. Planned iterative development stages of the IAD development approach

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Martin (1991, P. 128), bureaucratic structures are unsuited to highly complex, dynamic business processes (Carnell 2003). Fixed procedures and static working patterns are not beneficial in a volatile and changing business environment. Thus there is recognition that complex issues cannot be brought under unilateral control (Crozier 2004). In agreement Highsmith states ‘…trying to build collaborative practices into a rigid hierarchical, control-oriented culture would be folly’ (2002, P. 128). Yet when questioned the Developers and the Client Department both believe that, although the journey has been difficult, the agile development approach has been successful for this case study project, particularly in light of its evolving and volatile nature. Success was acknowledged by Senior Management who reported that organizational effort required to process the schemes is considerably lessened, and that operating costs had been significantly reduced. Project documentation reports savings of £1.6m of benefit that represents the ‘value for money’ criterion against which the project’s success was measured (Berger, 2007). In this section the case study project is examined from two perspectives. Firstly, the exploration of the inherent antecedents and characteristics that influenced the level of diffusion of the RAD/IAD development approach adopted, and secondly examination of the key factors that influenced the success and/or failure of the ISDM adopted.

The Level of Diffusion of the ISDM A number of factors impacted significantly upon the successful diffusion of the IAD approach. Key areas of concern affecting the diffusion process were the antecedents of user involvement, requirements negotiation and decision-making procedures. Additionally, the JAD workshops used for requirements negotiation proved unexpectedly challenging. It was these factors that were influenced by the nature of the organizational culture, the inherent working patterns and characteristics of the organizational people, and

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the agile development approach. For example during requirements negotiation activities, the attitudes and behaviour of a number of the business participants involved was counter productive to the speed and consensus in the decision-making processes that is crucial for agile development. User involvement, requirements negotiation and decision-making are examined below.

User Involvement For this case study user involvement proved significant as an antecedent that influenced the application of principles specified in the IAD development method. Agile approaches necessitate regular and prolonged user involvement, user availability and high levels of commitment from those involved. It was clear that in the early stages of the development, although Business Managers were assigned to development teams it proved difficult to insulate them from their daily ‘Business as Usual’ activities. This was frustrating for the Developers who commented,‘… also you find that you do sometimes go into meetings and there are a lot of project people, a lot of development people and no business people or very few’ (Developer 17). The business people were not always available to participate when required. The Client Project Manager commented ‘it is often those people maintaining the day-to-day activities, who were the least likely to be available, but who were the key knowledge contributors. I think we had this gap … the business expertise at that time we needed them they were all off doing other things to keep the organization running. So we suffered really not having the right expertise available to us and, necessarily that expertise has to be available on demand. That is the conflict, people have their jobs to do’. It seems that during the planning stages although the need for user participation was recognized little thought had gone into the maintaining of their day to day activities during their involvement with the project.

Knowledge-Based Diffusion in Practice

The consequences of this were two-fold. Firstly, key domain knowledge was sometimes lacking in design sessions. Senior Manager (2) reflected ‘One of the things that we’ve learnt … is the need for their (Business Managers) involvement during analysis’. Secondly, user commitment was affected because people had to fulfill their daily work tasks and this impacted on the ultimate user acceptance of the system. Thus in the early stages business motivation and commitment were reduced at a time when the foundations of the project were being created. The Developers found this particularly frustrating, one commented ‘One of the things that seemed to happen before, probably nearer the start, was trying to get information (from Business Managers) by a certain deadline and if it wasn’t there by that deadline there were implications on all the other deadlines’ (Developer 11). As a consequence the planned IAD development cycles did not iterate as anticipated from the start and this affected the project’s development and progress. One view put forward by the Business Managers is that perhaps senior management placed too much faith in the anticipated expertise and experience of Developers at project inception. They were expected to understand and take on board a wide range of highly complex requirements that were characteristic of the agricultural grants and subsidy schemes. As a result the substance of the system being developed did not materialize early enough in the project. In fact one the of the Developers Managers commented ‘I remember them (Developers) saying, scratching their heads and saying that they didn’t know that these things (agricultural schemes) inter-linked … and they were scratching their heads and trying to find a technical solutions’. Similarly Business Manager (6) said ‘… you could see the faces of developers, the penny’s dropping as to how complex some of these schemes really are. The disbelief in their faces when they find out what the EC are actually obliging us to do’. It was evident in the design / development workshops that when the

key knowledge holders were not able to attend due to other commitments it proved difficult to progress development forward, and subsequently delays occurred.

Requirements Negotiation and JAD Workshops As is often the case the volatile nature of the development environment meant that it was not possible to fully define the continually changing business needs. Consequently the Client organization produced a high level ‘To Be’ vision of the business requirements that conceptualized the Generic Process Model from which a Requirements Catalogue was formulated prior to procurement and given to the outsourced developers. The Developers used JAD sessions as the mechanism for negotiating the crucial business knowledge underlying the new IT system. JAD is a systems development technique involving workshops at which both developers and business stakeholders articulate system requirements, negotiate and prioritize said requirements to develop system specifications. JAD workshops are a key aspect of the IAD development approach. Utilized throughout the development process they form a fundamental part of the iterative development cycles. The essence is to get users involved in structured meetings to make fast decisions, around which development activities evolve. The integrated team environment is aimed at breaking down communication barriers, and increasing the level of trust and confidence between developers and business people. Typically JAD sessions consist of small and diverse teams of 4-8 key people, comprising developers, users and other stakeholders empowered to make decisions for a rapidity of development within the short iterate cycles that optimize speed, unity of vision and purpose of the development process. The aim is to produce documented business requirements within a set timeframe, i.e. a time-box. Thus project control is achieved by applying fixed

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timescales to project activities and when slippage occurs, requirements are prioritized and descoped to meet the specified timeframe sacrificing functionality if necessary rather than by extending the deadlines and incurring project delays. Therefore JAD sessions need careful management to ensure that the JAD groups are productive and reach a consensus otherwise their benefit is lost. However all participants describe early JAD sessions as ‘difficult’. In the first instances, the size of these joint JAD sessions was larger than the typical 4-8 people characteristic of JAD. Large gatherings of 12+ people were assembled in an attempt to involve representatives from all business areas and this meant that the sessions were less productive than expected. Problems also experienced with the level of attendance by key business knowledge holders committed to their ‘Business as Usual’ activities compounded this situation. Additionally, participants were used to line management procedures and they felt outside their ‘comfort zones’ in such JAD sessions. They were unwilling to openly voice opinions in front of their colleagues, particularly if senior managers were present. This was problematical for the Developers who reported that ‘There’s definitely an attitude of not wanting to criticize your boss … that would just be a comment that is not of the same opinion of your boss, that seems to be perceived as a criticism so there isn’t that openness of being able to comment or speak their mind’ (Developer 6), and ‘…having 20 people to a workshop where only one person speaks and they happen to be the most senior person in the room isn’t helpful’ (Developer 1). This problem was particularly visible in development meetings where difficulty arose in the prioritizing and subsequent scheduling of scheme development work. In scheduled meetings those business managers present believed their own priorities to be paramount and although required to make decisions, did not feel able to do so if they felt it was counter to their own individual agendas. Developer (21) commented ‘… actually

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what happened was everybody was still saying ‘my priority is first, mine’s the first’, from 5-6 different Business Leaders.’ The Business Managers had their individual agendas related to their specific scheme/grant management, for example ‘I had to promote my business needs so I could do my job’ (Business Manager 7). A further difficulty related to this issue was getting agreement from the managers about what was core to development and what was secondary. There is evidence that for some managers cosmetic changes to the system were as important as getting a fundamental aspect of the system working. For example ‘I think it’s very difficult to keep the Business on track during meetings, they do tend to wander off and try to solve every single little problem’ (Developer 17). The inability to make decisions about business needs was a key concern for the Developers who needed prioritization of development work to meet time-boxed development deadlines. Consequently, little team identity, unity or spirit developed, causing lack of trust between developers and business stakeholders that was not conducive to collaborative working environment, nor the promotion of consensus in decision-making activities, as discussed in the following section.

DECISION-MAKING A key problem experienced that had a noticeable impact on development, and which can be measured in terms of missed delivery deadlines, relates directly to former, inherent bureaucratic behaviour i.e. decision-making. The fast, authoritative decision-making from development teams necessary for agile development was not achieved in many aspects of the project environment. The inherent organizational protocols embedded in the bureaucratic tradition of the Client organization were a significant antecedent shaping the way in which this particular ISDM was adapted for use within the project environment.

Knowledge-Based Diffusion in Practice

The bureaucratic nature of the development setting engendered a perceived ‘Blame Culture’ environment. This is borne out by the Client Project Manager who commented ‘The ability to make effective decisions is a cultural aspect of the Department in that people don’t particularly want to make decisions because they don’t want the blame attached to them if something goes wrong’. In other words decision-making was restrained and limited in terms of the hierarchical role structure of the organization. Business Managers were rarely prepared to make decisions they thought were outside their direct area of responsibility. This was particularly evident in situations that were outside their own domain of business experience. They preferred to refer such decisions up the chain of command. Thus they were often reluctant to make decisions in workshops. For example in meetings they would leave, defer or just avoid decisionmaking leaving issues unresolved. Comments from Developers support this ‘…they (Business Managers) were empowered to make decisions but they just couldn’t. It was very frustrating we were trying to meet deadlines but this, well it just made it very difficult’ (Developer 21). JAD sessions necessitate rapid and timely decision-making practices that facilitate ‘fit for purpose’ development to satisfy core business needs. In other words, decision-making needs to be fast, authoritative and open to compromise. This proved difficult to achieve because such characteristics ran counter to the conventions of decision-making operated by Business Managers in their normal work patterns. Although they had been especially empowered to make decisions, the Business Managers found it difficult to make such rapid decisions in the joint design sessions. For the greater part they preferred to defer decisions up the hierarchical management structure. Project commentary confirms this ‘…the difficulty in the workshops was that people didn’t want to make decisions or couldn’t’ (Business Manager 7). Such behaviour affected the ability of the Developers to meet their development and delivery schedules

and the delays experienced cascaded throughout development and ultimately influenced the success of the project. Consequently, even though the project consisted of integrated development teams there is a perception that these managers were still working with mindsets of their former scheme-specific behaviour. Empowerment is not enough, there has to be a willingness to make important critical business decisions. Empowered decision making is inhibited by hierarchical cultures. Morgan (1997) proposes that ‘the limits of ‘empowerment’ are usually quickly felt as people run into the constraints imposed by the existing hierarchy’ (P. 169). Empowerment focuses on people rather than the process. Traditionally, within bureaucratic environments it is the time-horizons of the inherent culture that determine the speed decisions can be made. In contrast, the decision-making model specified in IAD focuses on activities and objectives rather than people and behaviour. Therefore the success of ISDM adoption was influenced by the interplay between activities specified in the development method and the structure and culture of the organization. Also an ISDM can be affected negatively by the behavior and attitudes of organizational personnel so they impact upon the overall trajectory of a project (Beynon-Davies & Williams, 2003; Jones & King, 1998).

CONCLUSION The chapter has explored knowledge-based diffusion against an empirical case study in which an agile ISDM [RAD/IAD] promoted by an outsourcing IT supplier was unbundled and contextualized within an organizational context. It supplies rich detail as to what ‘un-bundling’ means in practice to better understand the practical application of ISDMs within organizations. However, literature suggests that the unbundling and contextualization of an ISDM may prove problematic in situ particularly within highly centralized and bureaucratic

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organizations. This was clearly the situation of the case study. The application of the IAD approach promoted by an outsourcing IT supplier was clearly tempered by the relationship between the development approach and the nature of the organization. Difficulties were experienced during the process of unbundling and contextualization. We can say that the ISDM innovation described in the case only partly diffused in the early stages of the project amongst project participants. Although the developers, senior managers, and to a certain extent business personnel were receptive to adopting the practices of the ISDM, in its early stages infusion of some of the features of the ISDM was less than straightforward and proved difficult for a number of Business Managers. The attitudes and behaviours characteristic of conventional practices of the organization were initially counter with those necessary for the effective application of the ISDM adopted. Thus knowledge management crucial to the adoption or rejection of technology is subjective and can be influenced by actors and their contrary ideas or requirements. Additionally, the related social system and environment have a significant impact upon the unfolding adoption process. We examined how the success of ISDM adopted was influenced by the inherent structure and culture of the host organization, the actors involved and the IAD development activities. Key areas of concern were the difficulties associated with the management of user involvement and decision-making processes necessary for the effective operation of JAD workshops. We can emphasize how the organization’s culture influenced people’s behaviour. The inherent organizational protocols played a significant role in determining the working patterns, behaviour and attitudes of those involved. Problems experienced reflect the nature of the traditional hierarchicaldriven business policies and procedures governed through knowledge management and management hierarchy. As a consequence the climate of trust, co-operation, collaborative and flexible working

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practices necessary for consensus and authoritative fast decision-making was not present and this had significant practical implications. It is these elements that are crucial if iterative development of knowledge based systems is to succeed. This has implications for practice because governmental environments that tend to be bureaucratic in nature exhibit similar characteristics in terms of their structure and culture (Clegg et al. 1997). However, any development project, particularly of the scale of the one described, evolves in terms of its interaction with the holding organization. There is evidence in our case of an evolving rate of diffusion and infusion of aspects of the ISDM which initially proved problematic. As the project matured and development evolved through the subsequent second project environment (year 3 onwards), Business Managers appear to have begun to infuse and accept some of the initial, problematic elements of the ISDM due to, we believe, a familiarization with the development approach. Indeed interviewees report a greater acceptance of the need for collaboration and acknowledgement to practice effective decisionmaking. A senior manager reflects ‘I think over the last 12 months it has become more effective but as usual you learn as you are going along’ (Senior Manager 13). We should hence not assume that the diffusion and infusion of an ISDM is a necessarily linear, all-or-nothing process. The difficulty in making the transition from the previous bureaucratic behaviour of referring decision-making up the line management hierarchy is attributed to the bureaucratic nature typical of most Government departments. This may partly explain some of the difficulties experienced with IAD or agile practices more generally. However, in a large-scale project environment there is scope for a wider range of stakeholder involvement and greater time for practices to diffuse and infuse within the holding organization. A lesson here for knowledge based diffusion is that the presence of inherent antecedents and characteristics that present areas

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of risk may be mitigated through a cultural acclimatization of both the environment and of key stakeholders involved. The authors would like to thank the outsourced developers and all organizational participants of the IS development project for their time and contributions. The authors would like to thank the outsourced developers and all organizational participants of the IS development project for their time and contributions. We would also like to thank the National Assembly of Wales for funding this research.

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Button, G., & Sharrock, W. (Eds.). (1993). Practices in the work of ordering software development: The discourse of negotiation (pp. 159–180). Oxford: Pergamon. Carnell, C. A. (2003). Managing change in organisations. London, UK: Prentice Hall. Clarke, P. A., & Staunton, S. (1989). Innovation in technology and organization. London: Routledge. Clegg, C., Aztell, C., Damodarant, L., Farbey, B., Hull, R., & Lloyd-Jones, R.,…. & Tomlinson, C. (1997). Information technology: A study of performance and the role of human and organization factors. Ergonomic, 40(9), 851–871. doi:10.1080/001401397187694 Cockburn, A. (2002). Agile software development. Boston: Addison Wesley.

Baskerville, R., & Pries-Heje, J. (2001). A multiple-theory analysis of a diffusion of information technology case. Information Systems Journal, 11, 181–212. doi:10.1046/j.1365-2575.2001.00106.x

Coughlan, J., & Macredie, R. D. (2002). Effective communications in requirements elicitation: A comparison of methodologies. Requirements Engineering, 7(2), 47–60. doi:10.1007/s007660200004

Baskerville, R., Pries-Heje, J., & Ramesh, B. (2007). The enduring contradictions of new software development approaches: A response to ‘Persistent Problems and Practices in ISD.’. Information Systems Journal, 17, 241–245. doi:10.1111/j.1365-2575.2007.00236.x

Crozier, M. (2004, September). Theatres of innovation: Political communication and contemporary public policy. Paper presented at the Australasian Political Studies Association Conference, (ACIS), Adelaide.

Berger, H. (2007). Agile Development in a bureaucratic arena: A case study experience. International Journal of Information Management, 27(6), 386–396. doi:10.1016/j.ijinfomgt.2007.08.009 Beynon-Davies, P. (2002). Information systems: An introduction to informatics in organisations. Basingstoke, UK: Palgrave. Beynon-Davies, P., & Williams, M. D. (2003). The diffusion of information systems development methods. The Journal of Strategic Information Systems, 12, 29–46. doi:10.1016/S09638687(02)00033-1

DSDM Consortium. (2001). Dynamic systems development method. Retrieved September 25, 2001, from http://www.dsdmna.org/en/about / principles.asp Fichman, R. G. (1992). Information technology diffusion: A review of empirical research. In proceedings of the 13th International Conference on Information Systems (ICIS), Dallas, TX (pp. 195-206). Atlanta, GA: AIS. Fichman, R. G., & Kemerer, C. F. (1999). The illusory diffusion of innovation: An examination of assimilation gaps. Information Systems Research, 10(3). doi:10.1287/isre.10.3.255

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Gharavi, H. Love, P.E., & Sor, R. M. (2004). Diffusion of innovation: An institutional perspective. Presented at the Australasian Conference on Information Systems (ACIS), Tasmania. Highsmith, J. (2002). Agile software development ecosystems. London: Addison Wesley. Hofstede, G. (2003). Cultures and organisations: Software of the Mind. London: Profile Books Ltd. Hollingsworth, J. R. (Ed.). (1991). The logic of coordinating American manufacturing sectors (pp. 35–74). New York: Cambridge University Press. Hovorka, D. S., & Larsen, K. R. (2006). Enabling agile adoption practices through network organizations. European Journal of Information Systems, 15, 159–168. doi:10.1057/palgrave.ejis.3000606 Iacono, S., & Kling, R. (Eds.). (1996). Computerization movements and tales of technological utopianism, computerization and controversy: Value conflicts and social choices. Chichester, UK: Wiley. Jones, T., & King, S. F. (1998). Flexible systems for changing organizations: Implementing RAD. European Journal of Information Systems, 7, 61–73. doi:10.1057/palgrave.ejis.3000289 Kishore, R., & McLean, E. R. (1998). Diffusion and infusion: Two dimensions of ‘success of adoption’ of IS innovations. In Proceedings of the 4th Americas Conference of the Association on Information Systems (AMCIS), Baltimore, MD (pp. 731-733). Atlanta, GA: AIS. Light, B., & Papazafeiropoulou, A. (2004). Reasons behind ERP package adoption: A diffusion of innovations perspective. In Proceedings of European Conference of Information Systems (ECIS), Turku, Finland. Retrieved from http:// www.informatik.uni-trier.de/~ley/db/conf/ecis/ ecis2004.html

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Lyytinen, K. J., & Damsgaard, J. (2001). What’s wrong with the diffusion of innovation theory. In Ardis, M., & Marcolin, B. (Eds.), Diffusing software product and process innovation. Dordrecht, Netherlands: Kluwer. Martinez-Brawley, E. E., & Emilia, E. (1994). Retrieved September 25, 2008, from www.eric.ed.gov/ ERICWebPortal/ recordDetail?accno=ED390619 Miller, K. W., & Larson, D. K. (2005). Agile software development: Human values and culture. IEEE Technology and Society, 24(4), 36–47. doi:10.1109/MTAS.2005.1563500 Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2(3), 192–222. doi:10.1287/isre.2.3.192 Morgan, G. (1986). Images of organization. London: Sage Publications. Mustonen-Ollila, E., & Lyytinen, K. J. (2004). How organizations adopt information systems process innovations: A longitudinal analysis. European Journal of Information Systems, 13, 35–51. doi:10.1057/palgrave.ejis.3000467 Newell, S., Swan, J. A., & Gallier, R. D. (2000). A knowledge-focused perspective on the diffusion and adaptation of complex information technologies. Information Systems Journal, 10(1), 239–259. doi:10.1046/j.1365-2575.2000.00079.x Rogers, E. M. (2003). Diffusion of Innovations (5th ed.). New York: The Free Press. Schwaber, K., & Beedle, M. (2002). Agile software development with SCRUM. Hemel Hempstead, UK: Prentice Hall. Sharifi, H., & Zhang, Z. (2000). A methodology for achieving agility in manufacturing organizations. International Journal of Operations & Production Management, 20(34), 96–512.

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Valier, F. M., McCarthy, R. V., Aronson, J. E., & O’Neill, H. (2004). The province of diffusion of innovations: Usable social theory for information systems research. In Proceedings of Americas Conference on Information Systems, New York (pp.1441-1445). Atlanta, GA: AIS. Van de Ven, A. H. (1986). Central problems in the management of innovation. Management Science, 32, 590–607. doi:10.1287/mnsc.32.5.590

Van Slyke, C., Lou, H., & Day, J. (2002). The impact of perceived innovation characteristics on intention to use groupware. Information Resources Management Journal, 15(1), 5–12. Veryard, R. (1987). Implementing a methodology. Information and Software Technology, 29(9), 469–474. doi:10.1016/0950-5849(87)90003-6 Weber, M. (1964). The theory of social and economic organization. New York: The Free Press.

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Chapter 4

Deploying Knowledge Management in R&D Workspaces Won-Chen Chang National Cheng Kung University, Taiwan Sheng-Tung Li National Cheng Kung University, Taiwan

ABSTRACT The active and effective management of valuable knowledge is widely believed to be a core competency for solidifying the competitive advantage of an organization. Whether knowledge management (KM) is a new idea or just a recycled concept per se both managerial and academic campuses have sought a vast array of KM strategies, solutions, frameworks, processes, barriers and enablers, IT tools and measurements over the past decade. Although there are many KM studies for both public and private sectors, most of them focus on the practice of international companies and western experiences, relatively few cases are reported on KM deployment and implementation in the Chinese community, especially for knowledge intensive research and development (R&D) institutes whose missions are to serve traditional industries. To reveal some of the accomplishments gained in the Asia-Pacific region, this chapter presents and discusses the lessons learned from a particular case study in fostering the KM initiative and system in a research-oriented institute serving the metal industry.

KNOWLEDGE ASSETS IN R&DORIENTED ORGANIZATIONS R&D plays a fundamental role in the competitiveness of technological innovation. These R&D processes can primarily be seen as information transformation processes, transforming information about client orders, market demand and DOI: 10.4018/978-1-60566-701-0.ch004

technological advancement into product and process designs (Drongelen et al., 1996). In the case of R&D organizations, knowledge workers synthesize tangible and intangible resources to create value-added knowledge-based products as their major outputs. These knowledge assets are indexed in terms of consultancy, innovative products, expert reports, and intellectual properties. The majority of professional knowledge and expertise frequently originates in the context and

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Deploying Knowledge Management in R&D Workspaces

activity of research projects and industrial services implementations. In a project-based engineering firm, there are three main aspects of knowledge: technical, entrepreneurial and project management knowledge (van Donk & Riezebos, 2005). From another point of view, types of project knowledge can be viewed as: knowledge about projects, in projects and from projects (Damm & Schindler, 2002). Knowledge for R&D work exists in various forms and sources as indicated in Table 1. The ability to manipulate R&D knowledge highly depends on the type of knowledge source and form. For instance, internal-explicit knowledge is easy to collect and manage, while external-tacit knowledge requires a lot of efforts to acquire and maintain. Accordingly, when an organization wishes to incorporate KM, the first step is to implement knowledge audit to identify the sources of R&D knowledge and decide the management priority. In practice, Paraponaris (2003) further indicates that, for R&D process, knowledge could be viewed as a stock of regular object inventories to explore the potential for innovation. Nevertheless,

the transfer of implicit knowledge among individuals is another story. Knowledge acquisition is not a matter of ‘copy, paste and save’ between individuals or teams with the knowledge to those without it (Sapsed et al., 2000). Knowledge sharing networks, (i.e. communities of practice) provide a common purpose and effective links allow for repeated interactions that create knowledge spillovers based on shared knowledge creation. Moreover, accumulation of personal knowledge in each individual is not totally equivalent to accumulation of embedded knowledge in the organization. In other words, the implicit characteristics for tacit knowledge and collective knowledge in know-how and business service experiences, and a culture that is unwilling to share within an organization, make knowledge transformation, accumulation and sharing difficult (Szulanski, 1996). Milliou examined the impact of R&D information on innovation incentives and welfare (Milliou, 2004). If members in such research teams can agilely and correctly acquire and assimilate organizational knowledge assets that are already

Table 1. Forms and sources of R&D knowledge (Adapted from Parikh (2001) Modified with additions by Chang (2008)) Internal

External

Tacit

Experiences/judgments* Insights/intuitions/beliefs* Educational background Cultural background Intra-organizational relationships Unwritten rules of thumb History and stories Master technicians Experts/researchers

Industry experts/consultants Industry best practices Communities of professions* Inter-organizational relationships Consumers Academic researchers Informal social networks* Other research organizations

Explicit

Organizational databases Information systems File systems Standard operating procedures Discussion minutes/trails Design and prototypes Product manuals Own patents Training courses* Machine/equipment* Manufacturing processes*

Trade publications External databases Benchmarking matrices Others’ patents Competitors’ products and manuals Academics research articles Specifications and design manuals Seminars and conferences* Standards Regulatory guidelines and governmental policies*

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known to other organizational units, they can accelerate tasks and improve the quality of outputs. For example, Booz Allen & Hamilton employed a ‘knowledge engine’ to better distill and deploy the firm’s ideas and learning (Burgelman, et al., 2004). There are still many immeasurable parts of knowledge assets, some R&D organizations have tried to issue intellectual capital reports in terms of human capital, structural capital and relational capital (Leitner & Warden, 2004). These organizations found, during the measurement, that they learned more about their knowledge production processes and explored some productive uses of knowledge-based assets.

KM IN R&D-ORIENTED ORGANIZATIONS In the context of R&D, the barriers to promoting institutional knowledge cycle are mainly decided by the choices made in the organization structure and R&D strategy, which in turn depend on the business strategy and other functional strategies within the organization (Drongelen, et al., 1996). Common KM barriers and enablers found in R&D-oriented organizations are reviewed in Table 2. Most of the barriers and enablers are similar with those found in many companies. In brief, issues on culture, infrastructure and technology are the three main concerns for implementing KM in R&D-oriented organizations. In the past decade, many R&D-oriented organizations have paid great attention to incorporate KM into their strategic management and routine practices. From review of literature, we summarize some of the KM efforts including initiatives and strategies in R&D-oriented organizations, as shown in Table 3. The focus of KM varies with the characteristics of R&D-oriented organizations. For instance, some organizations are concerned about knowledge integration and management with external partners, while others might be more interested in managing R&D outputs of projects and securing the pass-on of expertise knowledge. 58

CASE BACKGROUND In recent years R&D organizations have encountered many intensified challenges including increase of domain complexity, evolution of technology, competition for research funds and management of new operation modes, i.e. serving the needs of industry more effectively so as to raise private funds from industries. Thus, competition through knowledge exploitation, transfer and leverage are reaching a new paradigm in this sector. In reaction to these structural changes, some government sponsored non-profit research-oriented organizations in Taiwan have voluntarily begun to incorporate new instruments for managing knowledge-related activities more effectively and efficiently. These R&D organizations, including Industrial Technology Research Institute (ITRI), Taiwan Textile Research Institute (TTRI), Institute for Information (III), and Metal Industries Research & Development Centre (MIRDC) provide diversified professional R&D and information services for the government and local industry. In addition, they are paying to improve the production, productivity and performance of knowledge and its management. In view of the history of KM development within these R&D organizations, the first KM initiative was introduced into a joint industry market research project - Industrial Technology and Information Services (ITIS). This project was sponsored by the Ministry of Economic Affairs and has been operated jointly by eighteen R&D organizations since the early 90s. In the beginning, a Web-based knowledge bank was formed in 1999 to effectively manage industrial information collected by these eighteen institutes and later, they tried to build a cross-institute KM framework facilitated by a famous international consulting firm. However, the project-based KM idea was not successfully implemented due to two obstacles: first, the KM framework proposed by the international consulting firm was based on their own KM experiences acquired from a single organization

Deploying Knowledge Management in R&D Workspaces

and the characteristics of group dynamics within a single organization and cross-institutes were different in terms of power structure, culture and fellowship. Second, it was hard for the project members to get equivalent support from their mother institutes for contributing their time and spending as the contributions these project members made to the cross-institute KM community could not be acknowledged to the full extent by an individual institute. In other words, the disposition of each institute toward the KM initiatives varied, therefore the recognition of a psychology contract, i.e. the expectations that members have about each

other in return, among participants was in vain as no one was willing to actively participate and take it seriously. Undoubtedly, a KM community that lacks comparable knowledge contributions from each party is destined to be a failure, but in spite of the downfall, this pilot plan did ignite sparks for these institutes to take further KM action. For instance, in 2000, ITRI began its KM journey by forming a KM interest group and in the next year kicked off an organizational-wide KM scheme focused on constructing a ‘competency network.’ Six areas including white light LED, nanotechnology, water world, mobile information appliances,

Table 2. Common KM barriers and enablers found in R&D-oriented organizations (Source Chang 2008) R&D oriented cases

KM barriers

KM enablers or success factors

British Telecom Labs (Warren & Graham, 2000)

Not mentioned

Building the network

KM in research and development (Armbrecht, et al., 2001)

Not mentioned

Culture, infrastructure, technology

R&D cooperation project of Nokia, Airbus France and Airbus Germany (Barnard & Poyry, 2004)

Lack of good tools Differences between groups Changes in economic and business situation making people unwilling to share Lack of personal contacts Lack of time& high workloads

Adequate KM tools and procedures People involved making a conscious effort for sharing The presence of people who facilitate sharing and become personally acquainted with other groups, thus enhancing trust.

Role of tacit knowledge in innovation processes of small technology companies (Koskinen & Vanharanta, 2002)

Bureaucracy

Coaching type of leadership Engaging technology companies and their customers in interactive learning and effective sharing of tacit knowledge

A survey focused on strategies and tactical moves employed by CKOs from 22 companies in the Geneva Knowledge Forum (Raub & Wittich, 2004)

Unable to target key actors Using the hierarchy to put pressure on resistors Overemphasizing IT aspects and putting IT in the driver’s seat Fostering knowledge network Purchasing ready-made KM solutions in the market Excessive reliance on outside experts for KM implementation Delivering a purpose Presenting KM as a management fashion Keeping KM vague or avoiding KM terminology together

Targeting key actors Aligning KM with contributions from key functional units Gaining support from line managers and top management Fostering knowledge network Identifying and leveraging existing KM initiatives Establishing networks with outside KM practitioners Delivering a purposeful message Adapting the message to different target groups Focusing on business value Communicating KM ‘quick wins’

French national project for the automotive industry (Barthès & Tacla, 2002)

In R&D projects, there is simply no extra time to organize a complete knowledge capitalizing cycle.

Not mentioned

Enhancing knowledge sharing – case studies of nine companies in Taiwan (Hsu, 2006)

Not mentioned

CEO’s commitment on: Continuous learning initiatives Performance management systems which motivate employee knowledge sharing Information disclosure to create climate of sharing

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innovative R&D and human resources were chosen to pilot run the six communities of practice. In 2003, MIRDC also pioneered its KM journey by initiating a KM plan for a limited number of project members and a few months later expanding to an organization-wide KM portal. To establish a set of effective practices for managing workspace knowledge, MIRDC tackled its KM challenges by activating changes in organizational learning and sharing, and introducing an integrated KM portal to enhance productivity of knowledge work (Chang et al., 2004). Founded in 1963 by the United Nations and transferred to the Taiwanese government in 1968, MIRDC has positioned itself for researching and developing the leading technology of the metal industry and related industries in Taiwan. After 42 years of development, MIRDC has blossomed into the third largest research organization supervised by the Department of Industrial Technology,

Ministry of Economic Affairs of Taiwan. The Centre has devoted itself to innovative R&D and has been granted nearly 300 patents, it carries out hundreds of R&D projects and offers an array of managerial and technological services to the government and industrial communities. MIRDC has now positioned itself on firm ground for the transition of technology from basic research to purposeful applications and in terms of scale, it can be rated as mid-scale among worldwide R&D organizations. The annual turnover was around US$26-28 million from 2000 to 2005 and has increased to US$40 million in 2007. The total number of employees in 2007 was around 572, 29 with PhD qualifications and 275 with Master Degrees, the majority of them well educated and with an average experience of 11 years, forming a large talent pool for R&D services to Taiwanese industries.

Table 3. A review of KM initiative and strategies adopted by R&D organizations (Source Chang 2008) Cases

KM initiatives

Bridge people with people

Bridge people with system

British Telecom Labs (Warren & Graham, 2000)

Generate and disseminate technology trend and comparative R&D information

Regular research audits in groups allow cross-fertilization of tacit knowledge

Build a central database and intranetbased directory Use groupware Adopt automated patent classifier

R&D cooperation project of Nokia, Airbus France and Airbus Germany (Barnard & Pöyry, 2004)

Develop procedures and tools for facilitating and improving KM processes among partners

Not mentioned

Build the Knowledge portal, i.e. WISE Easy access to all documents, tools and people Provide knowledge object (KO) annotation to enhance tacit knowledge transfer and knowledge generation trace

Samsung Advanced Institute of Technology (Sohn, 2004)

Develop and secure Samsung’s leadingedge position in key technology areas

A careful combination of a KM system and reinforcement mechanism Adopt ‘knowledge intensive (KI) staff meetings’ integrated into formal problem solving process to form extensive social network across technology boundaries

‘Praise Ground’ website that address activities or behavior of knowledge sharing, creation, collaboration and problem solving

French national project for the automotive industry (Barthès & Tacla, 2002)

Develop a means of managing the knowledge created in complex high priority urgent R&D projects efficiently.

Not mentioned

Groupware and agent are integrated to offer an agent-supported portal for collaborative R&D work.

Enhancing knowledge sharing – case studies of nine companies in Taiwan (Hsu, 2006)

Not mentioned

Tutor and lecturer development program Workshop and forum Knowledge modularization

Not mentioned

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Prior to the implementation of the KM plan, very few efforts were undertaken to identify, capture and transfer the knowledge assets within MIRDC. Workspace knowledge, including R&D expertise, domain know-how, best practices, project documents, administration and customer information, was not systematically collected and organized as all the information was scattered in various departments, different directories and separate databases. As a result, engineers often complained about the difficulties in acquiring and accessing necessary information and knowledge that existed in the centre, and were hard to find. In order to be innovative in R&D performance, the knowledge utilization activities should not only apply existing knowledge but should also transform it into new and creative knowledge, which provides competitive advantage. The centre had to discover a routine practice to enhance its knowledge cycle, driving the initiatives of KM in MIRDC.

FIVE STAGE KM APPROACH ADOPTED BY MIRDC Previous studies indicated that IT solutions can efficiently facilitate explicit knowledge access and utilization, while implicit knowledge sharing and transformation is decided by people-to-

people interactions (Gold, et al., 2001; Huber, 1991). From our survey, MIRDC believes that the organizational management strategy, application of IT solutions, cultivation of an innovative and sharing culture, and gaining support at all levels are the major enablers of KM deployment as raised in knowledge auditing. Undoubtedly, this means that KM manipulation is a sophisticated and multi-disciplinary task. Building internal alliances can pave the way to initialize a KM plan. Thus, MIRDC organized a cross-departmental task force consisting of supervisors, IT personnel and human resource managers to handle KM planning, coordination and implementation. In addition, in our review, MIRDC was clearly aware that if it wished to succeed in promoting KM, a cohesive and evidence-based deployment framework had to be developed beforehand. Therefore, through a synthesis of previous concepts and best practices (Holsapple & Joshi, 1999; Rubenstein-Montano, 2001; Maier and Remus, 2003), MIRDC proposed a KM approach, which is further summarized as a five-stage KM approach including auditing, planning, execution, evaluation and reinforcement, shown in Figure 1. MIRDC attempted to synergize previous frameworks and developed a more exhaustive KM deployment framework that not only highlights KM key concerns and drivers but also includes detailed assignments, check points of

Figure 1. Five-stage KM approach and critical tasks involved (Source Chang (2008))

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quality control/quality assurance and resource allocation.

KM AUDITING Varied modes of operation within organizations require different types of knowledge assets as well as KM systems. This activity is the groundwork of KM, which aims to clarify and examine the basic questions of KM initiatives. Therefore, the major work of the KM auditing stage is to conduct a status quo survey across various workspaces in the centre. The survey conducted in 2003 primarily focused on the identification of needed knowledge assets, the current KM practices in each department, what gaps existed in KM, and consensus of KM vision and mission throughout the organization. Once these knowledge assets were identified and represented in the form of an organizational K-map, managers were provided with a whole picture of knowledge resources and knowledge gaps. The KM task force conducted two internal studies within three months in 2003, a questionnaire and a focus study group. Firstly, an open-ended questionnaire was sent to all employees to elicit comments and information about the current KM

practice, type of knowledge assets, and expectations of KM objectives. Sixty-one replies were collected. A summary response analysis report regarding overall KM needs was prepared as background information for the focus group study, which was held five times and eight to ten crossdepartment employees were invited each time. Through KM auditing, MIRDC found that the intrinsic KM needs come from the occurrence of a ‘knowledge vacuum’, one of the greatest fears of organizational management, which refers to the losing of proper possession or the inability of unified government over organizational knowledge. After thoroughly examining the management status of explicit and tacit knowledge, MIRDC found that a crisis of knowledge vacuum did exist for both types of knowledge that could be worsened if no actions were taken. Table 4 summarizes the KM gaps/barriers found in knowledge audits. First of all, the management of explicit knowledge was problematic as the majority of it (see Table 5) was dispersed and fragmented, due to the inherent individualism of the various departments. This explicit knowledge could be treated as a series of knowledge objects (KOs) and was supposed to be easy to handle but did not receive enough attention to be effectively managed as assets.

Table 4. Summary of KM gaps/barriers found in knowledge audits (Source Chang (2008)) Aspects

KM gaps/barriers

General issues

Lack of KM strategies and their alignment with organizational strategies Management support is not strong Researchers lack time and have high workloads, thus inhibiting their participation in KM activities KM activities are not tightly integrated with workflow The necessity awareness of KM among employees is poor

Explicit knowledge

Knowledge is dispersed and management of explicit knowledge is problematic and unsystematic Hard to reach distributed databases and lack of KM portal Lack of on-line collaboration platform Search efficiency for explicit knowledge objects is low Management of PKO, such as MS PowerPoint files, is insufficient. No clear regulations and procedures guiding the production, sharing and accumulation of explicit knowledge\

Implicit knowledge

Rigidity of bureaucratic structure categorized by technology fields and each department operated as profit-center Cross department R&D networking is inadequate so that new knowledge creation, i.e. innovation, is slow Internal expert skills directory is not available Lack of mechanisms and incentives to encourage implicit knowledge sharing

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KM PLANNING This activity defines the objectives, scope, strategies and approaches of the KM plan. Prior to implementing KM, one elementary issue is how to integrate it with the organization’s vision and mission. For the sake of attaining KM goals and closing the KM gaps, two simple and straightforward strategies were formulated; ‘bridging people with systems’ and ‘bridging people with people’ as shown in Table 6. By applying the ‘Plan-Do-Check-Action Cycle’ the KM deployment framework was thoroughly configured in the planning stage to ensure every KM effort would be implemented and coordinated in an appropriate way.

KM EXECUTION This activity represents an effort to realize the goals of KM in routine practice. Even though

knowledge sharing and information exchange is part of the Taiwanese culture and is a strong advantage over U.S. organizations (Stankosky, 2005), knowledge sharing and transfer does not spontaneously happen in a R&D organization as employee motivation for knowledge exchange is certainly influenced by national cultural norms. Hsu (2006) indicated that Chinese people tend to divide their ‘gainsharing’ circle into an ‘ingroup’ and an ‘outgroup’, and only the ‘ingroup’ can effortlessly enjoy knowledge sharing. Furthermore, influenced by Chinese culture, employees in Taiwan are often conservative when expressing their opinions because they were taught to respect corporate power structures and behavioral norms, and that managers’ knowledge is superior to that of employees (Pun K. et al., 2000). As a result, challenges like relation circles, evaluation anxiety and power barriers need to be faced when organizations in Taiwan wish to encourage knowledge sharing in KM practices (Hsu, 2006). KM is a series of long-term systematic processes

Table 5. Various forms of KO identified from knowledge auditing (Source Chang & Li (2007)) Origins

Forms of knowledge objects

R&D works

Patent documents, research logs, research reports, manuals, experiment records, engineering drawings, technical specifications and project management notes

Education and training

Training reports, best practices, lessons learned, policy and strategy declarations, norms and rules

Customer account

Customer profiles, customer contact records, proposals, bid files, market analysis reports and marketing plans

Table 6. KM visions, strategies and goals of MIRDC (Source Chang & Li (2007)) KM visions Enriched knowledge, diversified learning, interesting work, concrete impartation, faster processing, and quality output. KM strategies

KM goals

Bridging people with systems

1. A centralized knowledge repository, mainly powered by Lotus Notes, is used to store all knowledge objects as identified in knowledge auditing. 2. Establish a KM portal to connect individual information systems which are dispersed at different sites.

Bridging people with people

1. Develop industry-focused communities of practice to call for a horizontal network platform that links employees to enable knowledge sharing. 2. Link KM with collaborative R&D process management.

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and to combine them with synergies, people and tools, MIRDC established a sophisticated KM deployment framework as shown in Figure 2. In order to tackle the knowledge vacuum existent in MIRDC and avoid the KM gap, this framework covers the assignments, steps and control points for systematically managing KM progress by developing a series of ‘small steps’ within the five stages to deliberately manage the KM plan. For example, the first step of KM planning is to propose the KM plan to management in order to gain support and resources. The major tasks in the KM execution stage can be divided into two groups. For the KM task force, their job mainly focused on developing the KM portal, facilitating organizational change and managing the KM progress. For example, to facilitate organizational change, the following actions were undertaken: formulate the KO output and submission procedures in a rules and regulations system, arrange five training seminars to

present the KM concept and KM portal, and hold activities to promote the KM portal. The primary issues to be communicated in the trainings were: the kind of KM that was going to be carried out, the functionalities of the KM portal and how personal benefit associations were made. Parallel to the task force actions, the knowledge workers in R&D teams and administrative units were obliged to submit KO in response to the rules and regulations. This was particularly effective since most R&D projects were sponsored by the government and all R&D outputs had to be as requested in the contracts. All the R&D outputs and related reports were recorded as part of the employee’s evaluation system. Nevertheless, MIRDC generated the administration databases for human affairs, accounting, procurement and internal news postings so that employees were free to access those databases to search for useful information. In the planning stage the management also found that horizontal knowledge exchange and

Figure 2. KM Deployment Framework in MIRDC (Source Chang & Li (2007))

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communications for creating a total solution to the clients were inadequate and inefficient due to a rigid hierarchical structure with the technology classifications. To initiate lateral communications and knowledge sharing across disciplines, MIRDC established six industry-focused communities of practice in 2005 and 2006 respectively. These areas were precision and micro parts/assembly, moulds and dies, transportation vehicle parts/assembly, flat panel display, motor and export-oriented metal fabricated products. However these communities did not work well and additional actions were taken to enhance collaborative innovation. Two adjustments are argued in later sections. The promotion of KM awareness and initiatives to all departments influences the success of KM implementation in MIRDC, hence the task force has spent great efforts to promote KM awareness and educate employees about KM portal features. The successful introduction of the KM portal can be attributed to the following promotional activities and incentive programs: •

•

• •

•

•

To encourage novice users to use the KM portal. The task force held a KM portal knowledge seeking game and offered rewards (convenience store coupons), nearly a quarter of the total employees participated; Periodically published KM e-news to keep employees/staff aware of KM progress and the latest updates; Modified organization rules and regulations to bring changes to work habits; Communication with managers to gain basic support for auditing the KO’s production; Invitation of two R&D teams to test running an ontology-enabled document management and collaboration system by rewarding a free notebook to the team; Encouraging sharing of non work-related postings such as recreational topics, internet jokes, inspiring stories and quotations

to create an informal and receptive KM culture. The three-layered architecture of MIRDC’s KM portal is illustrated in Figure 3. The infrastructure services layer integrates various internal information systems and collaboration systems including the MIS system, Lotus Notes system, homepages of Websites, library system, E-learning system and individual file systems. The middle knowledge services layer aims to promote the knowledge cycle. Thus, knowledge services including workflow management, document management, search engine and K-map are integrated to support such processes. The upper presentation services layer benefits users by providing access to a unified application for KM processes. The KM portal uses a Java-enabled Web interface for easy browsing and accessing of information and knowledge. Personal alert messages from workflow management systems and operational databases are delivered to meet the specific needs of a user whenever he/she logs onto the portal.

KM EVALUATION This stage refers to the activity of examining the impact of KM implementation on the organization and the performance of the KM system. A thorough assessment of KM solutions usually involves evaluating the extent to which knowledge cycles are supported. Practically speaking, MIRDC did not take this KM plan from an academic perspective, thus it was unable to conduct a sophisticated evaluation for both the bridging people with systems strategy and the bridging people with people strategy. Alternatively, it assessed the success of KM through a user satisfaction survey for the KM portal system. These evaluations were important as they enabled the assessment of the effectiveness and usability of a developed KM system (Nagi and Wat, 2005).

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First of all, the IT engineers tried to enhance the system stability based on user feedback. Later the IT managers interviewed heavy users in the first quarter of 2004 after introducing the KM portal and collected comments about how the portal could be further improved to fulfill their needs. Accordingly, in the second quarter of 2004, IT engineers added new functionalities to make it as robust as possible, which included on-line administration approval, personal workflow alerts and on-line booking. A user opinion survey was then conducted by asking several open questions about user perceptions of the KM portal, which 72 users responded. The results showed that most users were satisfied with the improved system that offered the following benefits in facilitating knowledge work and knowledge flows across the organization: •

•

Almost all staff users enjoyed the convenient single sign-on and integrated workflow functions such as MIS and Notes online approval in the KM portal; More centralized knowledge storage, and more efficient knowledge discovery across dispersed databases facilitated by a search

•

•

•

engine. For instance, most engineers and managers felt a great ease in browsing various technical and training reports and then posting articles of interest; The on-line personalized workflow information for daily work could give timely reminders for users and improve work-efficiency. Integration of personalized daily workflow and routine practices such as project alerts, accounting alerts and facilities arrangement with the KM portal is a dominant function to the sustainability of the KM portal. Any information management system that is isolated from routine practices is expected to fail in the long run; (4)Users were satisfied with the daily industry e-news provided by a Web spider, which gave a quick glance at the everyday news. The news headlines were then stored in the database and could be searched for future R&D work; Employees located at the MIRDC headquarters and in two regional offices in different cities were happy with the simultaneous administrative announcements without solicitation.

Figure 3. Three-layered architecture of MIRDC’s KM portal (Source Chang & Li (2007))

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The growth of the KM portal usage is another factor to evaluate. The initial daily usage count was nearly fifty and after improving the KM portal functionalities based on user opinions, the average daily user count gradually grew as shown in next section. This evidence supports that the popularity of the new KM system was decided by the effective integration with the existing tools and culture. A rough estimate of the general cost of the KM deployment program throughout the organization in time and operational resources is given in Table 7. Total man power spent on the KM deployment program was estimated about 18 man-months including the time spent by the task force, IT engineers and employees. Expenses

spent on commercial software and KM portal construction was around US$62,000. This activity concerns the continuous improvement based on user responses and availability of new tools. The final stage in the manipulation of KM consists of monitoring progress and making adjustments accordingly on an ongoing basis. Following the introduction of the KM plan, more sophisticated KM portal functionalities (bridging people with systems) and knowledge sharing activities (bridging people with people) were implemented every few months, as indicated in Figure 4, to make the KM portal even more popular. These reinforcements consist of the linkage with daily workflow as work proceeds (e.g.

Table 7. Estimate of general costs of the KM deployment (Source Chang & Li (2007)) Types of cost Manpower

Expenses

Items

Spending

Time of cross-department task force spent throughout the deployment process

10 man-months

Time of internal IT engineers spent on configuring and setting up the IT platform

5 man-months

Others (time of employees spent on interviews and questionnaire)

3 man-months

IT software expenses (mainly for search engine, web spider and project workflow groupware)

US$30,000

Portal development by external IT service provider

US$32,000

Figure 4. Portal functionality replenishments & usage growth (Source Chang (2008))

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providing on-line administration approval and project report alerts), customer directory and personal messages (e.g. unknown work leaves, unchecked travel expenses), and automatic email publishing on the KM portal. These reinforcements echoed user feedback and encouraged users to interact with the system and other people, significantly increasing the average daily usage of the portal as new functions were added. As suggested by Barthès and Tacla (2002), the KM system must contain all the administrative and organizational knowledge deemed to be necessary for smoothing the user’s work. We found similar evidence from MIRDC experiences. By the first quarter of 2006 the average daily usage reached 130 which indicated that roughly one third of the employees in the offices would log onto the KM portal to support their daily work. Perhaps this is not such a great achievement in comparison with other famous international research organizations, but at least it demonstrates that MIRDC has successfully embedded the application of the KM portal into their organization. This case revealed that accessible central document management, integrated workflow system with personal alerts and on-line information sharing are essential to KM.

examine the commonality and specificity of the KM deployment framework adopted by MIRDC, we conducted a literature review. There are three types of KM frameworks with varied factors focused and questions concerned, i.e. system approach, step approach and hybrid approach as illustrated in Figure 5. Based on our analysis, the MIRDC’s KM deployment framework was designed in an attempt to fully answer the question of why, what, when, where, who and how of KM. This is analogous with the hybrid approach which covers the overall perspectives of KM and is generally applicable in different workspaces. On the other hand, considering the specific nature of the project operation in the MIRDC context, the common communication protocols existing in MIRDC such as specifying assignments of KM task forces, control points and resource allocation are embedded in the deployment framework which allows managers to closely watch over the progress of KM and make adjustments when necessary. Sharing and teamwork are common disciplines in the Taiwanese education system. Consequently, Taiwanese knowledge workers are more open to sharing thoughts and ideas, especially if they are encouraged to communicate (Stankosky, 2005).

KM DEPLOYMENT IN R&D CONTEXT

Figure 5. An analysis of KM implementation frameworks (Source Chang (2008))

Having reviewed the KM developments in MIRDC, this section will deliver the overall managerial implications for the KM field. The KM deployment framework is an enhanced approach in terms of applying process quality assurance and quality control concepts in KM implementation. Compared with other KM arguments which are mainly focused on the knowledge spiral or knowledge cycle (Holsapple & Joshi, 2002), the five-stage approach offers a holistic view of KM manipulation activities and serves as a clear and reliable guideline to assure that every step of KM implementation is properly undertaken. To

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One primary solution to enhance knowledge flow between employee and across departmental boundaries is the structuring of social network (Dahl and Pedersen, 2005). However, not all social contacts diffuse knowledge. Our study observed that a rigid hierarchical R&D structure due to technology segmentation in MIRDC actually inhibited the dynamics of the knowledge cycle across domains and between departments, making it no longer able to meet today’s business demand for total solutions in one-stop. MIRDC found that a parallel collaborative R&D structure with a focused industry interest across departments would streamline the sharing, exploitation and leverage of knowledge assets, thus to enhance the transfer of tacit knowledge, sharing should become a standard activity, not only for short-term projects. This is the reason why MIRDC started formulating six industryfocused communities of practice since mid 2005 in the hope of blending together individuals who have varying expertise and know-how. However it was observed that the organizational climate change to a more interactive one was slower than expected. One main reason was the lack of strong support from managers across departments and limited resources allocated to these loosely coupled communities. To tackle this problem and promote knowledge innovation across domains and among engineers, MIRDC transformed some of the communities into industry-focused work teams, i.e. ‘Mission Offices’, to solidify the value system of teambuilding. This change can be viewed as an evolution of the social network structure for solidifying interactive levels between employees. Necessary budget was allocated to these ‘Mission Offices’ to recruit specialists from various departments so that they could integrate their diversified knowledge and make the greatest contributions and produce innovations in the industry service. In addition, previous studies revealed that the acquisition and transfer of tacit knowledge can be enhanced by action learning. This is a process

through which participants learn with and from each other by mutual support, advice and questioning, as they work on real issues or practical problems while carrying out real responsibilities under real conditions (Koskinen and Vanharanta, 2002). Therefore, MIRDC strengthened another new mechanism of bridging people with people called ‘Pioneer & Innovation Program’ by allocating more research funds. This program was initiated to encourage cooperation among crossdepartmental engineers by submitting collaborative innovation ideas about new technologies or new applications of existing technologies. The budget supported the realization of the approved ideas, then knowledge exchange among engineers and new ideas began to boom. For instance, in 2005 the quantity of conception-type proposals (max. sponsorship US$30,000/each) and explorationtype proposals (max. sponsorship US$180,000/ each) was 24 and 9 respectively, and it increased to 44 and 14 in 2006. It is found that the ‘Pioneer & Innovation Program’ serves as a KM interaction platform for conceptualizing innovative ideas and discussing their feasibility by including other parts of the organization in the process. Meanwhile, through proper horizontal networking KM can help to identify and address gaps in the firm’s core technology area and explore emerging areas. It was observed that engineers were more likely to contribute their ideas under funding support and engaged in a sense of purposeful competition and operated under a project-based scheme. This contrasted with the original undisciplined and free knowledge share in KM communities, which often produced unproductive results. To deal with technological complexity and rapid change, this case illustrated that the fostering of ‘co-opertition’ (a combination of co-operation and competition) in a R&D context is a key to boosting the value of intellectual capital. Pure competition, i.e. profit center mode, will tend to deter individuals from sharing knowledge, even if it will lead to new knowledge and to greater rates

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of innovation. Thus we agree with Brännback (2003) that competition and cooperation usually co-exist in a network and enable the formation of Ba across different ontological dimensions. We also found that what MIRDC has conducted is analogous to the work and findings of Díaz-Díaz, et al. (2006). They identified two tacit knowledge mechanisms, namely technological alliance as channels for the transfer of knowledge, and the inter-firm mobility of engineers that would stimulate generation and integration of new organizational knowledge. Job changes help to build social networks across groups of firms by bridging the gaps between them (Dahl and Pedersen, 2005). Additionally, Möller and Svahn (2004) suggested that by constructing a strategic-innovation net and developing functional responsibilities, the collectivist and individualist could be merged and communication barriers between employees overcome. Suitably flexible and united task communication in an environment with a fixed reward and control structure has been proved to successfully create a more comprehensive and interactive information flow among team members (Brown and Eisenhardt, 1995; Kpskinen & Vanharanta, 2002), which is beneficial to creative works.

Figure 6. KNG Bridged by P&I Program & Mission Offices (Source Chang (2008))

Figure 6 illustrates the knowledge network gap (KNG) between departments bridged by ‘Pioneer & Innovation Program’ and ‘Mission Offices’. As a result, by interweaving two horizontal networking instruments, i.e. ‘Mission Offices’, and ‘Pioneer & Innovation Program’, with the vertical management hierarchy, and by adopting departmental knowledge sharing quantity as a rating factor in the yearly performance evaluation, MIRDC has successfully tackled the KM challenges in the Chinese context like relation circles, evaluation anxiety and power barriers, as indicated previously. This study further compared MIRDC’s KM networks with other literatures as illustrated in Figure 7. The formation of ‘Mission Offices’ naturally incur job changes and inter-firm mobility, while ‘Pioneer & Innovation Program’ acts like a technological alliance and a strategic innovation net.

AN INTEGRATED VIEW OF KM DYNAMICS It is widely perceived that technology alone does not offer a complete solution to manage organizational knowledge; an extensive change at behavioral, cultural and organizational level is needed to make KM successful (Barnard & Pöyry, 2004). Socio-technical perspective is another KM approach to understand the interweaving of social and technical factors in the way people work and adapt. Pan & Scarbrough (1998) claimed that a successful implementation of KM should comprise three socio-technical components: •

•

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Infrastructure – refers to the technical components such as hardware and software that enables the communications and interactions between people, Info-structure – incorporates a set of formal rules and norms governing exchanges and sense making between people;

Deploying Knowledge Management in R&D Workspaces

•

Info-culture – involves the background knowledge embedded in social relations and work group process plus core values and beliefs influencing employees’ willingness to exchange knowledge and help solve problems.

Snowden (2003) also discusses that the engineering approaches have become a hygiene factor in KM implementation. Furthermore, Hsu’s study (2006) suggested a stage model of knowledge sharing in the Chinese context. At first, organizational knowledge sharing can be supported with IT tools and networking at a time when a company seeks to achieve knowledge centralization and modularization. Once this routine is established and self-normalizing, the company is changed from the role of manager to facilitator, which encourages a sharing climate. Consequently, based on our observation of MIRDC’s experiences and reviewed literature, we argue that different angles of KM approaches and organizational behavior studies are analogous with each other. Herzberg’s two factor theory (Herzberg, Mausner & Snyderman, 1959) states that peoples’ attitudes about work and the social-technical KM perspective can be mapped harmoniously. Figure

8 presents the integrated view between MIRDC’s KM practices, two factor theory and the socialtechnical view of KM. For KM enforcement, we argue that IT tools act as hygiene factors which can create KM dissatisfaction, but their presence alone does not motivate or create satisfaction. On the other hand, a sharing culture, trusting relationships and respect based on personal knowledge contributions are motivating factors that determine KM advancement and achievement. Therefore, KM practitioners should be aware of that under high IT conditions (hygiene factors) and low sharing cultures (motivating factors), employees often have few complaints but are not highly motivated, while low IT availability and high sharing culture represents a situation where KM involvement is exciting and challenging, but complaints about supporting IT conditions occur. Regarding the KM social-technical perspective, the three components play different roles. Info-structure serves as the ground rules and standards that guide the process of KM implementation. The infrastructure of KM has higher impacts on explicit knowledge management and lower impacts on implicit knowledge management,

Figure 7. Comparison of mechanisms of KM networks (Source Chang (2008))

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Deploying Knowledge Management in R&D Workspaces

Figure 8. MIRDC’s KM approach, T-F theory & S-T perspective (Source Chang (2008))

while info-culture primarily decides the degree of networking between knowledge workers, which leads to the openness of sharing critical knowledge stored in the minds of people. This leads to a schema of the interaction between MIRDC’s approach to KM, mapped onto Herzberg’s ‘Two Factor Theory’ (T-F theory) and a social-technical perspective (S-T perspective) as shown in Figure 8.

KM FAILURE ANALYSIS AND LESSONS LEARNED Successful implementation of KM in MIRDC is very much dependent on the promotion of KM awareness within the organization and the implementation of a good reward mechanism to encourage contributions from engineers. Table 8 reviews the KM gaps and barriers found in knowledge audits and related ‘remediation’ ap-

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proaches adopted by MIRDC. In order to keep all parties involved, various activities such as focus group meetings, weekly KM news and competitive incentive programs are important to encourage user participation. Meanwhile, nonstop improvements on the KM portal and KM process play an important role to show the organization’s commitment to continuous growth in this area. The usage growth trend of the KM portal clearly proves the Taiwanese saying: ‘Employees follow what their leaders do, not what they say’. Except for the transformation of failed communities of practice to ‘Mission Offices’ and ‘Pioneer & Innovation Program’, during the process of KM deployment in MIRDC, there are some KM efforts that were not successfully implemented as expected: •

On-line R&D project collaboration: failure reasons may be owed to project engineers

Deploying Knowledge Management in R&D Workspaces

Table 8. Review of KM gaps/barriers and approaches adopted by MIRDC (Source Chang (2008)) Dimensions

KM gaps/barriers

MIRDC’s approach

General issues

Lack of KM strategies and their alignment with organizational strategies Management support is not strong Researchers lack time and have high workloads, thus inhibiting their participation in KM activities Necessity awareness of KM among employees is poor

Develop KM strategies and policies Gain fund from project leaders Integrate KM portal with daily administration workflow

Explicit knowledge

Knowledge assets have not been clearly defined Management of explicit knowledge is problematic and unsystematic Hard to reach distributed databases and lack of KM portal Lack of on-line collaboration platform

Conduct knowledge audit and focused group discussion Use Lotus Notes as the central knowledge repository Connect distributed databases through a KM portal Invite two R&D teams to test run a project collaboration platform Develop a presentational KO management prototype system Outsource an intelligent Chinese search engine Formulate KM regulation and assign responsibilities

Management of presentational KO, such as MS PowerPoint files, is problematic. Search efficiency for explicit knowledge objects is low No clear regulations and procedures guiding the production, sharing and accumulation of explicit knowledge Implicit knowledge

•

•

Rigidity of bureaucratic structure categorized by technology fields and each department operates as profit-center Cross department R&D networking is inadequate so that new knowledge creation, i.e. innovation, is slow Expert skills and customer directory are not available Lack of mechanisms and incentives to encourage implicit knowledge sharing

working in proximity, non-user-friendly collaboration platform and utilization of collaboration platform not obliged; KM strategies not intimately aligned with organizational strategies: After launching the KM portal, engineers enjoyed ever handy knowledge sharing and access, however the alignment between KM pursuit and organizational performance remains vague. One major reason is the weak commitment from top management; The transfer of organizational tacit knowledge, i.e. passing on the know-how and philosophy behind the engineering drawings, cannot be systematically measured and secured.

There are two more lessons we have learned from this case. The first one relates to the politics among key stakeholders. After reporting the KM

KM promotion activities

Firstly formulate six industry-focused communities of practice, then replaced by mission offices Start the Pioneer & Innovation program Partly supported by KM portal Enhanced by mission office and Pioneer & Innovation program

plan, the KM task force found that top management does not seem to plan to allocate enough resources as expected. In a wish to launch the KM plan and establish the KM portal, the task force communicated with two general project leaders to improve document management, patent management and on-line project collaboration, and the leaders were persuaded and endowed half of the budget. In other words, in a hierarchical structure paralleled with intensive project operations, i.e. a matrix system, there are different ways to acquire the resources you need to accomplish the goals. The second lesson learned relates to the package of KM activity. Perhaps it is better not to loudly advocate the term KM to enforce the management of knowledge because in many situations, KM might seem dangerous to employees in terms of job security and extra workload. Like the formation of ‘Mission Offices’ and ‘Pioneer & Innovations Program’ in MIRDC, they were not launched and

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announced by the name of KM, however they actually trigged lateral knowledge interactions, circulation and creation.

CONCLUSION In conclusion, this case study contributes to the KM research in three aspects. First of all, this chapter reviews the development of KM among Taiwanese R&D organizations, contributing to a field which has not been extensively studied in previous literature. Secondly, this case reveals a very sophisticated deployment process for managing existing knowledge in the R&D workspace that could be very useful for other organizations in guiding and implementing a KM plan. Finally, this case echoes other studies which have identified that the organization structure can create significant effects on KM achievements. Many researches have revealed that the diffusion of knowledge between firms, departments and teams can take place either through formalized collaboration or through informal social network (Dahl & Pedersen, 2005). Social networks among R&D engineers carry knowledge across organization boundaries. Several factors limit the scale and depth of social networks and communities including frequency of communication, physical proximity, mutual disclosure of information and mutual trust, etc. The case study emphasizes the perspective that a rigid hierarchical R&D structure through technology segmentation should also incorporate a parallel R&D collaboration structure that is cross-departmental and industry-focused to obtain horizontal networking and interactive knowledge sharing. A parallel R&D collaboration can actively enforce the occurrence of information trading, the exchange of information between employees working for different interests in the organization. With respect to the commonality and specificity of the KM deployment framework, based on the characteristics of our case, we suggest that the

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KM experiences gained from MIRDC might be generally applicable to other institutes that have similar organization structure and routine operation mode, i.e. technology-segmented organization structure, project-based operation and non-profit orientation.

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Burgelman, R. A., Maidique, M. A., & Wheelwright, S. C. (2004). Strategic management of technology and innovation (4th ed.). Boston: McGraw-Hill Irwin.

Gold, A. H., Malhotra, A., & Segars, A. H. (2001). Knowledge management: An organizational capabilities perspective. Journal of Management Information Systems, 18(1), 185–214.

Chang, W. (2008). Fostering knowledge management deployment and knowledge exploration in R&D workspaces. Unpublished doctoral dissertation, National Cheng Kung University, Taiwan.

Herzberg, F., Mausner, B., & Synderman, B. (1959). The motivation to work. New York: Wiley.

Chang, W., Chuang, S., & Li, S. (2004). A case study of adopting knowledge management systems for research-oriented organizations: MIRDC’s experience. Proceedings of the Knowledge Management Conference in Asia Pacific, Taiwan, 128-135. Chang, W. C., & Li, S. T. (2007). Fostering knowledge management deployment in R&D workspaces: A five-stage approach. R & D Management, 37(5), 479–493. doi:10.1111/j.14679310.2007.00484.x Dahl, M. S., & Pedersen, C. Ø. R. (2005). Social networks in the R&D process: The case of the wireless communication industry around Aalborg, Denmark. Journal of Engineering and Technology Management, 22, 75–92. doi:10.1016/j.jengtecman.2004.11.001 Damm, D., & Schindler, M. (2002). Security issues of a knowledge medium for distributed project work. International Journal of Project Management, 20(1), 37–47. doi:10.1016/S02637863(00)00033-8 Díaz-Díaz, N. L., Aguiar-Díaz, I., & Saá-Pérez, P. (2006). Technological knowledge assets in industrial firms. R & D Management, 36(2), 189–203. doi:10.1111/j.1467-9310.2006.00425.x Drongelen, I. C. K., Weerd-Nederhof, P. C., & Fisscher, O. A. M. (1996). Describing the issues of knowledge management in R&D: Towards a communication and analysis tool. R & D Management, 26(3), 213–229. doi:10.1111/j.1467-9310.1996. tb00957.x

Holsapple, C. W., & Joshi, K. D. (1999). Description and analysis of existing knowledge management frameworks. Proceedings of the 32nd Hawaii International Conference on System Sciences, Hawaii. New York: IEEE. Holsapple, C. W., & Joshi, K. D. (2002). Knowledge manipulation activities: Results of delphi study. Information & Management, 39, 477–490. doi:10.1016/S0378-7206(01)00109-4 Hsu, I. (2006). Enhancing employee tendencies to share knowledge: Case studies of nine companies in Taiwan. International Journal of Information Management, 26, 326–338. doi:10.1016/j.ijinfomgt.2006.03.001 Huber, G. P. (1991). Organizational learning: The contributing processes and the literatures. Organization Science, 2(1), 88–115. doi:10.1287/ orsc.2.1.88 Koskinen, K. U., & Vanharanta, H. (2002). The role of tacit knowledge in innovation processes of small technology companies. International Journal of Production Economics, 80, 57–64. doi:10.1016/S0925-5273(02)00243-8 Lee, S. M., & Hong, S. (2002). An enterprise-wide knowledge management system infrastructure. Industrial Management & Data Systems, 102(1), 17–25. doi:10.1108/02635570210414622 Leitner, K., & Warden, C. (2004). Managing and reporting knowledge-based resources and process in research organizations: Specifics, lessons learned and perspectives. Management Accounting Research, 15, 33–51. doi:10.1016/j. mar.2003.10.005

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Maier, R., & Remus, U. (2003). Implementing process-oriented knowledge management strategies. Journal of Knowledge Management, 7(4), 62–74. doi:10.1108/13673270310492958 Milliou, C. (2004). Vertical integration and R&D information flow: Is there a need for firewalls. International Journal of Industrial Organization, 22, 25–43. doi:10.1016/S0167-7187(03)00090-0 Möller, K., & Svahn, S. (2004). Crossing east-west boundaries: Knowledge sharing in intercultural business networks. Industrial Marketing Management, 33, 219–228. doi:10.1016/j.indmarman.2003.10.011 Nagi, E. W. T., & Wat, F. K. T. (2005). Fuzzy decision support system for risk analysis in ecommerce development. Decision Support Systems, 40, 235–255. doi:10.1016/j.dss.2003.12.002 Pan, S. L., & Scarbrough, H. (1998). A sociotechnical view of knowledge-sharing at Buckman Laboratories. Journal of Knowledge Management, 2(1), 55–56. doi:10.1108/EUM0000000004607 Paraponaris, C. (2003). Third generation R&D and strategies for knowledge management. Journal of Knowledge Management, 7(5), 96–106. doi:10.1108/13673270310505412 Parikh, M. (2001). Knowledge management framework for high-tech research and development. Engineering Management Journal, 13(3), 27–33. Pun, K., Chin, K., & Lau, H. (2000). A review of the Chinese cultural influences on Chinese enterprise management. International Journal of Management Reviews, 2(4), 325–338. doi:10.1111/14682370.00045 Raub, S., & Wittich, D. V. (2004). Implementing knowledge management: Three strategies for effective CKOs. European Management Journal, 22(6), 714–724. doi:10.1016/j.emj.2004.09.024

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Rubenstein-Montano, B., Liebowitz, J., Buchwalter, J., McCaw, D., Newman, B., & Rebeck, K. (2001). SMARTVision:Aknowledge-management methodology. Journal of Knowledge Management, 5(4), 300–310. doi:10.1108/13673270110411724 Sapsed, J., Bessant, J., Partington, D., Tranfield, D., & Young, M. (2000). From IT to teams: Trends in the management of organisational knowledge. Paper presented at the R&D Management Conference: Wealth from Knowledge: Innovation in R&D Management, Manchester, UK. Snowden, D. (2003). Innovation as objective of knowledge management, Part 1: The landscape of management. Knowledge Management Research & Practice, 1, 113–119. doi:10.1057/palgrave. kmrp.8500014 Sohn, J. H. D. (2004). Rewarding KM performances at SAIT. KM Review, 7(4), 8–9. Spiegler, I. (2000). Knowledge management: A new idea or a recycled concept? Communications of the AIS, 3(14), 1–23. Stankosky, M. (2005). Creating the discipline of knowledge management. Burlington, MA: Elsevier. Szulanski, G. (1996). Exploring internal stickiness: Impediments to the transfer of best practice within the firm. Strategic Management Journal, 17(Winter Special Issue), 27-43. van Donk, D. P., & Riezebos, J. (2005). Exploring the knowledge inventory in project-based organizations: A case study. International Journal of Project Management, 23, 75–83. doi:10.1016/j. ijproman.2004.05.002 Warren, P., & Graham, D. (2000). Knowledge management at BT Labs. Research Technology Management, 43(3), 12–17.

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Chapter 5

Innovation in New Technology and Knowledge Management:

Comparative Case Studies of its Evolution during a Quarter Century of Change Sean Tung-Xiung Wu Shih Hsin University, Taiwan

ABSTRACT The research on which this chapter is based monitors the evolution of IT innovations and their effect on human emotions, including longitudinal influential factors, and examines some of the resulting syndromes, which are termed Computer Fear Syndrome (CFS) and User Alienation Syndrome (UAS). The research involves an analysis of the empirical data derived from several case studies and concludes with a funnel model that explains appropriate management action and puts forward new ideas for developing knowledge management systems in a variety of organizations that may alleviate or prevent such syndromes in the work place.

Introduction This comparative research depicts and explains the evolution of organizational innovation through the adoption and deployment of information systems. There are two cases that have been carefully and thoroughly investigated for years. The first is the M Company, one of the top two computers groups in Taiwan, which developed corporate publishing systems in 1988. The second was the C Company, the largest telecommunications group in Taiwan,

which brought in knowledge management (KM) systems for corporate training in 2004. There are differences in the objects and the objectives of innovation between the two cases. Technically, the corporate publishing systems are desktop based; relatively compact local working groups while the knowledge management systems are web-based, with more sophisticated, boundaryless environments. The goals of the former systems were to reduce labor and increase sufficiency of production. The purposes of the latter intended to share intelligence and to encourage creativity

DOI: 10.4018/978-1-60566-701-0.ch005

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Innovation in New Technology and Knowledge Management

through collaboration. In the early days of computerization, the Computer Fear Syndrome (CFS) was a conceptual threat while the User Alienation Syndrome (UAS) may be a subconscious threat at present. It is worthy of note that there are more similarities in the processes of innovation of the current case with the first case that occurred more than a quarter of a century ago. The fundamental corporate decision setting is also the same as before. Management, as always, has to comply with innovative ideas, investment and risk at the same time. The goals of expected efficiency whether in physical profits or in mental productivity have also remained the same over the years. The individual user’s behavioral factors involved in implementation and results of innovations have also always needed to be identified. The organizational factors in management actions are worthy of constant re-examination. Yes, there are many intriguing, even novel, variables that may affect innovations, according to the vast body of related literature. However, how many of these are fundamentally influential? This research attempts to reveal, by selective quantitative and qualitative evidence from very fruitful resources, which factors are changeable and which are likely to stay in place for a very long time. The author/researcher concludes with several statements that may be helpful for those who want to adopt new technology, especially, in acquiring knowledge management systems in the future. Discussion of the measurement of the efficiency of knowledge management or latent, unstructured psychological constructs have also been amended according to the specific requirements of the present. The researcher of this study was the Director in charge of the Innovation Project for the M Company and a consultant for the C Company.

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Case 1: M Company The Problem Around the mid 1980’s, business began to seriously consider bringing in PC-based information systems to replace labor intensive work.1 One of management’s great concerns was that of the Computer Fear Syndrome (CFS). CFS referred to computer anxiety or negative attitudes toward adopting computing work and predicted that employees with CFS would performance poorly. (Wu, 1995). When computers “invaded” human life, some researchers argued as to whether the computer was “a threat or a promise?” (Cherry, 1971) They thought that senior persons or novices who were used to a traditional work environment would express negative attitudes towards computers. If they began to learn computing tasks, they would be slower and make more errors than new employees. The Fear Syndrome, generated by an anxiety of failure, might also limit their performance. (Caldeira &Ward, 2001) (Shneiderman, 1980). So, management had two choices: First, if the CFS did widely exist; they would have to recruit new, young employees and let the current workers go. Second, if the CFS did not really cause significant harm, they would need to provide training programs for current employees and educate them about the EUC environment. The CFS was also cautiously perceived and discussed in Taiwan. Hung and Xu (1988), based on a survey of government organizations, found that staff employees who reported themselves as having the CFS was 20%. However, the range of the CFS that was recognized and evaluated by supervisors was bewilderingly greater, from 4.4% to 48.2%. Since the evidence was inconclusive, the reaction of different companies varied. The publishing business used to be a highly labor intensive industry, and was interested in computerization. (Bjorn-Andersen, Earl, Holst & Blunden, 1982) (Young, 1988). Thus, the U Company, one of the top two publishing companies in Taiwan, was the

Innovation in New Technology and Knowledge Management

first one to launch the computerized publishing process around 1985. (Boldt, 1987) It decided to hire a brand new crew to learn the EUC process and to work for the Department of Typesetting and Composition. It did not even bother to ask current employees if they wanted to change. Because of this decision, the U Company had to transfer current employees to other jobs, such as security, or to pay for their layoffs. Obviously, the cost of this policy was enormously expensive. There were, and are, not many companies that could afford to make such a move. (Wu, 1984b, 1984a). The M Company made a different decision.

Case Description The M Company was one of the top two computers groups in Taiwan whose business covered systems integration, PC manufacturing, distribution and other related information services. The H Company was one of the M Company’s subsidiaries and ran business in cultural and educational fields. It published a magazine on computers, provided computer training programs, and ran media campaigns for its clients. In 1987, the Board of Directors of the M Company determined to spin off the H Company and let it extend its business independently. The researcher, who used to be the Vice Director of the Computerization Project of the U Company, was invited to become the Chief Executive of the H Company and took responsibility for the H Company’s innovations. The researcher proposed that the M Company and the H Company work together to develop Desktop Publishing (DTP) Systems for the H Company’s operation. The idea was that if the new systems were to be successful in meeting the H Company’s mission requirements, it would create a new DTP total solution and initiate a new market of corporate/personal publishing. (Wu, 1985). Examining the U Company’s experience, its computerization design could be categorized as a “Simulation Model” that maintained the con-

ventional organization and kept both its Editorial Department and Composition Department. The publishing processes had remained the same; the writers and editors stayed on, using old forms of manual operation. Compared to the U Company, the H Company’s design was a “Reengineering Model” that altered all processes and entirely removed the Composition Department. It was expected that the writers/editors would cover writing, typesetting, page design and composition at the same time. This project also wanted to answer the following questions: 1. How can we measure the evaluation of the innovation of DTP? 2. What is the extended evidence of the CFS on IT innovation? 3. What are the factors that may affect the IT innovation? An academic research project was designed and conducted along with the Product Planning Project. (Wu, 1995)

Methodology Overview We examined the idea of User Psychology that attempted to establish theories on computer users in the early days of the birth of the PC. It presented a distinct field of the “Human Aspects of Computing” Study both in computer and management sciences after the 1980’s. (Moran, 1981) (Ramsey, 1979). Researchers have observed plenty of variables that might affect, even determine users’ behavior. Among them, Newell’s (1972) user’s behavior formula and Moran’s (1981) follow up especially drew our attention. After a thorough and careful discussion, we chose two sets of dependent variables (DV) to evaluate the efficiency of DTP. Since time and errors were always employed as users’ performance indicators by information

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Innovation in New Technology and Knowledge Management

systems designers (Card, Moran & Newell 1980; Shneiderman, 1979; Walther & O’Neil, 1974), one set of DVs was users’ Performance measured by their work efficiency (speed) and quality (errors) on specific assignments. The second set of DVs was users’ Adoption Behavior measured by their psychological acceptance and practice preference. In practice, we had tested many independent variables (IDV) and demographic variables. Many of them contributed little influence on the DV. For this reason, we ruled out those variables in this final report. We kept two, which are also commonly recognized by previous researchers, for deliberation. These IDVs were users’ knowledge (about the system) and users’ motivation (to participate in the innovation). In order to observe the CFS, we designed a quasi-experiment to compare the performance of current employees with that of new users.

Quasi-Experiment Design This DTP was developed from September 1987 and was launched in August 1988. Then, the following quasi-experiment design was summarized as Table 1. A group of seventeen employees of the Editorial Department of the M Company was designated to be the quasi-experimental group, as Group E. Two control groups with the same number of persons as Group E were comprised of college students who had no past experience of traditional publishing. Students from computers and information departments were assigned to

Group C, while students from liberal arts departments were in Group A. All groups took the same training courses and same tests before and after training. Word processing (WP) and desk top publishing (DTP) were defined as the core tasks of the computing work. All groups’ performances were measured by a final assignment at the end of the training program. Users would be required to input and compose two scripts into one section using a specified layout with WP and DTP. In spelling languages, to input alphabet does not sound a problem, but it requires certain special skills to input Chinese characters. It was also more difficult to compose page layout at that time.

Measurement Performance was measured by users’ speed and errors during their final assignment. WP speed was measured by average words typed during test times. DTP’s speed was measured by minutes required for finishing the task. Error was measured by wrong operations during the whole task. Users’ knowledge was measured by a “UKAT” test, which was designed by the Technical Advisory Committee of this Project and modified by pilot studies, with an internal reliability Alpha =.4817. Users’ motivation was measured by “MLS” psychometric scales, tested by pilot studies, with an internal reliability Alpha =.7419.

Table 1. Summary of the Quasi-experiment Design Pretest Time

Posttest

Sep.1988Feb.1989

Group

E, C, A

Measurement

Knowledge Motivation

80

Training

E, C, A

Online Work

Evaluation

Mar.1989Nov.1989 E, C, A Performance 1. WP 2. DTP Acceptance Preference

E

E Innovation Experience 1. By scorers 2. By users

Innovation in New Technology and Knowledge Management

In-Depth Personal Interview This procedure recorded users’ points of view from their self-report. Each user of the three groups was interviewed after the final assignment to reveal their attitude change of acceptance for and preference of the new DTP, or of and for the old manual tools. Group E was interviewed again six months after their online work. Three scales and questionnaires were employed: • • •

Acceptance scales: with internal reliability Alpha =.7558. Preference scales: with internal reliability Alpha =.6812. Innovation Project questionnaires: overall opinions about users’ CFS, attitude change, and innovation experience in open-ended form, only for Group E.

Participant Observation and Scorer Evaluation This method provided the researcher’s point of view and evaluation on users’ behaviors. The researcher and an associate, who was the Administrative Manager of the M Company, worked together to observe and score Group E users’

adoption by structured scales. The observation period, according to the Innovation Theory, was divided into two stages. Stage 1 was before the end of training. Stage 2 was six months after the online work. Scorer reliability of stage 1 was.7550 (p<.001) and stage 2 was.7214 (p<.001). One employee in Group E left the M Company and became a missing subject in Stage 2. Another employee became a contract freelancer, but was still included in this research.

Main Result General Description Speed Performance varied widely. Performance of errors made no significant variation. Acceptance was above merely good. Preference was in favor of the transition to computing work. Users’ knowledge was barely moderate. Motivation was around the very high level (see Table 2).

WP Performance Speed Employees were much better than students. The average speed (words per hour) of Group E was

Table 2. Description of Case 1 Variable

Mean

Std Dev

Minimum

Maximum

N

WP speed

559.88

570.61

88.00

3600.00

51

WP errors

1.04

1.74

0.0

10.00

51

DTP speed

107.37

46.38

10.00

171.00

51

DTP errors

3.57

1.75

0.0

9.00

51

Acceptance

73.35

13.67

36.00

100.00

51

Preference

72.49

17.46

0.0

100.00

51

Knowledge

55.24

19.61

0.0

96.00

51

Motivation

84.88

9.46

69.00

100.00

51

By Scorers

78.06

8.47

63.75

89.06

16

By Users

73.44

15.26

37.50

100.00

16

Innovation of Group E

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Innovation in New Technology and Knowledge Management

Table 3. ANOVA: WP speed by user roles Source of Variation

Sum of Squares

DF

Mean Square

F

Signif. of F

Between Groups

7492254.47

2

3746127.24

20.46

0.00

Within Groups

8787432.82

48

183071.52

Total

16279687.29

50

325593.75

1101.88, while Group C was 282.76, and Group A was 295.00. Significant difference was found with ANOVA (see Table 3). Table 4 showed that high knowledge (817.42) was better than low (330.96). Motivation made no difference. Further contrast analysis found that both high knowledge and high motivation would produce the best performance. Errors No difference was found in any user factors.

DTP Performance Speed Employees were still the best and Group C was better than Group A. The average speed of Group E was 56.94, while Group C was 111.06, and Group A was 154.12. Significant differences among groups were found with ANOVA (see Table 5) and its further contrast. Table 6 showed that high knowledge (74.58) was better than low (136.52). Motivation made no difference. Errors No difference was found from any user factors.

Acceptance and Preference The acceptance was mildly high. There was no main effect between groups, but an interaction between knowledge and motivation was found. Further covariance analysis found that users with high motivation and high knowledge produced high acceptance, while high motivation and low knowledge produced low acceptance. Users generally preferred the new computing work. There was statistical difference between groups, but the individual difference was greater. Users with high knowledge had a positive relationship between acceptance and preference, while those with low knowledge did not.

Innovation Experience Scorer Evaluation Both scorers’ evaluations were coherent with the results of the quasi-experiment. In Stage 1, users shared a slightly positive attitude towards computing work with no clear CFS. In Stage 2, after users got used to computing work, there was little CFS. Some of the users expressed an attitude change

Table 4. ANOVA: WP speed by knowledge and motivation Source of Variation

Sum of Squares

DF

Mean Square

F

Signif. of F

Main Effects

3473491.54

2

1736745.77

6.65

0.00

Knowledge

2398468.87

1

2398468.87

9.18

0.00

Motivation

466807.04

1

466807.04

1.79

0.19

2-way Interactions

528318.25

1

528318.23

2.02

0.16

Explained

4001809.77

3

1333936.59

5.11

0.00

Residual

12277877.53

47

261231.44

Total

16279687.29

50

325593.75

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Innovation in New Technology and Knowledge Management

Table 5. ANOVA: DTP speed by user roles Source of Variation

Sum of Squares

DF

Mean Square

F

Signif. of F

Between Groups

80614.28

2

40307.13

71.76

0.00

Within Groups

26959.64

48

561.66

Total

107573.92

50

2151.48

in favor of computing, while the others kept the same attitude as they had had before Stage 2. No one expressed a worse attitude toward computing than they had started out with. Self-Report 38% users responded that their attitudes had changed; the remaining 62% kept the same attitude they had had. 31% of users changed from a mutual attitude to a positive one. They explained that they did not realize the complete advantages of computing during the training period until they were very familiar with the new tools. One user, who went from a positive attitude to a neutral, said that she lost her curiosity toward computing when the work became routine. Three users always kept indifferent attitudes, while seven were in favor of computing from the beginning to the end. 94% of users reported that they had interest in computing, and 80% of them were very interested in it. One user said that she “got to do what I got to do.” although she was not interested in computing. During the innovation process, 81% users had met with difficulties; 38% users were not totally satisfied with their work yet. There were some

existing problems, but no obvious CFS. Four users preferred the old manual operation in certain periods of Stage 1. When the entire project had been completed, every user stated that they would choose computing work.

Reflection Evaluation of the Innovation It is very difficult to formulate an overall, quantitative and qualitative, evaluation for a management policy. (Kaplan & Norton, 1996, 1993, 1992) However, a financial report might reveal some solid evidence. After the analysis of the increase of investment in systems and the decrease of the human labor cost and manual overhead, the annual accounting statement indicated the cost of production had been cut by 28%. Furthermore, the production period had been shortened by 41%. These results provided more flexible time to accept business opportunities; therefore, revenue amazingly increased by 82%. These facts can be considered as a significant testament to the efficiency of the innovations.

Table 6. ANOVA: DTP speed by knowledge and motivation Source of Variation

Sum of Squares

DF

Mean Square

F

Signif. of F

Main Effects

49080.76

2

24540.38

19.72

0.00

Knowledge

44846.76

1

44846.76

36.05

0.00

Motivation

341.41

1

341.41

.274

0.60

2-way Interactions

31.60

1

31.60

.025

0.87

13.16

0.00

Explained

49112.35

3

16370.78

Residual

58461.57

47

1243.86

Total

107573.92

50

2151.48

83

Innovation in New Technology and Knowledge Management

CFS: Not a Real Threat The concern of CFS did not appear in this case study. The current employees had done much better than the other two control groups in every aspect. It seemed it was all right to count on the current employees. A slight attitude change in favor of innovation was found among employees and no one presented negative attitudes. Employees produced significantly successful performances and healthy mental adoptions to the innovation. Past experience did not seem to be a barrier to the management’s innovation policy. If there was a real task goal, if the employees wanted to stay, “they got to do what they got to do”, one interviewee pointed out.

Other Factors May Affect the IT Innovation What were the main user factors that would affect user behavior regarding IT innovation? Users’ knowledge was the key factor to performance. The statistics showed that people with higher knowledge of computers produced significantly better accomplishments than people with lower knowledge. It would be a good idea to continuously provide onsite training for employees. User motivation seemed not to be a factor in this study, though it was many previous researchers’ major concern. (Chintakovid, 2009; Rantapuska, 2002; Stanton, Mastrangelo, Stam & Jolton, 2004). It should be pointed out that users in this study were relatively young. There is a theoretical possibility that age could be correlated to motivation that only affects older persons. Further research might be useful in clearing up this matter. As almost always happens in reality, the new system was defined and selected by the management in business. The employees did not have much say about it before the deployment of the new system. Evidence from the open-ended interviews showed that the new

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system’s usability would affect user’s productivity. This might support the idea to include a couple of end users into the decision group during the design and introduction period of the innovation.

Case 2: C Company The Problem In the late 1990’s, the concept of “Knowledge management (KM)” burst into the business world with overwhelming force. There were two approaches to explain the practice of KM. The first placed KM as a new design of process and environment that emphasized to “share” and solve specific questions, especially in unstructured domain knowledge. (Hoffman, 1999; Nonaka, 1994; Ruggles, 1998) The second claimed that KM was a new look at information systems, decision support systems, data management and the use of the internet, even though it was a combination of recycled concepts. (Spiegler, 2000) The KM systems in this case were the first type rather than the second type information systems for daily operations. Whether KM is a marketing campaign promoted by IT providers, such as the enthusiastic Microsoft, or not, it triggered a tidal wave that saw business everywhere beginning to install KM information systems. (Alavi, 1999; Grant, 1996). After almost two decade’s experience, management did not worry about the CFS or resistance from the employees. However, unlike production and/or management information systems where there are physical indicators to evaluate the efficiency, it is much more difficult to measure the accomplishment of KM systems. Management needed to know what the efficiency of knowledge innovation was. (Hawryszkiewycz, 1999; Huber, 1991). C Company was one of the first large businesses to launch KM systems in Taiwan.

Innovation in New Technology and Knowledge Management

Case Description

Methodology

The C Company used to be a governmental monopoly agency that regulated, provided and sold all telecommunications goods and services. It did not transform into a governmentally-funded business entity until 1996. The C Company was, and is the single dominant telecom provider in Taiwan and has continuously reaped high profits. Since it still virtually controlled the whole national information infrastructure and network backbone, all other competitors worked, more or less, as its clients. The T Institute is an important division of the C Company and takes the responsibility for training, testing for professions, testing for certificates, production and publishing of training materials, copyrights and circulation. Most employees of the T Institute are tutors with MA or even Ph.D. degree. They belong to three teaching fields: cable telecom, mobile telecom, and data telecom. The T Institute has three branches that are located in different regions of Taiwan. Since the T Institute has played the role as the “brain” of the C Company, management decided to deploy the new KM systems in the T Institute from 2000. In 2003, a midrange manager wanted to investigate the efficiency of the KM systems and initiated a survey project. During this time, the researcher was invited to be the consultant to this Project. This Project intended to explore the following issues:

Overview

1. A measurable evaluation of the innovation of KM. 2. An analysis of the users’ performance on KM. 3. What factors may affect the innovation of KM. This Project was designed and conducted during 2004.

We entered relevant keywords into several famous databases including Science Direct on Site, JSTOR, ACM Digital Library, Academic Research Library, ABI Global, IEEE database and Google Scholar, and we found little useful literature that directly answered our concerns. There were many papers that gave advice on matters prior to KM, or how to establish KM systems. (Kanter, 1999; King, Marks & McCoy, 2002; Nonaka, 1994) (Ruggles, 1998; Vail, 1999) On the other hand, there were few research projects that monitored the efficiency of the KM systems with empirical studies. Brancheau et al. (1993) introduced a 2+2 EUC management model that was based on a comprehensive literature analysis. The two focal components of this model are the organization level and the individual level. The first level focuses on strategy, technology, and management action. The second level considers users, tasks, tools and personal actions. The other two parts of this model are antecedents (i.e. context factors) and consequences (i.e. outcome factors) of users’ work. The four components are not independent, but interconnected and dominate the practice of management innovation. This model and other studies correlated with our previous study in 1988 to a degree. However, we wanted to detect the factor of “management action” for this project, although it was a factor that was very difficult to measure. Most previous studies measured “management action” (or other managerial concepts) by attitude questionnaires that we were not fully satisfied with. We believed that there was not much insight to be gained from the following type of dialogue: “Do you agree with the importance of management’s action?”

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“Yes, I agree.” “How much do you agree? Agree? Or strongly agree?” “Strongly agree.” Thus, we attempted to discover more qualitative data to interpret the influence of “management action” later on. Since the efficiency of KM (DV) was quite conceptual, we decided to use multiple ways of evaluating the innovation of KM. The former four observations were the efficiency of users’ behavior. The last was a self-report scale to reflect users’ attitude. The five methods we chose to use were as follows. 1. Employee’s adoption: whether the interviewees had used the KM systems or not; 2. Applications that the users had processed on the KM systems; 3. Contents that the users shared with others through the KM systems; 4. Collaborations with others that the users had achieved specific goals with via the KM systems; 5. A scale of users’ attitudes toward the efficiency of KM systems. Again, we tried to investigate many IDVs and found that most of them did not play fundamental roles. This Project, concurrently with Case 1, emphasizes that the two most important IDVs were users’ knowledge (about the system) and users’ motivation (to participate in the innovation). An add-on IDV was management action.

Survey Design There were a total of 93 tutors from three branches of the T Institute. Therefore, we determined to interview every tutor. A dedicated interviewer was trained and he travelled to every branch of the T Institute to complete interpersonal surveys. He had to make appointments with the interviewees 86

who might not be available when he visited. No interviewee was to be excluded unless the interviewee refused to respond to the survey in person.

Measurement Employee’s adoption, users’ applications, shared contents, and collaboration were recorded by the facts, including the interviewees’ real behavior. Users’ attitude toward the efficiency of KM systems was measured by “EKS” scales that covered curricula, teaching tools and teaching methods. The internal reliability Alpha with after measurement purification was.9790. Users’knowledge was measured by the modified “UKAT” test, which was designed by the Technical Advisory Committee of this Project, with an internal reliability (after measurement purification) Alpha =.8799. This test contained two subsets. One was for knowledge of KM, and the other was for how to use the T Institute’s KM systems to accomplish various tasks. Users’ motivation was measured by the modified “MLS” psychometric scales with an internal reliability (after measurement purification) Alpha =.9686. These scales also included two subsets. One was for personal motivation, and the other was for interactive motivation should employees wish to share their knowledge through the KM systems.

Main Result General Description Sixty two (73%) of the total ninety three employees responded to this survey. The results were very informative; over one third of the interviewees had never used the KM systems for more than three years. All statistical means of the three factual measurements, which reflected the efficiency of users’ behaviors, were under mid-points. Even the efficiency reported by the interviewees was a moderate 64.12 on a 100-point scale. Users’ knowledge of KM was medium high, while the knowledge in using the installed KM systems was a little less

Innovation in New Technology and Knowledge Management

than mid-level. Motivation, whether personal or interactive, was moderate. (See Table 7).

between users’ knowledge levels and motivation levels.

Adoption

Efficiency in Attitude

The total adoption rate barely reached 62.9%. There was no difference found between interviewees’ knowledge levels and motivation levels. However, there was a significant difference (P<.01) between the locations of the branches. The percentage of adoption at the northern branch reached 79.3% while the central branch was 33.3% and the southern branch was 66.7%. The northern branch was located in Taipei, the capital of Taiwan. The southern branch was in Kaohsiung, the second largest metropolitan area in Taiwan.

Significant differences were found both in knowledge and interactive motivation. The higher knowledge level (69.42) reported better efficiency than the lower knowledge level (58.66). The higher interactive motivation level (70.63) reported better efficiency than the lower interactive motivation level (58.66).

Users’ Applications, Shared Contents, and Collaborations All of these three indicators were poor sources of significant data. No correlations were found

Personal Motivation and Age In the previous case study, we wondered if there was an interaction between motivation and age that had not been revealed in the Case 1. We found positive evidence in this case that personal motivation and age did produce an interaction with the efficiency in attitude. In further contrast, age indicated that older users with lower personal

Table 7. Description of Case 2 Variable

Mean

Std Dev

Minimum

Maximum

Adoption Yes No

Percentage

N

62.9% 37.1%

68

Applications

0.3852

0.2432

0.25

1.00

39

Shared Contents

0.3980

0.24

0

1.00

39

Collaborations

15.3276

11.6456

0

33.33

39

Efficiency in Attitude

64.12

14.93

17

86

38

Knowledge KM KM systems

75.3676 46.37

24.6213 20.29

25.00 0

100.00 86.67

68

Motivation Personal Interactive

64.0556 60.29

14.09 15.53

21.67 24.29

100.00 87.14

39

Table 8. ANOVA: efficiency in attitude by knowledge Source of Variation

Sum of Squares

DF

Mean Square

F

Signif. of F

Between Groups

1013.24

1

1013.24

5.47

0.03

Within Groups

6295.33

34

185.16

Total

7308.57

35

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Table 9. ANOVA: efficiency in attitude by interactive motivation Source of Variation

Sum of Squares

DF

Mean Square

F

Signif. of F

Between Groups

1787.16

1

1787.16

9.96

0.00

Within Groups

6459.96

36

179.44

Total

8247.11

37

Table 10. ANOVA: efficiency in attitude by personal motivation and age Source of Variation

Sum of Squares

DF

Mean Square

F

Signif. of F

Personal Motivation

1668.37

1

1668.37

17.96

0.00

Age

353.18

1

353.18

3.80

0.06

Interactions

867.76

1

867.76

9.34

0.01

Residual

2694.26

29

92.91

Total

146661.64

33

motivation would report lower efficiency than younger users, while there was no age difference factor in the high personal motivation level. (See Table 10)

Reflection Evaluation of the Innovation Though the attitude scales were widely applied in similar studies, we were not comfortable with depending solely on the self-reports. Thus, we employed four more methods to collect substantial behavioral data. Although the efficiency reported by the users’ attitudes was moderately above average, all the other four factual and behavioral data were much lower than the mid-level. It would be a fair judgment to conclude that the accomplishment of these KM systems was poor and that the efficiency of the innovation was not truly successful.

UAS: the Comeback of the Deviance of CFS This case implied a potential User Alienation Syndrome (UAS) that might be a variant of CFS.

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None of the employees feared computers. On the contrary, they were an elite group; they were tutors and taught specific topics about computers. However, more than one third of the interviewees had never touched the KM systems. It is a fact that the User Alienation Syndrome did exist. And, this might not be a single case phenomenon! In another non-systematic observation, it was found that there was a nationwide project that subsidized some awarded academic organizations to install KM systems. There was an assessment that required every user to process at least two sharing activities, such as to initiate a topic, to respond to a question, to post a document and so on, per month. It ended up with many users asking assistants to run anything, whether relevant or irrelevant, on the KM systems. Usability might be a factor, but not a very influential one. According to the Case 1 in 1988, the usability of the DTP systems had to be recognized as “Very Weak” by today’s technical standards. However, all employees had worked very hard on the systems. The User Alienation Syndrome might result from the interaction of systems’ characteristics and human nature. The goal of production and/or management is to produce while the goal of KM systems is to share. Users had to use the former systems to

Innovation in New Technology and Knowledge Management

survive and make their living, while users were quite free to choose if they wanted to use the KM systems to make a dream of their own come true. Human nature, the largest part of it, tends toward a convenient, simple and easy life. Most of a job’s duties are normal, stable and routine. Sophisticated employees have already learned a solution to deal with their daily, on-the-job operations. Especially for tenured employees and those who worked for very profitable companies, users have little reason to bother themselves to look for real breakthroughs, much less to conduct a revolutionary battle. The UAS seems to reflect an inborn structure in human nature. One is not able to force employees to use KM systems. All incentives or regulations, such as two posts per month, might be in vain after all. The KM systems might be only for the people who stand beyond the three standard deviations of the normal distribution of human nature.

Management Action Though many previous studies have concluded “corporate resource” was the main factor that decided the success of information systems adoption, (Caldeira & Ward, 2001) we deduced that the success of an innovation mostly depends on the users. Furthermore, we also found traces of evidence that management takes the responsibility for the quality of innovation. During the survey of this case, we recorded some heuristic facts as follows: Management decisions can be made on loose ground. ”There were two reasons that the headquarters of the C Company decided to bring in the KM systems. First, it was a novel issue in 2000. Second, they could afford to do it” stated a senior midrange manager who was involved in the original planning. Though the Director of the T Institute approved the maintenance fee every year, he himself had never been involved in the development of these KM systems. The Director defined this survey project as a voluntary and internal opinion exchange instead of the official

assessment that was originally purposed after much planning. In fact, the Director considered this position as one “shift” in his career. He was waiting for the opportunity to get promoted and go back to Headquarters. All the evidence and inferences showed that the Director was not truly interested and did not include the KM systems into his priority schedule. Case 1 of the M Company is a very good comparison. The researcher used to be the ViceDirector of the innovation project of the U Company. Though he held a high position, he was still a relatively young Ph.D. student then. Consequently, the U Company took external advisers’suggestions to establish a “simulation” system environment concerned with CFS, and dropped his proposal for a “reengineering” model. Afterwards, he was invited to the M Company and realized his plan to develop totally different DTP systems from the U Company. Eight years after the researcher left his position; the U Company shut down its “simulation” department and returned to a “reengineering” environment. It thus paid double cost in human resources, facilities, and wasted a considerable amount of time. Back in the 1980s, the U Company was protected by an old charter law and was able to generate easy revenue. Without this law, any deficit was very difficult to recover from. The successful experience of the M Company most likely indicates that dedicated executive management does make a difference. It also seemed, according to the T Company and U Company’ experiences, that management of easily profitable companies often makes expensive and inefficient decisions.

Cross Examination and Conclusion Evolution This quarter-century long evolution of systems development is summarized in Table 11.

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Table 11. A summary of the evolution of systems development Production/Management Systems

Knowledge management Systems

Operation

Daily based

Initiative project based

Support

Labor

Intelligence

Main Activity

Work

Share

Goal to Deal with

Routines

Creativity

Usability for

Individual

Collaboration

Efficiency Evaluated

Significantly Successful

Inconclusive

Challenge

CFS: Not found in this case

UAS: Maybe a latent threat

Users’ Knowledge

Core factor

Core factor

Users’ Motivation

Not significant in this case

Core factor

Management Action

Funnel Model: The executive management of the project

Other Factors

Did not play core roles in these two cases

The researcher suggested a “Funnel Model” to conclude how the “Management Action” affects systems’ development and efficiency. At the beginning and pre-planning stage, there are a lot of influential factors: 1. The corporate culture: How to react, or proact to external change. 2. The top management’s interest and attention. 3. Available technology and choices. 4. The size of investment, related resources and future overhead. 5. The expected efficiency. 6. The internal institution: How the corporation implements a project. The above factors form a wide range, which is the top opening of the funnel, and interact with each other. However, when the project is entering the practice stage, there is only one determinative and determinant exit of the funnel left. The last factor will be who the executive management in charge of the project is.

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Revolution? An ideal theory is that a rational knowledge process can complement human insufficiency. In the real world, not many people want to admit to their insufficiency. An expected goal is that a sharing environment can trigger human creativity. In reality, most people want to stay on the same track instead of go crazily off in search of innovation. Therefore, the researcher proposed some different (revolutionary?) ideas for the deployment of KM systems in the future.

For Employees vs. For Special Interest Groups Many KM systems we have seen were developed for “all” employees who belonged to the same functional division. It has ended up with the use of systems becoming just another form of “paperwork”. The systems designer may reconsider such an environment useful only for special interest groups and “qualified members”. This idea might also promote the image of KM as elitist.

Innovation in New Technology and Knowledge Management

KM Systems vs. KM Community Many KM systems designers began with the point of view of “systems analysis and design” and assumed that there was a “common user” with “common requirements”. Designers often unconsciously set up a fixed interaction framework for users during the deployment of their systems. We might drop the approach of “KM systems” and think about the idea of “KM community” instead. The idea is that we do not bother the shared database and the common usability; we provide an autonomous place for members to use. The sharing process works as if we are going to visit a neighbor’s virtual house. He does not live in a dormitory and he has something fantastic in his study waiting for us. Thus, the researcher has initiated the concept of “Member-Driven” design to compare with the popular model-driven, datadriven designs.

Internal KM vs. External KM Many companies established KM systems for, and limited to, internal employees because there might be a lot of confidential information in the shared database. However, if we recognize that the fundamental purpose of KM is to share, to accelerate imagination, and to work out innovations together, we might open the fence and link to external KM communities. Both “Member-driven” and “External KM” will create technical problems such as the integrity of database, the management of redundant data, and data security, etc. However, it might be worthy trying to resolve these problems, if we want to discover the passion for knowledge that is deeply buried in human nature.

Methodological Issues Brancheau et al. (1993) criticized the fact that evaluation and review of information systems had almost totally relied on self-report questionnaires. They recommended other approaches such as case

studies, experimental designs, and longitudinal methods to solve more problems and to validate earlier findings. They also stated that they were still limited by the scope of the existing research. They attempted to describe a comprehensive profile; however, all of the literature and experiences available to them were from North America, and they lacked research reports from Asia and Europe. They suggested that new research design will be promising. Studies conducted outside the USA are expected to assist in creating a worldwide scope of research. The researcher indeed agreed with their comments. We designed several behavioral measurements to collect substantial data along with the self-report attitude scales, and took a year’s time to depict each case. The comparative results also suggested that the self-report questionnaires method might produce higher optimistic evaluations than exist in reality.

LIMITATIONS AND FUTURE RESEARCH Owing to the variants of the definition, the KM systems that were employed in daily operation might not fit the discussion of this case study. This research could provide some experience in Taiwan, and might assist in building a worldwide theory and cross-cultural perspective on systems, especially KM systems development. Most of fast developing countries (such as PRC) are bound to go through the evolution trace of the new IT innovation. Research of this field with the international point of view is recommended and expected in the future.

REFERENCES Alavi, M. (1999, February). Knowledge management systems: Issues, challenges, and benefits. Communications of the AIS, 1, 1–37.

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Bjorn-Andersen, N., Earl, M., Holst, O., & Blunden, E. (1982). Industry case studies of microelectronic applications: The printing and publishing industry. In Bjorn-Andersen, N. (Eds.), Information society: For richer, for poorer (pp. 287–297). New York: North-Holland. Boldt, J. R. (1987). Electronic publishing. Paper presented at 1987 Strategic Industry Conference, Taipei. Brancheau, J. C., & Brown, C. V. (1993). The management of end-user computing: Status and directions. ACM Computing Surveys, 25(4), 437–479. doi:10.1145/162124.162138 Caldeira, M. M., & Ward, J. M. (2001). Using resource-based theory to interpret the successful adoption and use of information systems and technology in manufacturing small and medium sized enterprises. Paper presented at The 9th European Conference on Information Systems, Slovenia. Card, S. K., Moran, T. P., & Newell, A. (1980). Computer text-editing: An information-processing analysis of a routine cognitive skill. Cognitive Psychology, 12, 32–74. doi:10.1016/00100285(80)90003-1 Card, S. K., Moran, T. P., & Newell, A. (1982). The psychology of human-computer interaction. New York: Erlbaum. Cherry, C. (1971). World communication: Threat or promise? A Social- Technical Approach (ch. 5-6). New York: Wiley. Chintakovid, T. (2009). Effects of gender, intrinsic motivation, and user perceptions in end-user applications at work. Unpublished doctoral dissertation. Drexel University, Philadelphia, PA. Grant, R. N. (1996). Prospering in dynamicallycompetitive environments: Organizational capability as knowledge integration. Organization Science, 7(4), 375–387. doi:10.1287/orsc.7.4.375

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Hawryszkiewycz, I. T. (1999). Knowledge sharing through workspace networks. ACM SIGGROUP Bulletin. Hoffman, C. V. (1999). Do we know how to do that?: Understanding knowledge management. Harvard Management Update, Number: U9902A. Huang, K. L., & Xu, M. L. (1988). A cultural and psychological study on office automation of governmental organization in Taiwan. Taipei: Committee of Research and Auditing. Huber, G. P. (1991). Organizational learning: The contributing processes and the literatures. Organization Science, 2(1), 88–115. doi:10.1287/ orsc.2.1.88 Kanter, J. (1999). Knowledge management practically speaking. Information Systems Management, 16(4), 7–15. doi:10.1201/1078/43189.16.4.1999 0901/31198.2 Kaplan, R. S., & Norton, D. P. (1992). The balanced scored card-measures that drive performance. Harvard Business Review, 70(1), 71–79. Kaplan, R. S., & Norton, D. P. (1993). Putting the balanced scorecard to work. Harvard Business Review, 71(5), 132–147. Kaplan, R. S., & Norton, D. P. (1996). Using the balanced scorecard as a strategic management system. Harvard Business Review, 74(1), 75–85. King, R. W., Marks, P. V., & McCoy, S. (2002). The most important issues in knowledge management. Communications of the ACM, 45(9), 93–97. doi:10.1145/567498.567505 Moran, T. P. (1981). An applied psychology of the user: Guest editor’s introduction [Special issue on the psychology of human computer interaction]. ACM Computing Surveys, 13(1), 1–12. doi:10.1145/356835.356836 Newell, A., & Simon, H. A. (1972). Human problem solving. Englewood Cliffs, NJ: Prentice Hall.

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Nonaka, I. (1994). A dynamic theory of organizational knowledge creation. Organization Science, 5(1), 14–37. doi:10.1287/orsc.5.1.14 Peters, D. M. (2000). Knowledge management: Four practical steps. Harvard Management Update, 5(3). Ramsey, H., & Atwood, M. (1979). Human factors in computer systems: A review of the literature. [DEN. Englewood, NJ: Office of Naval Research.]. Technical Report SAI, 79, 111. Rantapuska, T. (2002). Motivation structure of end-user application developers in organisational learning. Unpublished doctoral dissertation. University of Tampere, Finland. Ruggles, R. (1998). The state of the notion: Knowledge management in practice. California Management Review, 40(3), 80–89. Saunders, R. (2000). Managing knowledge: How to make money with what you know. Harvard Management Communication Letter, June. Shneiderman, B. (1979). Human factors experiments in designing interactive systems. Computer, 12(12), 9–18. doi:10.1109/MC.1979.1658571 Shneiderman, B. (1980). Software psychology. Cambridge, MA: Winthrop. Spiegler, I. (2000). Knowledge management: A new idea or a recycled concept? Communications of the Association for Information Systems, 3, 14. Stanton, J. M., Mastrangelo, P. R., Stam, K. R., & Jolton, J. (2004). Behavioral information security: Two end user survey studies of motivation and security practices. Proceedings of the Tenth Americas Conference on Information Systems, New York, New York. Vail, E. F. (1999). Knowledge mapping: Getting started with mnowledge management. Information Systems Management, 16(4), 16–23. doi:10.1201 /1078/43189.16.4.19990901/31199.3

Walther, G. H., & O’Neil, H. F., Jr. (1974). On-line user-computer interface: The effects of interface flexibility, terminal and experience on performance. In Proceedings of 1974 National Computer Conference (pp. 379-83). Montvale, NJ: AFIPS Press. Wu, T. X. (1984a). The planning of news information system. Journal of the United News Group, 20, 17–39. Wu, T. X. (1984b). Report of requirement analysis on news information system. Journal of the United News Group, 22, 78–84. Wu, T. X. (1985). Toward computing news: The goal of news information system project. Journal of the United News Group, 30, 36–41. Wu, T. X. (1987). The coming of computing news media: A technical perspective for the deregulation of journalism in Taiwan. Automation, 38, 168–170. Wu, T. X. (1990). Users’ behavior at the changing period of computerized editing: A case study of INFOHIT magazine. Journalism Research, 43, 153–178. Wu, T. X. (1995). Management concerns on computer fear syndrome: An empirical study of a publishing company. Proceedings of the 11th International Conference on Advanced Science and Technology, University of Chicago, Chicago: CAPAMA (pp. 32-39). Young, R. (1988). Electronic publishing. Paper presented at Computer Aided Publishing Conference, Taipei.

ENDNOTE 1



The author uses the term of “information systems” for consistency. Back to that time, the term in practice was labeled as End User Computing (EUC) environment.

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Section 2

Applications of Knowledge Management

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Chapter 6

A Survey of Epistemology and its Implications for an Organisational Information and Knowledge Management Model Ah-Lian Kor Leeds Metropolitan University, UK Graham Orange Leeds Metropolitan University, UK

ABSTRACT This is a theoretical chapter which aims to integrate various epistemologies from the philosophical, knowledge management, cognitive science, and educational perspectives. From a survey of knowledgerelated literature, this chapter collates diverse views of knowledge. This is followed by categorising as well as ascribing attributes (effability, codifiability, perceptual/conceptual, social/personal) to the different types of knowledge. The authors develop a novel Organisational Information and Knowledge Management Model which seeks to clarify the distinctions between information and knowledge by introducing novel information and knowledge conversions (information-nothing, information-information, information-knowledge, knowledge-information, knowledge-knowledge) and providing mechanisms for individual knowledge creation and information sharing (between individual-individual, individual-group, group-group) as well as Communities of Practice within an organisation.

INTRODUCTION Epistemology is the study of knowledge which includes what it is and how it is acquired. Nonaka and Takeuchi (1995) emphasise the need to understand what knowledge is, know how to manage it, and exploit it to increase an organisation’s

competitive advantage. They view every member in an organisation as knowledge workers where new knowledge always begins with an individual which can then be transformed into organisational ‘knowledge’. In this chapter, we would like to address a few issues. Firstly, there is a need to revisit seminal epistemology and unify them with contemporary epistemology so as to uncover the

DOI: 10.4018/978-1-60566-701-0.ch006

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A Survey of Epistemology and its Implications

elusive meaning of knowledge. Secondly, there is general lack of consensus over tacit and explicit knowledge as well as information and knowledge, which will affect knowledge management in organisations. To address these two issues, we have developed an Organisational Information and Knowledge Management Model to tease out the differences between information and knowledge for an organisation’s benefits, and have provided mechanisms for individual knowledge creation and information sharing among individuals within the organisation. The Organisational Information and Knowledge Management Model is grounded on philosophy, knowledge management, cognitive science, educational theories, and is consistent with Wiig’s (2002a; 2002b; 2004) New Generation Knowledge Management (NGKM) which addresses people-centric knowledge developments (e.g. how people learn, possess knowledge, apply knowledge, etc.). The discussion in this chapter is divided into two sections where the first section will present the outcome of a survey on epistemology related chapters in philosophy, knowledge management, cognitive science, and education while the second section, a conceptual design of an Organisational Information and Knowledge Management Model. In this chapter, we have categorised epistemology into the following: seminal epistemology (rationalist approach, empirical approach, pragmatic approach, social approach); contemporary epistemology (cognitive approach, knowledge management approach: pluralist epistemology). Pluralist epistemology is further divided into the following categories: dichotomy model of knowledge; multiple model of knowledge; continuum model of knowledge; duality model of knowledge; and knowing model of knowledge. The conceptual design of the Organisational Information and Knowledge Management Model aims to tease out the differences between information and knowledge, followed by depicting mechanisms for individual knowledge creation and information sharing, and finally Communities of Practice within the organisation. 96

LITERATURE SURVEY ON EPISTEMOLOGY Seminal Epistemology Rationalist Approach According to the rationalist approach to epistemology, knowledge is justified true belief (Plato in Newman, 2005) or unshakeable conviction (Descartes in Newman, 2005) which is attained through reason alone. Such type of a priori knowledge which is independent of sense experience, could be innate knowledge or acquired through intuition and deduction. Popper (Thornton, 2006) claimed that scientists begin with problems rather than observations and he attributed the growth of human knowledge to the search of solutions (involving the formulation of theories) which correspond to these problems. However, creative imagination which transcends the existing knowledge is required when current theories are inadequate to account for anomalies.

Empiricist Approach Empiricists argue that humans have no innate knowledge, the human mind is a blank slate (tabula rasa) and claim that experience is a source of a posteriori knowledge (e.g. Aristotle (Hett, 1936) and John Locke, 1689). Empiricists like Locke (1689) argue that human experience comes in the form of sensation and reflection where the former subsumes external senses (e.g. vision, smell, hearing, taste, and touch) and inner sensations (e.g. pain, joy, anxiety, etc.) which informs one about the things and processes in one’s external world. On the other hand, reflection informs one about the operations of one’s mind. Locke also argued that the outcome of our mental processes is ideas which are considered as the materials of knowledge. According to him, simple ideas cannot be created but can only be obtained from experience. However, when the mind has a repository of simple ideas which when reflected on (or applied

A Survey of Epistemology and its Implications

reasoning to), will result in a variety of complex ideas that transcend beyond our experience. Empirical (or scientific) methods are employed to collect data through the observation of these physical phenomena, analyse them followed by the derivation of laws or theories.

Pragmatic Approach Peirce (Atkin, 2006) viewed pragmatism as a principle of inquiry and account of meaning where meaningful propositions or ideas must have practical bearings. Theoretical claims (or hypotheses) are coupled with verification practices to test the truth of existing knowledge. However, to pragmatists, ultimate truth is not attainable so existing truth is always changeable. The inquiry methods suggested by them resemble the typical scientific methods which constitute the following cycle of actions: formulation of hypotheses, testing of hypotheses, draw conclusions from the tests or provide explanations for the observed effects followed by reformulation of hypotheses and so on and forth. According to Peirce (Burch, 2006), the three types of reasoning involved in scientific methods are: abductive, deductive and inductive reasoning. Abduction entails the inference of some form of plausible explanation which is considered the best explanation for the current state of knowledge for an unexpected or anomalous observed effect. Such explanation is considered a conjecture or hypothesis whose truth is not ensured. On the other hand, for deduction, the conclusions are necessitated by previously known theories where inferences are

being made from general principles to particular cases. To Peirce, deduction is a means of drawing conclusions about the expected observable effects given the hypothesis is true or drawing conclusions based on a set of facts or supposed facts known as suppositions (Garnham & Oakhill, 1994). Shanahan (1989) defines the roles of abduction and deduction in Cognitive Robotics: the former, for explanation which is a backward projection from effects to causes while the latter is employed for prediction, a form of forward projection from causes to effects. As for induction, it involves the testing (confirmation or refutation) of hypotheses and Holyoak and Nisbett (1988) define induction as the drawing of inferences to generate hypotheses or generalisations. However, Evans (1990) argues that inductive inferences can be used to classifying observations of specific observations into categories, thus resulting in the acquisition of the knowledge of concepts as well as categories. A pragmatic approach could be viewed as a bridge of the rational and empirical approaches which are mutually exclusive but are complementary to each other. A pragmatic approach could be viewed as a bridge of the rational and empirical approaches where they are mutually exclusive but are complementary to each other. Peirce (Atkin, 2006) viewed pragmatism as a theory of clarifying concepts (things, events, and qualities) and he introduced a maxim or a principle which allows us to better understand concepts that we use. The three pre-requisites for fully understanding a concept are: firstly, the particular concept ought to be familiar and saturates our

Figure 1. Scientific method of inquiry

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A Survey of Epistemology and its Implications

Figure 2. A pragmatic approach

daily experience, secondly, the ability to abstract and provide a definition for that concept facilitated by language (Saljo, 1995), and finally, the ability to predict the effects when a concept is held to be true. However, Dewey (1960) maintained that ‘understanding’ is demonstrated when various parts of a concept/s are ‘grasped in their relations to one another, a result that is attained only when acquisition is accompanied by constant reflection upon the meaning of what is studied’ (pp. 78-79). This implies that understanding of a concept is said to be attained when one is able to grasp a coherent network of its components with reflection being instrumental in this process. Kant (McCormick, 2006) refuted the tabula rasa model of the human mind (empiricist view) by arguing that the human mind is an active originator of phenomenal experience by systematically structuring its representations rather than a passive recipient of perception (Ross, 2000-2002). He (McCormick, 2006) claimed that knowledge about the world is not attributed to sense perceptions alone but due to the operations on perceptual inputs by the mind based on innate rules, principles, or categories that facilitate understanding. James (1890) in his book The Principles of Psychology, named analysis and synthesis as examples of such mental operations where the former is a process of breaking down of objects which appear as wholes in the first instance, into their parts while the latter, bringing together objects which appear separately, and combine them as new compound wholes. He also applied the Law of Contiguity to support his claim that that objects that are experienced together

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have the tendency of being associated in the mind. James also added that not everything presented to our senses will be converted to experience due to selective attention. Some attention can be immediate (where a new percept is novel) or derived (or apperceptive where a new percept is related to a known percept). Kant postulated that knowledge has form or structure due to the structure of the mind that facilitates the unification or integration of concepts into judgements and content which is provided by the interaction of the mind with the world (Ross, 2000-2002). Both Kant and James (Goodman, 2006) viewed the mind as possessing a priori templates for judgements (or values) and categories but not a priori judgements. According to James in The Principles of Psychology, knowledge could grow or change through rational processes, empirical discoveries or introspection (e.g. reflection put forth by Dewey, 1960) which is an inward process. These processes can be illustrated by a modified Kolb’s experiential learning cycle (Kolb and Fry, 1975) which is shown in Figure 3 (note: learning is synonymous with knowledge acquisition in this chapter).

SOCIAL APPROACH A scientific community is viewed as a group of individuals that are committed to the sharing of their theoretical beliefs, values, instruments, and techniques. Kuhn (1962) highlighted the significant role of such scientific community in effecting a revolution of scientific theories which is not pos-

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Figure 3. Modified Kolb’s learning cycle (Kolb & Fry, 1975)

sible with the mere accrual of facts. He explained that the scientific community has an established coherent framework of scientific thought (called paradigm) which constitutes stable and consistent conjectures, theories, or practice. Normal science that occurs within such a framework is not dramatic and it develops by the addition of new truths to the stock of old truths, or the increasing approximation of theories to the truth, and the ratification of past errors. However, a paradigm is considered stretched when it is ridden with anomalies where numerous observed phenomena cannot be accounted for and then a crisis is said to occur. In such a situation, Kuhn continued, the community of scientists needs bold (or active) individuals who will explore alternatives which are considered rivals to the existing established framework of thought. This new potential but immature paradigm will initially seem to be accompanied by numerous anomalies due to its incompleteness and there will be no consensus on the emerging theories or methods which include verification rules. Consequently, it will be opposed by the majority of the scientific community. However, scientists who could recognise the would-be paradigm’s potential will be the first to shift in favour of the challenging paradigm. When it is solidified as well as unified and widely accepted by the community, then it is ready to replace the old paradigm, and thus a paradigm shift is said to have occurred. The paradigm shift will entail a

general consensus on radical new world views, the transformation of theories, changes in definitions of terms, verification rules and etc. Vygotsky’s zone of proximal development (ZPD) concept which was introduced in the 1930s will help enlighten the role played by the community in helping individual’s acquisition of knowledge. Moll (1990) defines the term ‘zone’ as a social system where an individual is placed in a concrete social situation of learning and development. Vygotsky (Rieber & Carton, 1987) stated that knowledge is transferred to a learner in such a system. The ZPD relates to the development of knowledge structure with and without support. Here, we illustrate the notion of ZPD in Kor (2001). The level at which a person can perform a task independently is the actual level of development, and with help, he will be able to perform the task at a higher level, which is his potential level of development. Vygotsky’s notion of ZPD is the difference between these two levels (Hedegaard, 1990). This means that in this particular zone, an individual is directed toward his full development with ‘assistance’ provided in a social system. Sewell (1990) explains that the dual purpose of such ‘assistance’ is to first expose the adequacies and inadequacies in the individual’s current levels of thinking, and to then proceed to a higher level of understanding. Valsiner (1988) suggests that ‘assistance’ here embodies socially

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Figure 4. Zone of proximal development (adapted from Kor, 2001)

provided resources for the process of a learner’s development. However, Vygotsky himself never specified the forms of social assistance to learners in the zone of proximal development (Moll, 1990). Vygotsky wrote about collaboration and direction, and about assisting learners for example through demonstration, leading questions but did not specify beyond those general prescriptions. Based on his point of view, ‘assistance’, typically refers to direct interaction such as instruction, tutoring, or collaboration (in groups or community) which, according to Dewey (Field, 2006), is facilitated by a common language and shared meaning. Dewey also emphasised the social dimension of inquiry by claim that a theory of inquiry cannot be fully understood without considering its social context (e.g. how the theory will apply to its social aims, values, or norms). This means that individuals have the responsibility of aligning their individual efforts to socially defined goals and expectations. However, in the event of conflicts, then it calls for negotiation and harmonisation of experiences (resolution of habits and interests both within the individual and within the society).

CONTEMPORARY EPISTEMOLOGY In this section, we shall address two views of epistemology: one being from the cognitive approach (adapted from Kor, 2001) while the other is knowledge management which plays a pivotal

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role in fostering knowledge creation, codification, sharing, and diffusion.

Cognitive Approach The essential elements in Piaget’s stage-independent theory of cognitive development addressed in this chapter are: schema, assimilation, accommodation, and equilibrium. Schemata, according to Piaget, are internal mental structures which depict the way a person represents the world (Mayer, 1992) through perception, understanding and thoughts (Hill, 1990). When a piece of information is similar but not identical to a learner’s inherent knowledge structure, it will be assimilated by the existing cognitive structure. During this assimilation process, two changes will take place simultaneously. The first pertains to the stimulus itself while the second is the schema. The stimulus will be modified and, at the same time, the schema changes to accommodate the new input. The various ways of accommodating a new experience as outlined by Papert (1980) are abandoning the old or new knowledge, modify one or the other, or place both in separate compartments. When the conflict between the contradictory old and new knowledge has been resolved then equilibrium is said to have occurred. The schemata, in such a situation, are found to be in a stable state. Mendelson (1996) maintains that the notion of conflict is used as a tool to foster a conceptual change. However, if an incoming piece of information is either exactly the same or totally different from

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an existing mental structure then it will have no influence on the schema (Piaget in Mayer, 1992). The reason being the former is nothing new so will not be a stimulus of change while the latter can neither be understood nor encoded, thus failing to relate to the existing knowledge framework. According to Strike and Posner (1985), conceptual change theory gives due emphasis to prior knowledge in learning and it also focuses on the transformation of conceptions in the learning process. They adopt the words, ‘accommodation’ and ‘assimilation’ from Piaget’s theory of equilibration to denote conceptual change. The former represents large-scale conceptual change while the latter refers to conceptual change of a lesser scale. Three views of conceptual change are as follows: •

•

Schema change (Rumelhart and Norman, 1977). A cluster of related concepts is referred to a schema and is likened as a schema as a kind of tree structure in which subschemata correspond to subtrees. They classify three types of learning that can occur within a schema framework: accretion which occurs within existing schemata through the gradual addition of factual information interpreted in terms of relevant pre-existing schemata; tuning which involves the slow modification and refinement of schemata through continual use and, presumably, it is instrumental for the development of expertise; and structuring involves the creation of new schemata to account for new information; Theory change (Carey, 1985; Vosniadou, 1995). A theory structure differs from schemata in that it provides a causal explanatory framework within which a phenomenon it describes can be understood (Vosniadou, 1995). In domain-specific theory change, Carey (1985) proposes a few possible changes: change in the individual concepts that make up the theory, change

•

in the relationships between the concepts, and change in the scope of the phenomena that the theory explains. Vosniadou’s (1995) notion of theory change appears to be an extension of Carey’s work when she further describes the changes in terms of theory enrichment through addition or theory restructuring through deletion or modification; Mental model change. A mental model is perceived as a form of knowledge structure (Gentner and Stevens, 1983) while some see it as a transient representation which is constructed on the spot to deal with a particular situation (Johnson-Laird, 1983; Vosniadou and Brewer, 1992; Vosniadou et al. 1999). According to Vosniadou et al. (1999), such a representation can be manipulated mentally to provide causal explanations for physical phenomena and make predictions about the causal effects of the physical world. Mental models change in different ways as a result of learning and the change in a mental model is either of the mental model itself or in the underlying structures that constrain it (Vosniadou, 1995).

Knowledge Management Approach: Pluralist Epistemology The pluralist epistemology recognises the existence of more than one type of human knowledge which interacts with each other. The term is first used by Spender (1996) to capture the different types of knowledge that an organization uses (e.g. knowledge that is held individually or collectively). Based on existing Knowledge Management literature pluralist epistemology could be classified into the following categories: •

Dichotomy Model of Knowledge: This model which is prevalent, has been the subject of extensive debate and our dis-

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•

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cussion here will only focus on the typical categories of knowledge being explicit knowledge and tacit (or sometimes known as implicit, a term used by Polanyi, 1967) where the former is formal, systematic, and can be quantified, captured, codified (or structured), stored, reused, and disseminated. Typical examples of explicit knowledge that have been given include product specifications, codified procedures, a scientific formula, principle, or a computer programme. On the contrary, tacit knowledge is not easily captured, expressible, codified, communicated, nor shared. It is often associated with deeply rooted actions or experiential knowledge that resides in the heads of the knower (Nonaka and Takeuchi, 1995; Spender, 1998; Davenport and Prusak, 1998). According to Nonaka and Takeuchi (1995), both explicit and tacit knowledge are dichotomous yet mutually complementary and interact with each other in the knowledge conversion process. There is no general consensus about the explicit and tacit knowledge. Although the Spiral Model of Knowledge (Nonaka and Takeuchi, 1995) is considered a piece of seminal work in Knowledge Management, its knowledge conversion process (from tacit to explicit and vice versa) is critiqued (Hildreth et al, 2002; Stenmark, 2002; Tsoukas, 2005) based on Polanyi’s (1967) stance that tacit knowledge is ineffable which is ascribed to something that is known but can only be described very vaguely. Schön (1983) views tacit knowledge as always richer in information than its articulated form due to the ‘language deficiency syndrome’ (a term coined in Kor, 2001). Multiple Model of Knowledge: Choo (1998) extends the explicit-tacit model by including cultural knowledge to explicate organisational knowledge. The three

categories of knowledge are interdependent and cultural knowledge constitutes cognitive and affective structures which are inherent in an organisation’s members and are persistently utilised for perception and justification purposes. Also, it is said to include assumption, beliefs, and values about the organisation and its environment. Boisot (1995) classifies knowledge into four different categories. The first is proprietary knowledge developed by an individual or a group, which is context specific, can be codified but not completely diffused because it becomes not meaningful when it is utilised in a different context. Next is idiosyncratic personal knowledge which is derived from personal experiences and thus cannot be codified nor diffused. On the contrary, public knowledge can both be codified and diffused in the form of textbooks or other printed resources. Lastly, commonsense knowledge which is social-contextual personal experiences that have been internalised, its ownership and meaning are shared by the community and thus, can be diffused but not codified. Spender (1996) also suggests four categories of knowledge: conscious knowledge which is explicit knowledge held by an individual; objectified knowledge which refers to explicit knowledge held by the organisation; automatic knowledge (preconscious individual knowledge) which experience is linked to intuition where experts linked experience to intuition where experts arrive at solutions without being able to explain how or the reasoning bit Davenport and Prusak (1998); collective knowledge, which is highly context specific (also known as proprietary knowledge in Boisot’s I-Space model), is manifested through the practice of the organisation (where practice is commonsense knowledge in Boisot’s I-Space model).

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•

•

Continuum Model of Knowledge: Polanyi (1967) and Leonard et al. (1998) present knowledge in a continuum model where one extreme end of the spectrum is a completely explicit form of knowledge (conscious) while the other end is, completely tacit (unconscious and experiential). Leonard et al argue that most knowledge lies between these two polarised points of extremes. Polayi’s (1962) explained the causes for language deficiency syndrome (Kor, 2001) which occurs in situations where the tacit predominates to the extent that articulation is virtually impossible. According to Polanyi, this is a phenomenon of an ineffable domain where we know something in our heads but find it beyond our description and this is further supported by Schön (1983). The reasons for such ineffability, in Polanyi’s point of view, are due to defective articulation and also the inability to co-ordinate the essential elements in a coherent manner. Tsoukas (2005) views organisational knowledge as a continuum with propositional knowledge on one end while, narrative knowledge on the other. Duality Model of Knowledge: As for Hildreth et al (2002), they come up with a duality model of the hard and soft (basically synonymous with the explicit and tacit terms which has been previously discussed) of knowledge which looks like the Chinese ying yang symbol. They maintain that these two forms co-exist and interwoven in all knowledge but with varying degree and in other words, knowledge is to some degree both hard and soft and thus categorisation of knowledge is not needed. Additionally, both are considered mutually dependent viewing the fact that when one increases, the other decreases.

The KNOWING Model of Knowledge: Polanyi (1962, p.vii) regards knowing as ‘an active

comprehension of the things known and action that requires skill’. The latter is echoed by Schön (1983) who views one’s knowledge as being in one’s actions. On the other hand, the component active comprehension refers to the formulation and application of theories (e.g. a set of rules considered as maxims or rules of thumb (Davenport and Prusak, 1998) which provide guidance for the doing followed by the interpretation of the experience. Polanyi gives an example of science which consists of a set of formulae that have a bearing on experience and without the exercise of operational skills on these formulae, which will be guided by maxims, there will be no shaping of a scientist’s knowledge. Another example given is the rules of art that do not determine the practice of art but merely serve as beacons to an art only if they can be integrated into practical knowledge of art. Seely Brown and Duguid (1998) add a social dimension to knowing by claiming that it not only focuses on what that is in the heads but the interactions with the things in the social and physical world.Seely Brown and Duguid (ibid) classify knowledge into the know-what (explicit knowledge) and know-how (which can have an explicit component). However, in this chapter, we have extended the know-what type of knowledge so that it encompasses both a mixture of effable (when it is conceptual) and ineffable knowledge. An example of the latter is the know-what-it-islike knowledge which is derived from sensations and is a non-inferential (Russell, 1961) or nonpropositional (Dretske, 1996) type of factual knowledge. Wittgenstein (Richter, 2006) used the term ‘private language’ to refer to private sensations which are incomprehensible to others except for the knower who alone knows the sensations to which it refers to. It has been argued that sensation on its own, is not knowledge (Russell, 1961), but a bearer of information (Dretske, 1996), and this is echoed by William James (1890) when he maintained that a pure sensation is an abstraction (in Chapter XVII Sensation, The Principle of Psychology). However, when it is interpreted or connected to 103

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an existing or new schema then it will be known as perceptual knowledge of the external world. Assume one has never tasted a kumquat but the know-what-kumquat taste-is-like will only exist after when given the opportunity. However, the question that is raised is how would the new taste be interpreted. If the sweet and sour citrus fruit taste is likened to that of a satsuma orange, then this means the taste of the kumquat (new schema) has been interpreted in the light of an existing schema (taste of satsuma orange). However, the sweet and sour taste cannot be described in a precise manner. This type of knowledge has phenomenal (‘what it is like’) features without conceptual constituents, which are also known as qualia (i.e. qualitative features) (Boghossian, 1995 in Pitt, 2004; Good, 2006; Tye, 2007) which are ineffable, subjective and very private (Dennett, 1992). According to Dretske (1996), perceptual knowledge is derived from qualia which are employed to identify what the object is. We shall not address the debate on whether qualia are real properties because the existence of such properties is still debatable in contemporary philosophy of mind. However, in this chapter, we shall adopt the stance that qualia are considered sensory perceptual knowledge that corresponds to sensations and the qualitative aspects of experience (Hubbard, 1996), which has critical or salient features (Raffman, 1993). Raffman argues that perceptual discrimination cannot be described but can be identified. He gives an example of the identification of a sound interval which could be performed by both the trained and untrained listener. Both could experience qualia but the trained one could go a step further by classifying the qualia of the stimulus based on the salient features (ibid). Know-how or also known as things you do with knowledge (Davenport and Prusak, 1998), relates to the procedural uses of knowledge, represents the possession of a skill, trained capacity, competence or a technique (Scheffler, 1965). Scheffler makes a distinction between this know-how (having a skill) and knowing that a skill is such

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and such (i.e. information about the skill). Also, he continues to claim that understanding or appreciation of a know-how is non-existent. However, skills can generally be honed through practice but one cannot practice know-that. Practice will effect an increase in certain skills to the point when it could be automatic. Know-that involves the propositional uses of knowledge and Scheffler (1965) not only views it as a verified belief but one which entails the ability to appropriately justify the belief. The know-that statement can also be utilised to indicate a norm. As an example ‘Everybody knows that they ought to bring their dinner money to school on Mondays if they are not on free meals’. The know-why type of knowledge, in this chapter, is akin to Theory Change and Mental Model Change, which provide a causal explanatory framework for a physical phenomenon so that it could be understood. This type of knowledge will be synonymous with knowledge of cause and effect. In this chapter, a summary of the discussed seminal and contemporary epistemologies including the attributes to describe the various categories of knowledge have been tabulated in Table 1. The 4 groups of attributes are: effability, codifiability, perceptual or conceptual, and social or personal. As discussed previously, effability relates to whether a piece of knowledge could be articulated while codifiability concerns its capture and embedment in physical representations such as artefacts, repositories, or signs system. Perceptual links to sensations and phenomenal features without any conceptual constituents. On the other hand, conceptual relates to mental functions such as problem solving, understanding, reasoning, logic, and etc. Social knowledge refers to knowledge on which cultural or social groups come to agree by convention or is constructed through social interaction (Wadsworth, 1996). This type of knowledge encompasses organisational knowledge as well. On the other hand, personal knowledge is either idiosyncratic or constructed

A Survey of Epistemology and its Implications

purely based on cognitive operations without entailing any social interaction.

CONCEPTUAL DESIGN OF ORGANISATIONAL INFORMATION & KM MODEL Seeing that knowledge is empowerment and it enhances an organisation’s competitive advantage (Wiig, 2004; Murmann, 2004; von Krogh and Roos, 1992;), organisational knowledge theorists and practitioners stress the need to know what knowledge is and how to manage as well as exploit it. This is the reason why the KM field has in recent years, sparked so much interests. The crux in Information and Knowledge Management is knowledge creation, information coding, and information sharing. In this chapter, we shall discuss a novel Organisational Information and Knowledge Management Model in Figure 11. which clarifies the differences between information and knowledge; provides mechanisms for individual knowledge creation, and information sharing. Here, we take the stance on knowledge which, in accordance with the Cognitive Approach resides in our conscious mental states or minds of the knowers (Glasersfeld, 1995; Davenport & Prusak, 1998). It has been internalised and is deemed to be meaningful or useful for potential mental or physical actions. Also, it is significant due to a particular context (Tsoukas, 2005) because of the meaning ascribed by the context (Wittgenstein in Richter, 2006). This stance contrasts with the conception of knowledge as being set out in artefacts, and documents (Boisot, 1998) or repositories and organisational routines, processes, practices, and norms (Davenport & Prusak, 1998). In this chapter, we view information as anything (including knowledge) that has been articulated, codified (embedded in documents, repositories, signals in multimedia sources, etc.), and is transferrable through hard or soft networks. According

to Davenport and Prusak (1998) a hard network has a tangible and definite infrastructure (e.g. cables, documents, satellite dishes, etc.) while the soft network is less formal and tangible (e.g. handing of notes or word of mouth).

Information and Knowledge Conversions Based on the categories of knowledge and their respective attributes which are tabulated in Table 1, Table 2, Table 3 we categorised three classes of information: coded (or printed) information which is all explicitly represented (e.g. books, pictures, icons, graphs, images, etc.); sensory information (relate to perception and transmitted through multimedia channels), and practice or art information (relate to skills and doing). Different possible conversions within an individual (as shown in Figures 5, 6, and 7).

Information: Nothing A piece of incoming information will either be ignored or rejected when it is identical or totally different from an existing mental structure; can neither be understood nor encoded (as discussed in ‘Cognitive Approach’), does not appear to be intelligible or plausible (Strike and Posner, 1985). This phenomenon could also occur when there are tenacious preconceptions (Ausubel, 1968) which contradict the new information, pervasive mental models, or cognitive load where the receiver has been bombarded with too much information at one single point in time.

Information: Information Information could be exaggerated, reduced when only the gist is abstracted. It will remain as information in the receiver’s head as long as it is acquired through rote learning without being internalised.

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Table 1. Seminal and contemporary epistemologies with attributes of knowledge

Table 2. Seminal and contemporary epistemologies with attributes of knowledge (continued)

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Table 3. Seminal and contemporary epistemologies with attributes of knowledge (continued)

Information: Knowledge

COGNITIVE OPERATIONS

Knowledge is not transferred to a learner but instead the learner builds a representation of the domain (Shuell, 1992). In this subsection we shall discuss the mechanisms (extracted from Kor, 2001) by which information can be converted to knowledge. The mechanisms will be coded into two categories namely cognitive operations, and experience which involves kinesthetics (doing) and perception.

Understanding Let us examine a classic notion of understanding put forth by Dewey (1933). Dewey first distinguished ‘information’ from ‘knowledge’ by arguing that ‘information is only knowledge as its material is comprehended’ (p. 78). In other words, ‘understanding’, here, is viewed as the bridge between information and knowledge. What then is ‘understanding’ from Dewey’s point of view? He

Figure 5. A schematic outline for Dewey’s process of understanding (adapted from Kor, 2001)

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Figure 6. Coded information Knowledge conversion)

Figure 7. Sensory/practice/art information Knowledge conversion

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explained that ‘understanding’ means that ‘the various parts of the information acquired are grasped in their relations to one another, a result that is attained only when acquisition is accompanied by constant reflection (will be further discussed in the ensuing subsection) upon the meaning of what is studied’ (pp. 78-79). This implies that understanding of a piece of information is said to be attained when one is able to grasp a coherent network of its components with reflection being instrumental in this process. We attempt to illustrate Dewey’s notion of ‘understanding’ by showing that Shuell’s (1992) notion of ‘understanding’ is in line with that of Dewey’s when he maintains that the relationships among the concepts and facts are an integral part of understanding. He cites some of the characteristics associated with the concept ‘understanding’: paraphrasing, summarising, and answering questions. However, Scardamalia and Bereiter (1991) adopt Piaget’s assimilation and accommodation perspective of understanding when they posit that understanding is a two-way interaction between prior knowledge and new material. Here, prior knowledge interprets new material and on the other hand, new material modifies prior knowledge.

Reflection Dewey (1960) defines reflection as ‘the kind of thinking that consists in turning the subject over in the mind and giving it serious and consecutive consideration’ (p. 3). To trigger reflection, one must first be placed in a novel, unfamiliar situation which causes perplexity and uncertainty (Dewey, 1960). Reflection that occurs prior to an event is said to be in the pre-reflective stage (Dewey, 1960) and in this preparatory stage, Boud et al. (1985a) claim that inquiry takes place. The post-reflective stage occurs after the event when the outcome is mulled over. Boud et al. (1985a) include a phase in between the two, which is during the actual occurrence of the event. Dewey (1960) prescribes several ways reflection can take place: compare

and contrast one’s knowledge in the form of a thing or event with that of an expert, compare a thing or event as it is before with what it is after. The Socratic method could also be employed to provoke reflection (Lepper, et al, 1993; Kor, 2001; Kor, et al, 2001) to challenge a learner’s belief by posing questions, leading him step by step till he recognises his ignorance or false belief thereby revise his belief or effect conceptual change. Experience alone is not the key to learning and reflection is one activity that could transform experience to learning (Boud, Keogh & Walker, 1985a; Dewey, 1960; Kolb & Fry, 1975). Kolb’s experiential learning cycle shows that concrete experience is turned into abstract concepts and generalisations through observations and reflection. Reflection facilitates effective problem solving (Collins & Brown, 1988; Lepper et al., 1993) and also leads to new understanding and appreciation (Boud et al., 1985b; Dewey, 1960). It helps one transcend beyond the surface learning, thus enabling one to grasp the deep meaning of a concept and to integrate it with prior knowledge through the assimilation and accommodation processes. Dewey (1960) maintains that reflection impels inquiry about the reliability of one’s belief, an evaluation of its value, the gathering of data to confirm or refute it, and the justification or rejection of its acceptance. Lessons could be drawn from COLA, a Cross Organisational Learning Approach which aims to engage an organisation (in the construction industry) in rigorous and continuous evaluative reflective practice that will result in organisational learning which can be generalised and transferred to other contexts and at the same time provide the essential flexibility to cope with changes in a dynamic environment (details of the approach can be found in Orange et al, 1998, 1999a, 1999b, 2000).

Reasoning Holyoak and Nisbett (1988) maintain that the act of reasoning involves the use of rules about

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events in a particular domain. Inferential rules are context dependent and they encompass general rules about causal relations that exist in that particular domain. Successful learning through reasoning, according to Vosniadou and Ortony (1989), largely depends on one’s ability to identify the most relevant bodies of knowledge that reside in the memory. In this section for reasoning, we shall explain several types of reasoning such as common-sense reasoning, scientific reasoning, and analogical reasoning: •

Common-Sense Reasoning

In Qualitative Physics, common-sense reasoning is typically the type of reasoning employed to reason about physical systems. Common-sense reasoning, according to Resnick (1989) is the act of drawing on the repertoire of commonsense knowledge about the physical world to predict and explain a phenomenon. Buchanan and Wilkins (1993) point out that inference drawn from common-sense reasoning is often ‘mentally effortless’. Forbus (1990) implies that with common-sense reasoning, one can ‘reason fluently’ about a phenomenon without first having to grasp its underlying formalisms; •

Scientific Reasoning

As mentioned in ‘Pragmatic Approach’, three types of reasoning involved in scientific methods (according to Peirce in Burch, 2006) are: abductive, deductive and inductive reasoning. Scientific reasoning generally entails experimentation and hypotheses formation (see Error! Reference source not found.). According to Klahr and Dunbar (1988), the conception of dual space search during scientific reasoning comprises an experiment search and a hypothesis search. In the former, hypotheses are first formulated based on prior knowledge, followed by conducting experiments to confirm or refute the hypotheses formed. As for the latter, the order of events is the reverse

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with experiments preceding the formulation of hypotheses and rule discovery through generalisation is its primary aim. As discussed previously, inductive reasoning involves the drawing of inferences to generate hypotheses, generalisations, or principles. Thus, this is exemplified by the experiment search that has just been mentioned. Holyoak and Nisbett (1988) cite instance-based generalisation as an example of rule discovery where inferences are drawn from one or more instances. On the other hand, deductive reasoning, on the other hand, involves inferences being made from general principles to particular cases. Mayer (1992) lists three types of deductive reasoning: categorical reasoning, conditional reasoning and linear reasoning. However, only the second category is relevant here because it assumes the cause and effect relationship. An elementary form of such logical reasoning to signify a conditional relation is If p, then q or if not p, then not q’ where ‘p’ is some antecedent condition while ‘q’ is some consequent condition; •

Analogical Reasoning

Analogical reasoning involves the transfer of relational information from a base domain to a target domain (Vosniadou and Ortony, 1989). Success of an analogical transfer depends on the perceived similarity between the domains. Vosniadou and Ortony emphasise the similarity in surface properties as well as the relational structure of the base and target domains while Gentner (1983) stresses the structural similarity. Surface similarity in problems refers to the surface content that is alike with common details shared while structural similarity refers to the correspondence between the relations of objects in one problem with that of another problem. In the problem-solving context, analogical reasoning is defined as a process which involves the abstraction of a solution strategy from a previous problem and relating it to the given information in the new problem to be solved (Mayer, 1992).

A Survey of Epistemology and its Implications

Mayer prescribes three conditions for a successful analogical transfer: recognition, which involves the identification of a potential analog (base) from which to reason; abstraction, where a general structure, principle or procedure from the base is abstracted; mapping, where knowledge acquired is applied to the target. He suggests that the mappings that can occur between two domains are of similar features and relations, or of dissimilar features but similar relations.

Association According to James (1890, Chapter XIV, Association of The Principles of Psychology), knowledge acquisition through association will occur if an incoming information, which appears as whole is analysed into parts (analysis process) or when separate pieces of information are linked together as composite wholes (synthesis process).

Problem Solving Vosniadou (1995) states that learning to solve problems entails the acquisition of certain strategies and algorithms which make it possible to devise and execute a solution plan. We shall discuss very briefly, the two broad categories of problems: well-defined and ill-defined problems: •

Well-Defined Problems

Well-defined problems are defined by four parameters: an initial state, goal state, set of operators, and path constraints (Newell and Simon, 1972; Greeno, 1976) with clearly specified solution steps. General Problem Solver (GPS) is a classic program developed for solving well-defined problems. It operates on problems that can be formulated in terms of objects and operators (Newell and Simon, 1972). VanLehn (1999) remarks that some basic concepts in GPS such as goals, subgoals, and operations have come to define a paradigm for understanding human problem solv-

ing. Well-defined problems such as the ‘cannibal and missionary problem’ require little or no prior knowledge but hinge mainly on reasoning. Here, general problem solving strategies could be used to enable the problem solver to traverse from the original to the final goal state. Problem-solving is seen as a search through a state space. The essential characteristics of a problem space are: an initial state of knowledge; a set of operators which can be used at any point to generate a neighbouring point (intermediate states); an end state of knowledge (Newell & Simon, 1972). A general strategy for this category of problem is means-ends analysis (Newell & Simon, 1972) which entails identifying the difference between the two states, and selecting the best action to reduce the difference. This is suitable for knowledge-lean domains. However, for knowledge-rich domains such as physics, prior knowledge is essential, and expertise involves acquiring a good repertoire of domain relevant knowledge as well as developing domain-specific problem-solving strategies (Vosniadou, 1995); •

Ill-Defined Problems

With ill-defined problems, little or no information is provided on the initial state, goal state, or operators (Kahney, 1993). The problem has to be defined by the solver himself. Greeno (1976) argues that some problems with goals which are vague, undefined or limitless can be regarded as quite well-defined as long as solvers can find a solution path out of the many possible ones and work towards achieving the specified goal.

Sensory Experience According to Augustine (Scheffler, 1965), knowledge acquisition requires a ‘personal confrontation’ with reality. The confrontation with the information from a source is via multi-modalities, which often results in different personal experiences. Every such experience involves an object (event, or state of affairs) which we interact with

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and manipulate using our senses (Wadsworth, 1996). Pendleburry (1996) states that the description of sense experiences consists of their sensory modality (e.g. auditory experience, gustatory experience, etc.) and contents of an object, event, or state of affairs). Nagel (1974) maintains that the description of the contents are objective (in terms of their physical properties such as size, shape, weight, etc.) while the qualia (already discussed in ‘Knowledge Management Approach: Pluralist Epistemology’) or character (according to Dretske, 1996) of the sensations themselves are subjective. Wittgenstein (Richter, 2006) used the term ‘private language’ to refer to private aspects of sensations, which cannot be understood by others except by the user who alone knows the sensations to which the language refers to. Polanyi (1962) argued that theories (also known as maxims in procedural knowledge found in Table 1, Table 2, and Table 3) are essential to provide guidance for the interpretation of our experiences. Perception will only lead to knowledge if there is prior cognitive structure for the perceived. Dretske (1996) describes the process by which perceptual knowledge is constructed. In this chapter, we shall use the example of a fragrant pink rose. The first step involved is seeing and smelling a fragrant pink rose (sensation). In this sensory phase, sensations carry information about the object, the rose (e.g: visual information is the rose’s colour, shape, size, orientation, etc.) and also carry the qualia or character (e.g. shade of pink, type of fragrance, type of smell, intensity of the smell, etc.). Next the cognitive mechanism is responsible for extracting and using essential information to generate the perceptual knowledge that ‘the rose is pink and fragrant’ with the condition that there is a relevant prior cognitive structure. Lovell (1974) defines several ways by which perceptual knowledge are constructed: through discrimination or differentiation between properties of objects and events, and generalisation which entails the abstraction of common and salient features.

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PRACTICE OR ART According to Ohlsson (1996), learning to do concerns skill acquisition. However, rules for learning to do (or rules of art) do not determine the practice of the doing or the art (Polanyi, 1962). Polanyi argued that the rules are mere maxims which serve as guides to the art (known as maxims of procedural knowledge in Table 1, Table 2, Table 3). Scheffler (1965) affirms that know-how cannot be appreciated and one cannot learn to understand it. He continues to state that know-how can be generally improved through practice but one cannot practise the know-that. Hubbard (1996) highlights the qualia aspect of know-how by adding that qualia can be educated, with examples given being ear training in musicians, palate training in wine training, and etc. As previously defined, know-how represents the possession of a skill, trained capacity, or competence. Scheffler (1965) differentiates between know how to and knowing how to do it well where the former seems relevant to a situation where training and required understanding is minimal. On the other hand, the latter concerns advanced skills which are generally built through training and practice by means of repeated trials and performances, and active engagement with experts who would help direct one’s attention to the essential salient features in the skills.

Knowledge: Information When knowledge in a knower’s head is expressed and codified then it is transformed into information and this is resonated by the following view, ‘knowledge can move down the value chain, returning to information and data’ (Davenport and Prusak, 1998). It will be knowledge to the knower but information to the receiver when it is articulated. However, for knowledge that cannot be articulated, a set of procedures or pointers could be provided to lead the receiver through a similar environment so as to try effect similar

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or approximate experiences. These experiences might not necessarily result in exactly the same type of knowledge because experiential knowledge is subjective and idiosyncratic (Shuell, 1992) so it depends on how the knower perceives and interprets it in the light of his or her prior knowledge (Strike and Posner, 1985; Vosniadou and Brewer, 1992). However, when the goals and salient features of the experiences have been highlighted then the expected outcomes will be similar or approximately the same.

Knowledge: Knowledge Knowledge is organic and it grows when it interacts with the environment (Davenport and Prusak, 1998). A new piece of incoming information could trigger off the following changes in an existing knowledge structure: accretion, tuning or structuring or restructuring (abandoning the old – Papert, 1980 in Cognitive Approach). Dewey (1902) stressed that it is imperative for knowledge to grow from experiences and Ausubel (1968) maintains that what the learner already knows influences learning. The ideas of ‘growth’ and ‘influence’ are combined together when Shuell (1992) views learning as knowledge being built on and influenced by the presence of prior knowledge. Jonassen (1992) refers this phenomenon as the reorganisation of prior knowledge on the basis of the newly acquired knowledge. However, Scardamalia and Bereiter (1991) adopt Piaget’s notion of assimilation and accommodation and view a two-way interaction between prior knowledge and the new material where the former interprets the latter while the latter modifies the former. Thus, prior knowledge is a crucial determining factor of the learning process and outcome (Ausubel, 1968; Duit, 1999). However, when knowledge stops evolving then it transforms into a dogma (Davenport and Prusak, 1998). The mechanisms for fostering an organic growth of an existing piece of knowledge are similar to the

ones discussed in the subsection on Information - Knowledge conversion.

INFORMATION AND KM IN AN ORGANISATION In information and knowledge management, the essential activities relate to codification, creation and transfer of information and knowledge. The discussion in this section will focus on knowledge creation, information sharing (between individuals and individuals, groups and groups, individuals and groups) which form the bases for the Organisational Information and Knowledge Management Model. As mentioned previously, the categories of information in this chapter are: coded (or printed) information; sensory information; and practice or art information.

Knowledge Creation (within an Individual) Nonaka and Takeuchi (1995) view every member in an organisation as knowledge workers where new knowledge always begins with an individual. An organisation ‘learns’ when individuals learn, and team learning is viewed as a fundamental unit of a learning organisation. Members of an organisation act collectively but they learn individually (Kleiner and Roth, 1998). Mechanisms for individual knowledge creation (informationknowledge and knowledge-knowledge conversions) have been discussed previously. For knowledge constructed from newly encountered coded (or printed) information, Shuell (1992) maintains that a learner ought to carry out various cognitive operations. Examples of such operations are: comprehension, introspection (or reflection), reasoning, inference, analysis, synthesis and problem solving (see Rummelhart and Norman (1977) classify three types of knowledge creation (see namely: accretion occurs within existing schemata through

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gradual addition of factual information interpreted in terms of relevant pre-existing schemata; tuning involves the slow modification and refinement of schemata through continual use, and, presumably, it is instrumental for the development of expertise; structuring involves the creation of new schemata to account for new information. However, a schemata will be restructured when it contradicts with the incoming information. Knowledge is created through manipulation and interaction with objects or events in the physical world, through our senses (Wadsworth, 1996). In order to derive perceptual knowledge from sensory experiences, it is essential to provide guidance (in the form of maxims) for the interpretation of our experiences. Appropriate environments for active exploration need to be set up, followed by highlighting the salient aspects of qualia for intended experiences. According to Gallagher and Reid (1981), knowledge is constructed from thinking about experiences with objects or events. When the coded information comprising maxims are acted out or put into practice then skills related knowledge will be created (see Figure 6 and Figure 7). Knowledge creation is not only facilitated by peer interactions but also vertical Figure 8. Individual-individual information sharing

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interactions (Hatano, 1993) which entails interactions with experts who are more mature (Schön, 1983). Some of examples of such interactions are observations, mentoring, apprenticeship, coaching, shadowing, and even collaborative inquiry.

Information Sharing Tsoukas (2005) claims that knowledge becomes organisational ‘knowledge’ simply by it being generated or developed, or transmitted by individuals within the organisation through knowledge creating activities. However, based on our information and knowledge conversions, everything that is transferable is information (effable and codifiable) so in our view, that there could only be organisational information in the physical or virtual environment while its collective knowledge resides in individual’s heads. Some of the information sharing mechanisms among individuals in a group, are through the typical easily coded and communicated forms, instruction, conferences, meetings, workshops, collaborative inquiry, collaborative projects or problem solving. However, sensory information, and art or practice information can only be shared

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through multimedia communication channels, narratives, analogies, metaphors, maxims or rules of the thumb (Davenport and Prusak, 1998), the setting up of similar environments for re-enacting the intended experience, highlight or ascribe modality (e.g. very great, great, same, small, very small) to the salient and critical aspects of qualitative phenomenal features known as qualia (Hubbard, 1996). However, sharing of information (individual-individual, individual-group, groupgroup) requires common language, negotiated or shared meaning, shared mental models, integration of perspectives or views made possible through dialogues (mentioned by Senge, 1990).

COMMUNITIES OF PRACTICE In a typical organisational information and knowledge management model, the Communities of Practice provides an environment for people to share knowledge (e.g. Lave and Wenger, 1991; Hildreth and Kimble, 2002), work, and communicate (Choo, 1998). Within the organisation, it is said to subsume groups or teams. The practice component in Communities of Practice (CoP) as-

sumes a situated meaning that is only meaningful when there is active social participation (Wenger, 1998), and it encompasses communal traditions which are linked to narratives (e.g. stories, anecdotes, examples, etc.) (Tsoukas, 2005). Engaging with experts in the community is the crux of CoPs (ibid). However, active social interaction often entails conflicts, negotiations, collaboration, etc. which forms the bases of shared understanding, meaning, and goals (Seely Brown and Duguid, 1991), also known as sensemaking (Choo, 1998). Generally, CoPs consist of shared trusts, belief, learned lessons, insights, narratives, anecdotes, and values which give its members, a sense of identity (Liebowitz, 2001; Seely Brown and Duguid, 1991; Tsoukas, 2005), and is central to the social construction as well as sustenance of soft knowledge (Kimble et al., 2002). According to Seely Brown and colleagues (1983), workplace learning is best understood in terms of communities that are being formed or merged together and its primary issue being ‘becoming a practitioner’ and not ‘learning about practice’. If an organisation values knowledge creation and information sharing, then it should provide a conducive ground to facilitate the growth and continuous improvement

Figure 9. Individual-Group (or Group-Individual), and Group-Group Coded Information Sharing

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Figure 10. Individual-Group Sensory/Practice/Art Information Sharing

Figure 11. An organisational information knowledge management model

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of CoP (McDermott, 1999) which if sustained, will be institutionalised (Tsoukas, 2005). Appropriate systems, processes, and tools ought to be designed and implemented to support information knowledge management (note: for a comprehensive overview of organisational and ICT tools developed by von Krogh and colleagues, please refer to Raimann, et. al, 2000, p.23) and have them embedded into the organisation’s business models and operations (Gavin, 1998). Undeniably, support, and incentives are necessary to stimulate individual knowledge creation (Wiig, 2004) through personal mastery which helps the individual clarify one’s personal vision, aware of the gap between the vision and the actual situation, followed by focussing one’s energies to close the gap (Senge, 1990). It is the organisation’s responsibility to foster a climate which could help seamlessly weave the principles of personal mastery into the organisation’s practice (ibid). Information brokering (note: the term used in Wolpert, 2003, is knowledge brokering) is essential to effect collaboration between individuals and groups facilitated by dialogues and discussion so that groups will be able to gain valuable insights which are not attainable individually (Senge, 1990). According to Senge (ibid), engineering knowledge is created when a new idea has been invented and proven to work in a controlled environment (e.g. laboratory in the Research and Development Department). The Kuhn paradigm shift

phenomenon will occur when the testing of an idea is followed by an invention, when replicated on a large scale, becomes innovation which will revolutionise the organisational business model, processes and operations. Figure 12 illustrates the information and knowledge cycle, which is an abridged form of the organisational information and knowledge management model. It entails the creation, application, codification, and transfer activities. When created knowledge is coded, it becomes information which can then be communicated and shared. Information sharing mechanisms are processes (i), (ii), and (iii) (as in Figure 11), which will facilitate schema change (Rummelhart and Norman, 1977) or cognitive growth (Piaget). When knowledge has been proven over time, and information is stable then, it could be disseminated to other organisations using the same transfer processes within the organisation and this is known as information diffusion.

CONCLUSION In our discussion we have demonstrated how the seminal and contemporary epistemologies could be synergised for the benefit of an organisation. The chapter shows that knowledge creation within individuals happens when there are information and knowledge conversions (within individuals) facilitated by reflection, reasoning, testing, acting

Figure 12. IKM Cycle

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out, perception, apprenticeship, and etc. In order to promote information and knowledge management, an organisation must provide incentives and motivation for individuals to create knowledge through continual learning, codifying information in multi-modal representations, providing the diverse means and resources for information sharing between: an individual and another individual; an individual or a group; a group and another group. This is followed by information diffusion. This chapter has also presented an Organisational Information and Knowledge Management Model which distinguishes the differences between information and knowledge, as well as mechanisms for the conversions between information and knowledge. For our future work, we will validate and revise this model by using it to investigate ways by which individuals as well as groups create knowledge and share information within an organisation.

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Chapter 7

An Ontology-Based Expert System for Financial Statements Analysis Li-Yen Shue National Kaoshiung First University of Science and Technology, Taiwan Ching-Wen Chen National Kaoshiung First University of Science and Technology, Taiwan Chao-Hen Hsueh National Kaoshiung First University of Science and Technology, Taiwan

ABSTRACT Financial statements provide the main source of information for all parties who are interested in the performance of a company, including its managers, creditors, and equity investors. Although each of these parties may have different perspectives when viewing financial statements, all parties are concerned with the financial quality of an enterprise, which requires carefully analyzing financial statements to estimate and predict future conditions and performance. When analyzing financial statements, due to the complexity of the task, even professional analysts may be subject to constraints of subjective views, physical and mental fatigue, or possible environmental factors, and are not able to provide consistent appraisals. As a result, researchers and practitioners have resorted to expert systems to imitate the decision processes and inferencing logics of financial experts.

EXPERT SYSTEMS An expert system is a computer program that is constructed by obtaining problem solving knowledge from a human expert and coding it into a form that a computer may apply to similar problems (Giarratano & Riley, 2005). The reliDOI: 10.4018/978-1-60566-701-0.ch007

ance on the knowledge of a human domain expert for the system’s problem solving strategies is a major feature of expert systems. It was developed by researchers in Artificial Intelligence in the 60s and 70s and was commercialized in the 80s. There have been many innovative applications in handling knowledge in business, management, science, engineering, medicine, and military. One of the latest comprehensive studies in the

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An Ontology-Based Expert System for Financial Statements Analysis

applications and methodologies of expert systems can be found in Liao (2005). An expert system is generally structured to consist of knowledge base, database, and rule interpreter (inference engine). The knowledge base holds the set of rules of problem-solving that are used in reasoning, which contains both factual and heuristic knowledge. Factual knowledge is the knowledge of the task domain that is widely shared, typically found in textbooks or journals, and commonly agreed upon by those knowledgeable in the particular field. Heuristic knowledge is the less rigorous, more experiential, more judgmental knowledge of performance. In contrast to factual knowledge, heuristic knowledge is largely individualistic; it is the knowledge of good practice, good judgment, and plausible reasoning in the field. In the terms of Artificial Intelligence, the former is the domain knowledge of a problem domain, and the latter is the operational knowledge that uses domain knowledge to reason to generate new facts. Most systems use IF-THEN rules to represent both types of knowledge. The database provides the context of the problem domain and is generally considered to be a set of useful facts. These facts are used to satisfy the condition part of the action rules. The rule interpreter is often known as an inference engine and controls the application of knowledge base via the facts of database to produce even more facts. One major issue with the traditional expert system methodology lies in the fact that all knowledge, whether it be domain knowledge or operational knowledge, is usually mixed together to form a “knowledge base”. Thus, both types of knowledge are usually blended together in knowledge rules. Such a design, as reported in many cases, may hinder knowledge engineers’ ability to express deeper relationships among knowledge items and, specifically, the knowledge that is semantic in nature. In addition, it may lead to inefficiency when the domain becomes large or the operational knowledge becomes complex (Lee & O’Keefe, 1996; Davis, 1990; Yao et al., 2003;

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Chan & Johnston, 1996). From the perspective of knowledge management and system management, it will be a better design by separating the domain knowledge from operational knowledge (Chan, et al., 2002; Bobillo et al., 2009). In the following section, we separate the inherent knowledge of financial statements from the knowledge of analytical process of rating assessment. The analytical process emphasizes very much on the “meaning” between accounting items, we thus recommend the application of Ontology methodology to develop the domain knowledge of financial statements. Ontology is more capable of establishing semantic relationships among accounting items, which are essential in addressing various managerial concerns during the reasoning process.

ONTOLOGY The word “ontology” was taken from philosophy, where it means a systematic explanation of beings. In the last decade, the Knowledge Engineering community has adapted the word “ontology” to refer to a systematic analysis and representation of knowledge of some domains of interest, so that it can be shared by others. The most often cited ontology definition is from Gruber, “… an ontology is a formal, explicit specification of a shared conceptualization” (Gruber, 1993). ‘Conceptualization’ refers to an abstract model of phenomena in the world by having identified the relevant concepts of those phenomena. ‘Explicit’ means that the type of concepts used, and the constraints on their use are explicitly defined. ‘Formal’ refers to the fact that the ontology should be machine readable. ‘Shared’ reflects that ontology should capture consensual knowledge accepted by the communities. While this is a very general definition, Noy at al. (2000) provide a more specification definition for application: “an ontology is a formal explicit representation of concepts in a domain, properties of each concept

An Ontology-Based Expert System for Financial Statements Analysis

describes characteristics and attributes of the concept known as slots and constrains on these slots”. Swartout et al. (1997) relate ontology to knowledge base by saying that “an ontology is a hierarchically structured set of terms to describe a domain that can be used as a skeletal foundation for a knowledge base”. In actual applications, an ontology represents a set of vocabulary and related content theory (Chandrasekaran et al., 1999). The vocabulary consists of terms used for capturing the conceptualization of the domain, and the content theory refers to the identification of specific classes of objects, their properties, and their relationships that exist in the domain. Both are applied together to express entities and relationships between entities of a domain and establish a meta-information that could be shared and reused. Some recent applications of Ontology in dealing with semantic knowledge content can be found in Chan et al. (2002), Bobillo et al. (2009) and Morbach et al. (2007).

FINANCIAL RATIOS FOR ANALYZING FINANCIAL STATEMENTS In analyzing financial statements, there are in general three methods, comparative financial statement analysis, common-size financial statement analysis, and ratio analysis (e.g. White et al., 2003; Soffer & Soffer, 2003). Comparative financial statement analysis is conducted by setting all financial statements side by side for reviewing changes and trends, which is normally based on a year-to-year or multiyear values in individual categories. A comparison of statements over several years would reveal direction, speed, and extent of trends. Common-size financial statement analysis is an inquiry into the internal structure of financial statements by examining the proportion of a total group or subgroup an item represents, and it particularly focuses on sources of financing and composition of investments. This method is especially useful for inter-company comparisons

because financial statements of different companies are recast in common-size format. Probably the most widely used financial analysis technique is Ratio Analysis. This method expresses ratio relationships between items on financial statements, and provides “meaning” for interpreting status of various business aspects. Analysts usually group related ratios together to quantity performances of a business, which will lead to the understanding of risks situation, trend of business development, identification of any peculiarities, and eventually the decision on credit and investment standing. Given the large quantity of items in financial statements, a long list of meaningful financial ratios can be derived. These ratios can be classified into two types: static and dynamic ratios. Static ratios are calculated from balance sheet data, and present a “snapshot” of the business; current ratio and net worth to total debt ratio are typical ones. Dynamic ratios are based wholly or in part on matters of financial movement; sales to inventory ratio and sales to receivable ratio fall into this category. According to a recent report by Duft (2007) most analysts prefer to classify ratios according to managerial functions, which include categories of: profitability, liquidity, leverage, and efficiency. Profitability ratios measure management’s ability to control expenses and to earn a return on the resources committed to the business. Liquidity ratios measure a firm’s ability to meet its current obligations; a business is believed to be in trouble if it is unable to meet its current obligations as they become due. Leverage ratios measure the degree of protection of suppliers of long-term funds and can also aid in judging a firm’s ability to raise additional debt and its capacity to pay its liabilities on time. Efficiency ratios measure how effective a company is in utilizing its resources. Due to the popularity of this method, we apply financial ratios to develop the operational knowledge for assessing financial quality of enterprises.

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DOMAIN KNOWLEDGE OF FINANCIAL STATEMENTS There are four basic financial statements: balance sheet, income statement, cash flow statement, and statement of retained earning. Each of these financial statements is designed for different purposes. Balance Sheet, also referred to as statement of financial condition, reports on a company’s assets, liabilities and net equity as of a given point in time. It indicates the book value of an enterprise by presenting the compositions of assets and liabilities. An Income Statement, also referred to as Profit or loss statement, reports on a company’s results of operations over a period of time. It presents revenue from sale of products and services and related expenses, and indicates how Revenue is transformed into Income. Cash flow statement reports on a company’s cash flow activities, particularly its operating, investing and financing activities. Statement of retained earning explains the changes in a company’s retained earnings over the reporting period. One can see that the domain knowledge of financial statements should encompass all four statements. However, for the reason of saving space, in the following, we consider only balance sheet and income statement to demonstrate the analysis of the domain knowledge and the development of its knowledge structure. From the knowledge point of view, it is clear that, just the monetary figures of items on Balance Sheet do not provide any insight on how various funds are utilized, nor can it answer any question about profitability, liquidity, leverage, and efficiency. To answer these questions, one needs to know how each number has come about, and only through the trace of detailed breakdown of each transaction can provide the information. Thus, the required knowledge lies in the “associations” between items of Balance sheet and that of Income Statement; this is where breakdowns of revenues and expenses of various transactions are recorded. Hence, it is

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essential to understand the relationships between items of the two statements to understand the domain knowledge.

RELATIONSHIPS BETWEEN ACCOUNTING ITEMS Accounting is built upon the fundamental accounting equation: Assets = Liabilities + Owners’ Equity This equation must always remain in balance. This means that every transaction that is recorded in accounting records must have at least two entries; if it only has one entry the equation would necessarily be unbalanced. For that reason our modern accounting system is called a dual-entry system. While the balance sheet reports assets and financing of those assets at a point in time, the income statement reports revenues and expenses over a period of time. Any two consecutive balance sheets are connected by the corresponding income statement. The relation between balance sheet and income statement can be examined by decomposing the accounting equation that is shown in Figure 1. Revenue and expenses are recorded to Net Income (or net loss) account, which is a temporary account of income statement. At the end of the accounting period, revenues and expenses are cleared for the new accounting period. Their balances flow into Retained Earnings, and then to Owners’ Equity. From the Figure, one can see that any increase in expenses would incur the similar amount in liabilities, so that the accounting equation can be balanced. However, this is not the only way to keep the equation in balance; other ways include the reduction of assets, partly reduction of assets and partly increase of liabilities, and other possible combinations. Hence, the possible combinations for maintaining the fundamental accounting equation in balance present basic relationships between items of bal-

An Ontology-Based Expert System for Financial Statements Analysis

ance sheet and income statement, and they make up the domain knowledge of financial statements. The construction of financial statements nowadays must follow very rigorous guidelines that are demanded by the accountancy profession. In general, items in each statement are expressed in terms of accounting categories and in the form of category tree. Each category may consist of further subcategories, until it reaches line items of the last layer that represents actual substances. The Owners’ Equality of Figure 1 presents part of its category tree, where the indicated Revenue and Expense item can be further expanded into various actual revenues generated and detailed expenses for operating the business. From the conceptual point of view, the naming practice of present accounting items is in fact embracing the unifying abstract notion of objects, where an object represents a collection of individuals with common features, which is normally called a class in system design. In the following analysis, we will apply the object-oriented concept to develop the taxonomy of domain knowledge.

STRUCTURAL AND SEMANTIC RELATIONSHIPS From the ontological prospective, the relationships between accounting items can be described in terms of structural relationship and semantic relationship, Figure 2 provides a simplified example. The structural relationship can best be described as part-whole relationship for items of two consecutive layers of the category tree. Items of the upper layer represent the “whole” concept, while its subcategories of the lower layer represent the “part” component. For example, as shown in Figure 2, the Assets category of the Balance Sheet consists of three subcategories: “Current Assets”, “Long-Term Assets”, and “Other Assets”. One could say that these three parts make up the whole of the Assets category, or each of the three categories represents one part of the Assets category. This two-way structural relationship extends all the way to the line items of the last layer, and one could identify the relationship between two items through intermediate layers. In addition to the part-whole structural relationship, another relationship between items is semantic in nature,

Figure 1. The decomposition of accounting equation

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which serves to represent managerial concepts in managing business performance. These semantic relationships are formed centering on interpreting efficiency and effectiveness of resources utilization, and can be further formulated into financial ratios for quantifying the measurement of performances. For example, as shown in Figure 2, the Expense under Owners’ Equity could affect Current Liability of the Liability category and/or Current Assets of the Assets category in different way; thus, Expense is semantically interpreted differently to the two categories. In analyzing financial statements, one important semantic relationship is the causal relationship between items, which may present the inputoutput, source-outcome, and/or cause-effect association. For example, in Balance Sheet, the Liabilities and Owners’ Equity are generally treated as the “components” for building up the

Assets of an enterprise, and the Assets itself is treated as the “source” for generating Operating Revenue on the Income Statement. In an Income Statement, the Expenditure is treated as the “cost” of Operating Revenue. Thus the semantic relationship could relate items across different parts of a statement or items from different statements. In summary, the basic structural relationships could be expressed with: • •

hasComponent (an item is made up of sub-items) isA (an item is a member component of another item).

The scope of expression of semantic relationships could be as diverse as needed in conveying the concepts of managerial concerns for assessment. It consists of relationships for expressing

Figure 2. A simplified structural and semantic relations between items

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the commonly accepted causal effects, and that for facilitating the application of the adapted method for analyzing financial statements. The application of the adapted analytical method may dictate the extent of the semantic knowledge that is needed. For the application of financial ratios, the central concept is the definition of numerator and denominator relationship for each ratio. Table 1 provides some of the basic semantic relations and corresponding examples. Figure 3 presents a simplified semantic network for Balance Sheet and Income Statement, which illustrates their relationships in terms of both structural and semantic relationships. This figure indicates that the class “Balance Sheet” has three components (hasComponent): Assets, Liabilities, and Owners’ Equity, and the “Income Statement” also has three components (hasComponent): Operating Revenue, Expenditure, and Surplus. With each component being a unique conceptual grouping, they are treated as classes. Between the two statements, the Assets class has sources from (hasSourceFrom) Liabilities and Owners’ Equity. The Assets class is also the source for (isSourceFor) generating Operating Revenue. The Expenditure class is the cost for (isCostFor) generating Operating Revenue, keeping Liabilities, or decreasing Assets. The Surplus class is calculated from Operating Revenue and Expenditure, so can be expressed as another type having sources from (hasSourceFrom) them.

DOMAIN KNOWLEDGE STRUCTURE WITH FINANCIAL RATIOS In developing the complete domain knowledge, the concepts of financial ratios must also be represented along with the inherent structural and semantic relationships; so that it could facilitate the reasoning process for evaluating business performance. The four performance categories presented above are: short-term repayment ability, long-term repayment ability, operation efficiency, and profitability. Each category may consist of more than one ratio with financial items from both statements. For the illustration purpose, we based on the experience of a senior accountant in determining the ratios that are relevant for each category. The composition of each category is defined in the following: Short-term repayment ability: (1) Current ratio=Current assets / Current liabilities (2) Net Operating Cycle (or Cash Conversion Cycle) = Average receivables collection period + Average processing time for inventory - Payables payment period Long-term repayment ability: (1) Equity ratio=Total Equity / Total assets

Table 1. Some of the basic semantic relations and corresponding examples Semantic relations

Examples

isSumOf (the value of an item is the sum of values of other items)

Current asset is the sum of cash, account receivable, inventory, and other related accounts.

isCostFor (an item is considered as a cost of another item)

Depreciation is the cost for some long-term asset like buildings.

isSourceFor (an item is considered as the source of another item)

Revenue is the source of cash or account receivable.

hasSourceFrom (an item is sourced from other items)

Asset is sourced from Liability and Holder’s Equity

hasDenominator (an item serves as the denominator of a ratio)

Total asset is the denominator of the equity ratio.

hasNumerator (an item serves as the numerator of a ratio)

Net income is the numerator of return on sale.

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Figure 3. A simplified relationships between balance sheet and income statement

(2) Time Interest Earned = (Net Profit Before Taxes + Interest paid) / Interest paid (3) Long-term funds to fixed assets = (Owners’ equity + Long-term interest - bearing liabilities)/ Net fixed assets Operation efficiency: (1) Total assets turnover (times) = Net sales / Average total assets (2) Fixed assets turnover (times) = Net sales / Average fixed assets (3) Accounts Receivable Turnover = Net credit sales / Average accounts receivable (4) Inventory turnover (times) = Cost of goods sold / Average inventory Profitability: (1) Return on sale = Net income / Net sales (2) Return on equity = Net income / Average owners’ equity (3) Return on total assets = {Net income + Interest expense x (1 - Tax rate)} / Average total assets

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Each ratio, as shown, has its own numerator and denominator, which may be represented by one or more items; with the exception of Net_Operating_Cycle whose denominator is taken as 1. In building the knowledge taxonomy and expressing the relationships for the knowledge structure, each ratio class is represented by two slots; (hasNumerator) and (hasDenominator). Each slot is related to specific item (items). For example, total assets turnover ratio is calculated by dividing the sale revenue in income statement as numerator with total assets at balance sheet as denominator. Figure 4 shows a semantic network of twelve ratio classes along with relevant classes of the two statements, following the convention of Protégé to name classes as either Abstract or Concrete class; the former may have instances while the latter may not.

DOMAIN KNOWLEDGE MODULE WITH PROTEGE With the domain knowledge represented in structural relationships, semantic relationships, and financial ratios, we apply Ontology to model the domain knowledge and represent the knowledge

An Ontology-Based Expert System for Financial Statements Analysis

Figure 4. A semantic network of domain knowledge with financial ratios

content in terms of classes, relations, attributes, and individuals. The Ontology-based knowledge model facilitates us to apply Protégé (Grosso et al., 1999; Stanford Medical Informatics, 2006) as the platform to develop the domain knowledge module of the expert system. Protégé was developed by Medical Informatics of Stanford University, and has been recognized as a major platform for developing ontology-based knowledge system. This platform provides features that allow users to develop knowledge taxonomy and express relationships between categories with ease. It also provides customization features to allow users with maximum flexibility in building knowledge bases. In addition to these features, this platform

provides a set of Open Source API, so that users can design their components in JAVA as “plugins” for the customization of knowledge base. The elements of Protégé’s knowledge model consist of Classes, Slots, Facets, and Instances. A Class describes a category of objects or concepts that are of the same properties. Instances are the actual entities of a class. Slots represent attributes of classes and instances, and facets express relations with other class (slot) and other information about slots. In addition, the Constraints box of the Class Editor allows users to express more complicated relations that can not be presented with Facets.

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Figure 5. Knowledge structure for assets and equity

Figure 5, 6, and 7 show the three classes of the first layer of the complete taxonomy in Protégé separately. Figure 5 presents the details of the class of Economic Resources and their Right of Demanding, Figure 6 for the Income class, and Figure 7 for the Financial Ratio class. Each class has its own slots (attributes) and relationships with other class through Facet, and may consist of more than one subclass. An example for expressing “meaning” for some commonly used terms in Protégé is given in Figure 8. Under Other Facets, the term “form”

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of assets can refer to either “tangible” or “intangible”. The term “liquidity” could mean “current”, “non-current”, or “others”. The term “activity_ of_bankroll” could be either “investment” or “finance”.

OPERATIONAL KNOWLEDGE IN DECISION RULES Operational knowledge, in this case, is the diagnostic knowledge, which takes domain knowledge

An Ontology-Based Expert System for Financial Statements Analysis

Figure 6. Knowledge structure for income

Figure 7. Knowledge structure for financial ratios

of financial statements and infers a conclusion on the financial quality of a company. It normally consists of a series of diagnostic steps while examining items of financial statements, and it may be subjective in nature; depending on the personal experience of the analysts. Our operational knowl-

edge of analyzing financial statements comes from a certified public accountant, who has more than 20 years of experience in this area. Normally, an analyst would examine some major items to start with, and decide if other relevant items are needed for examination to verify one’s professional con-

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Figure 8. Expression of meanings of some common terms

Figure 9. Decision rules for the four ratios of operating efficiency

jecture, which could lead to a conclusion in the end. There may be many intermittent examinations of related items before a final conclusion can be 136

reached. In applying financial ratios, an analyst normally evaluates each financial ratio into one of the five quality grades: 5 for being excellent,

An Ontology-Based Expert System for Financial Statements Analysis

Figure 10. Decision rules for rating the financial quality of a company

4 for being good, 3 for being above average, 2 for being acceptable, 1 for being unsatisfactory, and use these quality levels for making intermittent decisions and final decisions. We adopt this 5-grade convention in the following. Among the many methods that are used to represent this step-by–step knowledge, the rulebased methodology, with IF-THEN structure, is generally easier to apply and be understood than others. In the following, we will apply rule facility of JESS (Java Expert System Shell) to develop the operational knowledge module (Friedman, 2003). JESS is a Java based expert system shell, and was developed by Sandia National Laboratories. The decision process of operational knowledge starts with the evaluation of individual financial ratios of each category, then proceeds to evaluate each

category as a whole by assessing the combined quality of its ratio members, and finalizes the overall evaluation in the end by assessing the combined ratings of the four categories. The complete operational knowledge is made up of decision rules for each of the four categories and that for the overall rating. Figure 9 presents an example of decision rules for Operating Efficiency, which consists of four ratios: Total_Assets_Turnover (TAT), Accounts_Receivable_Turnover (ART), Inventory_Turnover (IT), and Fixed_Assets_Turnover (FAT). Figure 10 presents final decision rules for an actual case. It takes ratings of the twelve ratios as inputs, and presents the rules for rating each of the four categories of performance and that for the overall performance grade. As shown in the

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figure, the inputs of twelve ratios lead to category ratings as: Short-term Liquidity = Good, Longterm Solvency = Average, Operating Efficiency = Good, Profitability = Average. The overall grade of the company’s financial quality is based on these ratings, and it is “Average” in this case. The complete decision rules are made up of all possible combination of rules, which take into consideration each of the five grades for each ratio in the rule, and the same for every performance category.

THE COMPLETE EXPERT SYSTEM The complete system development framework is shown in Figure 11. This framework is made up of modules of knowledge base, inference engine, explanation facility, and user interface. The knowledge base module is the core of the system; it consists of domain knowledge of financial statements and operational knowledge of analysts’ decision rules. The user interface accepts the original facts from financial statements and invokes the inference engine to activate the decision rules in the operational knowledge base. Figure 11. The complete expert system

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The decision rules will then access relevant data residing in the domain knowledge, which may invoke further decision rules. The final conclusion of assessments will be presented through the user interface, along with relevant explanation from the explanation module. The entire framework is built in JAVA environment, which is essential for achieving integration between modules during execution. The domain knowledge is built in Protégé, and the operational knowledge is built with JESS. Protégé is JAVA based and is its open architecture makes it possible to communicate with other JAVA based systems through Plug-ins. For the case with JESS, the Plug-in JessTab (Eriksson, 2003) was specifically designed to serve as a bridge to allow Protégé to communicate with JESS. API of Protégé is able to work with functions of JessTab. During execution, functions of JessTab can manage Protégé’s knowledge base, and allows rules in JESS to access contents of the domain ontology in Protégé. Thus, the inference engine of JESS is able to function as the inference engine of the combined expert system. Figure 12 presents the codes that bring into the JAVA system JESS, JESS tab, and domain knowledge from Protégé.

An Ontology-Based Expert System for Financial Statements Analysis

Figure 12. JAVA codes for declaring JESS, JESS tab, and domain contents of Protégé

CONCLUSION This system was implemented to analyze Balance Sheets of a number of well-known high tech companies in Taiwan; they are available in public domain from Taiwan Stock Exchange. The resulting financial ratings are consistent with that of our analyst. Of course, for this system to be able to have the full capacity to assess the financial position of a company, its domain must be expanded to include cash flow statement and statement of retained earning, and the corresponding operational knowledge must also be added. This study has demonstrated the feasibility of developing expert systems for financial statements analysis by handling its knowledge into two modules: domain knowledge and operational knowledge. The domain knowledge of financial statements, which is inherent and well accepted in the discipline, is separated from the analytical procedures of operational knowledge, which may vary with different analysts. This approach will greatly facilitate the reuse of domain knowledge, and encourage, especially, small and medium sized companies to apply expert system technology; due to the reduced cost of knowledge development. In addition, the application of the Ontology approach to model the domain knowledge, which is semantic in nature, represents an innovative approach in modeling knowledge content of many business domains that are traditionally descriptive. The Ontology methodology will enable companies

to build semantic business relationships that are generally beyond the capability of present database systems.

REFERENCES Bobillo, F., Delgado, M., Gomez-Romero, J., & Lopez, E. (2009). A semantic fuzzy expert system for a fuzzy balanced scorecard. Expert Systems with Applications, 36(1), 423–433. doi:10.1016/j. eswa.2007.09.020 Chan, C. W., & Johnston, M. (1996). Knowledge modeling for constructing an expert system to support reforestation decisions. KnowledgeBased Systems, 9(1), 41–59. doi:10.1016/09507051(95)01019-X Chan, K. C., Peng, Y., & Chen, L. L. (2002). Knowledge acquisition and ontology modeling for construction of a control and monitoring expert system. International Journal of Systems Science, 33(6), 485–503. doi:10.1080/00207720210133679 Chandrasekaran, B., Josephson, J. R., & Benjamins, V. R. (1999). What are ontologies, and why do we need them? IEEE Intelligent Systems, 14(1), 20–26. doi:10.1109/5254.747902 Davis, J. S. (1990). Effect of modularity on maintainability of rule-based systems. International Journal of Man-Machine Studies, 32(4), 439–447. doi:10.1016/S0020-7373(05)80141-5

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Duft, K. D. (2007). Financial ratio analysis: An aid to agribusiness management. Retrieved from http://classes.ses.wsu.edu/EconS452/sectionDuft/docs/FinRatio1.pdf Eriksson, H. (2003). Using JessTab to integrate Protégé and Jess. IEEE Intelligent Systems, 18(2), 43–50. doi:10.1109/MIS.2003.1193656 Friedman, E. (2003). JESS in action: Rule-based systems in Java. Greenwich, CT: Manning Publications. Giarratano, J., & Riley, G. (2005). Expert Systems Principles and Programming. Melbourne, Australia: Thomson Course Technology. Grosso, W. E., Eriksson, H., Fergerson, R. W., Gennari, J. H., Tu, S. W., & Musen, M. A. (1999). Knowledge modeling at the millennium: The design and evolution of protege-2000. SMI Technical Report, SMI-1999-0801. Stanford, CA: Stanford Medical Infomatics. Gruber, T. R. (1993). A translation approach to portable ontology specifications. Knowledge Acquisition, 6(2), 199–221. doi:10.1006/knac.1993.1008 Lee, S., & O’Keefe, R. M. (1996). The effect of knowledge representation schemes on maintainability of knowledge-based systems. IEEE Transactions on Knowledge and Data Engineering, 8(1), 173–178. doi:10.1109/69.485645 Liao, S. H. (2005). Expert system methodologies and applications: A decade review from 1995 to 2004. Expert Systems with Applications, 28(1), 93–103. doi:10.1016/j.eswa.2004.08.003 Matsatsinis, N. F., Doumpos, M., & Zopounidis, C. (1997). Knowledge acquisition and representation for expert systems in the field of financial analysis. Expert Systems with Applications, 12(2), 247–262. doi:10.1016/S0957-4174(96)00098-X

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Morbach, J., Yang, A., & Marquardt, W. (2007). OntoCAPE: A large-scale ontology for chemical process engineering. Engineering Applications of Artificial Intelligence, 20(2), 147–161. doi:10.1016/j.engappai.2006.06.010 Nedovic, L., & Devedzic, V. (2002). Expert Systems in Finance-a Cross-Section of the Field. Expert Systems with Applications, 23, 49–66. doi:10.1016/S0957-4174(02)00027-1 Noy, N. F., Fergerson, R. W., & Musen, M. A. (2000). The knowledge model of Protege-2000: Combining interoperability and flexibility. Proceedings of the 2th International Conference on Knowledge Engineering and Knowledge Management (pp. 2-6). Riviera, France. Pacheco, R., Martins, A., Barcia, R. M., & Khator, S. (1996). A hybrid intelligent system applied to financial statement analysis. Proceedings of 5th Fuzzy IEEE (pp.1007-1012). New Orleans. Soffer, L., & Soffer, R. (2003). Financial statement analysis: A valuation approach. Upper Saddle River, NJ: Prentice Hall. Stanford Medical Informatics. (2006). The Protege ontology editor and knowledge acquisition system. Retrieved from http://protege.stanford.edu Swartout, B., Ramesh, P., Knight, K., & Russ, T. (1997). Toward distributed use of large-scale ontologies. In AAAI Symposium on Ontological Engineering (pp. 33-40). Stanford, CA: AAAI Press. White, G. I., Sondhi, A. C., & Fried, D. (2003). The analysis and use of financial statements. New York: John Wiley & Sons. Yao Tsung Lin, Y. T., Tseng, S. S., & Tsai, C. F. (2003). Design and implementation of new object-oriented rule based management system. Expert Systems with Applications, 25(3), 369–385. doi:10.1016/S0957-4174(03)00064-2

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Chapter 8

Knowledge Democracy as the New Mantra in Product Innovation: A Framework of Processes and Competencies Angelo Corallo University of Salento, Italy Marco De Maggio University of Salento, Italy Alessandro Margherita University of Salento, Italy

ABSTRACT In this chapter we carry out a critical analysis of “knowledge democracy” as a new mantra or buzz-word in product innovation leadership. A new paradigm has revolutionized the traditional process of invention, which was previously associated with a hierarchical dissemination of new ideas and competitive hoarding of knowledge assets. This chapter contends that at this environment has been replaced by a collaboration economy (based on so-called “wikinomics”) in which democracy governs the process of knowledge creation and its strategic application. Leadership in product innovation does not rely on the innate internal qualities of organizations, but on the collaborative contribution of stakeholders in many of the activities that make up the NPD lifecycle. The authors suggest a new approach to mitigate factors that can otherwise reduce the value of the NPD process. The chapter then examines how to promote such open collaboration through the development of a new managerial mindset, the acquisition of new distinctive competencies, the development of new organizational models, and the management of new collaborative technologies. The authors’ proposed framework of processes and competencies offers the potential for organizations to meet these needs. DOI: 10.4018/978-1-60566-701-0.ch008

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Knowledge Democracy as the New Mantra in Product Innovation

INTRODUCTION As the world runs into the 21st century, knowledge replaces capital as key production resource and driver of competitive advantage for companies. Learning how to identify, manage, and foster knowledge is thus vital for organizations who hope to compete in a fast-moving global economy (Davenport & Prusak, 1997). Successful organizations place people at the forefront, while creating an environment conducive to develop and leverage the idiosyncratic knowledge, competencies and motivation of each individual. The worldwide distribution of markets and industries represents another challenge and threat for today’s organizations since it asks a more open and boundaryless perspective of value creation for a larger number of stakeholders. The commitment to developing knowledge and competencies must be thus complemented by the capability to meet different expectations and link dispersed potential, expertise, and initiatives in a continuous process of organizational learning and renewal. The management of strategic knowledge is a point at the top of corporate strategic agenda and a concern for all the levels of an organization. In fact, managing knowledge to streamline innovation requires a strong integration of organizational, process, and technology-related issues. Knowledge management practices can foster innovation in a variety of models. In particular, the potential of communities becomes increasingly recognized in the effort of leveraging collaboration and collective intelligence, both in advanced technology sectors and in traditional industries. Information and Communication Technology (ICT) had a major role in creating such state of things. During the last decades, the diffusion of ICT has indeed facilitated the emergence of networks as socioorganizational entities based on participation, cooperation and co-creation of value (Burt, 2000; Cummings & Cross, 2003). The new paradigm revolutionized the traditional process of generating new ideas for products and services, mostly based

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on internally-bounded and centralized models. Today, the “collaboration economy” or “wikinomics” boosts the principle of democracy which governs the creation and application of strategic knowledge (Tapscott & Williams, 2006). The traditional drivers of competitiveness are replaced by capabilities such as peering, i.e. eliminating hierarchies in favor of meritocracy, quality of ideas and contributions; openness, i.e. enhancing participation and involvement of any stakeholder who has something to contribute; sharing of strategic assets such as ideas, intellectual property, and software in order to facilitate participation and cross-fertilization; and global action, to use and capitalize increasingly distributed resources (Hiltz, 1998; Johnson & Johnson, 1996). In this scenario, leadership in innovation cannot rely exclusively on the internal abilities of organizations committed in satisfying external needs. Indeed, it is rather based on the contribution of customers, suppliers, partners, and codevelopers involved in activities such as market research, design and development, prototyping, testing and production. The ultimate purpose is to allow more targeted product customization, reduce time to market and cost of resources, and minimize investment risks (Chesbrough, 2006). However, adopting a perspective of networking and distributed knowledge creation requires competencies needed to integrate new organizational forms and technology systems. Besides, traditional workflows should be redesigned to allow more open, dynamic and interactive product innovation processes. In this endeavor, a need arises to conceive integrated framework to support the transition, an effort which is even more challenging for small and medium organizations characterized by limited budget and resources. The focus of this chapter is thus threefold: (a) the business and technological capabilities critical to leverage knowledge democracy for innovation purposes; (b) the organizational models and collaborative processes required to enhance the generation and development of new ideas; and c) the strategy for

Knowledge Democracy as the New Mantra in Product Innovation

integrating competencies and processes in a holistic model for product innovation. Next section discusses some literature contributions focused on innovative communities and knowledge management. Section three presents ten success cases which can be considered as illustrative experiences of knowledge democracy. Section four extracts insights and lessons learned from literature and cases investigated to define an integrative model. Finally, section five summarizes and concludes the chapter.

DISCUSSION OF THE LITERATURE There are two major areas of interest for the purposes of this chapter. Given the focus of the investigation, the literature on new product development processes and technologies represents a first field of concern. Second, the theory on innovation communities and social networks represents a key domain for the role of extended collaboration to foster knowledge creation and generation of new ideas. An initial analysis is needed about the meaning of knowledge democracy and its consequences at strategic and operational level. The concept of knowledge democracy has been mainly used in macro-economic discussions related to knowledge and digital divide. In particular, it is generally consistent with the “community learning perspective” pointed by Andrew Michael Cohill (2000) who refers to “knowledge democracy” as a matter of knowledge acquisition and the skills required to manage that knowledge towards human, civil and social growth. Inspired by the vision of Ray Connor, a Member of Parliament in Queensland (Australia), Cohill’s opinion is about the knowledge democracy as the objective of communities committed in solving knowledge divide at geographical or social level. This is related to the degree of participation of each individual “based heavily on one’s ability to acquire information, turn that information into knowledge, and use that knowledge to improve one’s own socioeconomic

situation or that of someone else in the community” (Cohill, 2000). In this sense, the democracy stays in the need for governing institutions to ideally create the same conditions of access to technology (mainly ICT) and relevant knowledge for all the countries and citizens worldwide. Moving from this conceptualization, the meaning of knowledge democracy used in this chapter is slightly different. The focus is still on the “constructive” role of communities, but the attention is here shifted from the aim of social growth toward the purpose of stimulating innovation processes. Accordingly, the concept of knowledge democracy is adopted to refer to the emergence of collective thinking and distributed knowledge creation as powerful sources of innovation at both macro and micro level. The lens through which this perspective is investigated is the process through which a new product or service is created. New Product Development (NPD) processes assume a specific relevance for the success of companies nowadays. NPD can be described as the set of activities beginning with a market opportunity and ending in the production, sale and delivery of the product. NPD is thus the process of transforming a market opportunity and a set of assumptions about product technology into a product available for sale (Krishnan & Ulrich, 2001). According to several scholars (Ulrich & Eppinger, 2000), the activities carried out within NPD usually occur in a sequential order. For this reason, NPD is often defined as a disciplined and defined set of steps that describe the normal means by which a company repetitively converts ideas into saleable products or services. However, the different steps can be also implemented (and this is what actually happens) through continuous iterations, feedback loops and overlapping. The activities carried out within NPD process are numerous and diverse, and include design specifications, cost analysis, business analysis, concept development, prototype development, engineering, screening, marketing test, produc-

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tion, and others. This large number of activities led many scholars, specialized in different disciplines, to develop different models inherent to their specific field of study (Trott, 2002). A much diffused approach is known as “stage-gate”. A stage-gate process is a conceptual and operational map for moving new product projects from idea to launch and beyond, a blueprint for managing the NPD process in a way to improve effectiveness and efficiency (Cooper, 2008). Given its complexity and the number of actors being involved in the execution, NPD is a highly knowledge-intensive activity and this requires companies to gather, manage and apply knowledge in the most efficient and effective way. The second half of the last century was marked by the recognized centrality of knowledge and learning in the global economic scenario (Lundvall & Johnson, 1994), for three main reasons. First, the development of ICT downsized the costs of information management and enabled the easy creation of interactive formal and informal information networks. Second, the movement toward flexible specialization built a culture of communication and cooperation among workers, inside and outside organizational boundaries, facilitating the organizational capability to adapt rapidly and at low costs to market and industry changes. Third, the innovation is more and more critical for organizations’ survival and requires continuous interactive learning with several interfaces inside and outside the firm. In the last decades, an increasing number of knowledge-based theories arose, pointing different perspectives on knowledge management and organizational learning (Dixon, 1994; Senge, 1994; Von Krogh & Ross, 1996), and their integrative features (Pawlowsky, 2001). These theories were employed as a framework for the analysis of strategic organizational processes and dynamics, identifying knowledge workers and communities as “critical sources of knowledge and information as well as enablers of knowledge diffusion processes of major concern for the development of an organization” (Wilkens

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et al, 2004). It is a consolidated assumption that organizations learn through individuals acting as agents for them. In turn, individuals’ learning activities are facilitated or inhibited by an ecological system of factors called an organizational learning system (Argyris & Schön, 1978). While facing the threats and challenges of the knowledge-intensive business environment, many organizations have developed, supported and institutionalized new forms of interaction among workers, both internally and externally to organizational boundaries (Enkel et al., 2000). These communities are able to activate the generation of knowledge through the socialization processes (Nonaka & Takeuchi, 1995) and apply the integrated knowledge to a specific problem, task, field or environment (Pawlowsky, 2001). The extraordinary development of ICT and Internet has exerted a disruptive impact on organizations, allowing the creation of new organizational forms that benefit from networking, delocalization and fast-paced processes (Ghoshal & Bartlett., 1997). To exploit these benefits, more and more companies have created and supported virtual communities to maximize their capability to absorb new knowledge, share and apply it for the development of new ideas (Chesbrough, 2006). The ultimate purpose is to face successfully pressing challenges such as growing diversity of knowledge sources, increasing complexity of NPD processes and shorter product life cycles (Drucker, 1993; Toffler, 1980).While knowledge is commonly recognized as a key source of competitive advantage, there is still little understanding of how to create and leverage it in practice. Traditional knowledge management approaches attempt to capture exiting knowledge within formal systems, such as databases. However, “systematically addressing the kind of dynamic knowing that makes a difference in practice requires the participation of people fully engaged in the process of creating, refining, communicating, and using knowledge” (Wenger, 1998).

Knowledge Democracy as the New Mantra in Product Innovation

Technology-based interaction represents the basic distinctive trait between real and virtual communities. Virtual communities are harder to be developed effectively but they are also more capable to afford the challenges of the new business environment and its increasing complexity (Hildreth et al. 2000). In the attempt to define a systematic taxonomy of virtual communities interacting at global scale, three kinds of networks have been identified in literature (Gloor, 2006): •

•

•

Collaborative Innovation Networks (COINs), made of self-motivated people who share a common vision, meet on the web to exchange ideas, knowledge, experiences and to work in a collaborative way towards a common goal; Collaborative Interest Networks (CINs), composed by people who share the same interests but don’t perform a common work in a virtual team; this kind of community is very frequent on the web, it has many “silent” members that keep information gathered from different web sources, and few active members who share their knowledge and experience; Collaborative Learning Networks (CLNs), a community made by people willing to share knowledge and practices to benefit reciprocally from personal mastery and collective knowledge accumulation.

These three types of collaborative networks contribute to create a so-called Collaborative Knowledge Network (CKN), i.e. a “high-speed feedback loop in which the innovative results of COINs are immediately taken up and tested, refined or rejected by Learning and Interest Networks, and fed back to the originating COINs” (Gloor, 2006,). COIN is therefore the creative basis of CKN, the enabling factor for the creation of fluid organizations, characterized by relevant creativity, productivity and efficiency thanks to its fundamental principles of “creative collabo-

ration, knowledge sharing and social networking”. A COIN is generally created around a new interesting idea absorbed outside organizations, brought inside and discussed in a “swarm” collaborative and creative way to improve individual knowledge and capabilities and performance of the organization. CKN are essentially “groups of self-motivated individuals driven by the idea of something new and exciting, a way to greatly improve an existing business practice, or a new product or service for which they see a real need” (Gloor et al, 2003). Innovation teams and communities are but new concepts. However, a strong difference respect to the past can be found in the “virtuality” and “connectivity” enabled by the Internet. This has really changed the rules of innovation generation and dissemination, and the spreading of new ideas, which have become more and more global and fast (Chesbrough, 2006). CKNs are effective settings for the development of new products, services, practices and methodologies. Two different mechanisms operate in a CKN ecosystem (Gloor, 2006); first, Innovation Dissemination, where in each CKN there is a core group that generates ideas and proposes new goals, a middle level community of people that absorb the innovation, discuss, integrate and transmit it to the peripheral group; and second, an Innovation Incubator, in which the dissemination spiral activated by the CKN is potentially never ending as the COINs create innovative ideas and the CLNs discuss them and exchange information and experiences on their application, working in a collaborative way on potential developments. Every community is a society, with specific roles and a “code”. Virtual communities on the web make a “natural selection” of their members, based on the principle of social rewards for positive behaviors and sanctions for bad ones (Gillmor, 2003). Accordingly, the CKN arises along a social culture addressing the principles of: meritocracy, guaranteeing the reciprocity of the benefit

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of the knowledge sharing and the collaborative work; transparency, since belonging to the CKN requires and must ensure the clear understanding of the community composition; consistency, by sharing a common vision and focusing on common goals. These principles represent the real foundation of value creation processes within communities, and therefore the basis for enhancing knowledge democracy. The following paragraph describes different cases of business innovation and NPD. These cases are presented as successful applications of knowledge democracy concepts and practices. In fact, the stories are representative of how collective thinking and distributed knowledge creation can overcome some limitations of traditional knowledge management approaches, going beyond the collection of existing knowledge toward the generation of new knowledge, through the democratic involvement of people committed in the process of creating, refining, communicating, and using knowledge (Wenger, 1998).

CASES OF ‘KNOWLEDGE DEMOCRACY’ Collaboration and networking can have a relevant impact on the innovation processes of organizations. Within every industry, new ways of developing collaboration emerge along with new organizational models and a management mindset which sees the company as an open system porous to external knowledge and experiences. The “open innovation” idea is mostly based on making knowledge creation, problem solving and idea generation driven by the “wisdom of crowds”. But how does this happen? Which are the main models and tools that can trigger the wisdom of crowds? To answer these questions, ten interesting experiences have been identified and examined: (1) Chinese Motorcycle Industry; (2) Boeing; (3) BMW; (4) Procter & Gamble; (5)

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Kluster; (6) CrowdSpirit; (7) 3dswym; (8) Dell; (9) Philips; and (10) Fluid Innovation. These cases are analyzed hereafter with the goal to identify some distinguishing traits and lessons learned. The most of information was obtained from the websites of companies and/or the websites of the initiatives described. Moreover, the extensive analysis of cases contained in Wikinomics (Tapscott & Williams, 2006) has been a relevant source of information. The Chinese Motorcycle Industry is an emblematic example of “peer production”, as it is composed by a wide group of national firms (like Zongshen, Longxin and Jialing) which collaborate in new product design and production phases. The peculiar organization allows to dramatically reduce the lead time, speed up engineering and delivery of new motorcycles, and lower total costs under the average level of traditional supply chain models. Thanks to a collaborative culture, the motorcycle production grew from 5 to 15 million units per year since the middle 90s, making China the world leader in the industry with almost 50% of the global market. The major source of competitive advantage relies on the modular design, benefiting from the capability of suppliers to test, develop and re-test quickly the fit among the components provided by different firms. Ultimately, the product obtained is both low-cost and high-quality. Boeing is moving towards the paradigm of mass collaboration, trying to develop a global platform of integrated partners and stakeholders around a Collaborative Knowledge Network. The company aims to lowering costs, increasing innovation and providing the market with novel products in shorter time. The “787” is representative of this orientation as the aircraft was developed thanks to the integration of more than 100 suppliers located in six different countries. The design and production network is enhanced by a technological platform for synchronous collaboration (developed by Boeing and Dassault Systems) which is indeed called “Global Collaboration Environment”. The

Knowledge Democracy as the New Mantra in Product Innovation

platform links all the community members to an integrated system of tools for Product Lifecycle Management (PLM) and to a shared knowledge base containing all the design and technical data. During the design phase, all the stakeholders in the Boeing “ecosystem” are able to check the compatibility of their own components within the overall production line, also based on real-time simulations, resulting in increased effectiveness of production. At the same time, synchronous design and collaboration ensure the reduction of time and costs for product delivery. BMW developed a global network of Research and Development within the automotive industry. This Collaborative Knowledge Network involves more than 8.500 actors (embracing suppliers, universities, research institutes, and customers) located globally, from California to Japan. Boosting the synergies coming from the diversity of know-how and competencies, the network enhances the innovation and production capabilities. For instance, Japanese researchers at BMW work together with firms and universities to monitor, design and test new automotive technologies, such as engine components and electronic circuits. The integration of partners and suppliers allows to achieve faster product innovation and strengthen the successful differentiation strategy of the company. Procter & Gamble holds today a leadership position within the industry of “prosumer” products, also thanks to a continuous innovation approach. The “Connect and Develop” initiative has been for the company the means to collaborate with a wide network of organizations and individuals worldwide. This Collaborative Knowledge Network provided P&G with a platoon of 9000 researchers, able to collaborate with more than 200 scientists and engineers all over the world. The total amount of people involved in the network is quite impressive: about 1.8 million persons are linked with the ultimate goal to promote business growth and innovation. Through “Connect and Develop”, the company exploits the opportunity

to scan continuously for products and technologies to acquire, improve and introduce in the market. The launch of the initiative increased by 60% the productivity of R&D, decreased sensibly the overall costs of innovation and doubled its rate of success (Huston and Sakkab, 2006.) Kluster is a web-based interactive platform aimed to achieve a specific goal, i.e. to promote new ideas through collaboration. The platform was created at Mophie, a company producing iPod accessories and strongly oriented to exploit the contribution of customers for product design and development. Kluster enables a true cooperative work among users or co-designers which are involved in a set of activities related to NPD, like design, engineering, market delivery, promotion, and event planning. Final decisions are taken on the basis of an algorithm (a weight is associated to voting members within the community) based on the contribution given to successful initiatives and the risks involved. CrowdSpirit is an online community aimed to develop and market consumer electronic products (e.g. CD players and webcams). CrowdSpirit manages all the phases of a production cycle of a product, from design to after-sale and customer assistance. The network is a truly Collaborative Knowledge Network as its members may propose ideas to the community, which votes to determine the most promising idea that the CrowdSpirit should fund, prototype and test. The distribution channels of CrowdSpirit take then care of commercializing the product and promoting it among retailers. 3dswym is a technological platform (developed by Dassault Systems) equipped with 3D simulation functionalities and aimed to support the collaborative design of products. The distinctive feature of the platform is to allow users to “create” a product, its package, presentation and promotion features, and to check ex-ante the impact that the product is likely to have on the shelf, thanks to the availability of a “hyper-market” where the product is positioned. The idea of simulation was

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firstly introduced for products sold on supermarket shelves, where product shape and package may have a relevant role in the purchase decision of the customer. In this perspective, the simulator recognizes customer needs and expectations, generates and tests the new product, and ultimately allows to reduce time-to-market and risks of failure. Dell represents one of the most successful examples of adoption of web 2.0 tools for exploiting market opportunities. During the conceptualization of a product, Dell strongly relies on a corporate blog where users can suggest and discuss new ideas. The blog was enormously successful as it collected more than 8000 ideas articulated along specific categories. Each idea is associated to one of the following stages: “under-revision”, “in progress”, “coming soon”, “implemented”, “partially implemented”. The key success factors of the blog are the user-friendly access and utilization and the real commitment that the community boosts to post new solutions. Philips created the “Open Innovation Campus” with a twofold objective: (a) increase the efficiency and collaboration within the company by creating a cluster of R&S centers; and (b) facilitate open innovation by disclosing the campus to external actors and by enabling virtuous cycles of cooperation in research. The company has thus been able to catalyze ideas and innovation, enhancing the services offered to the customer. Furthermore, it shares technologies, promoting the application of its intellectual property to the high-tech companies present in the campus, to introduce rapidly innovative products on the market, reducing the time-to-market. Cooperation and networking are supported by facilities available to all the start-ups in the campus. One example of these facilities is represented by MiPlaza, related to micro-systems and nanotechnologies, embracing also a “clean room” equipped with laboratories and services for the analysis of materials. The success of the first campus, launched in Eindhoven in 1999, encouraged Philips to extend the good practice in

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seven other countries, developing a real network of global collaboration. Fluid Innovation is an on-line community able to virtually simulate a software market, allowing companies to evaluate the marketing impact of software technologies developed for non-commercial applications. Community members can invest virtual money in real technologies developed by existing companies, supporting them in forecasting the impact of those technologies in a real market, and in evaluating the opportunity of launching them on the market, and their successful potential. This approach allows to assess the availability of potential investors, starting from the Fluid Innovation community. Table 1 synthesizes the focus, actors involved, and success factors for each one of the cases analyzed. Moreover, the knowledge democracy dimensions which can be extracted are reported in the last column, with a first classification of technology, business and organization/process elements. The evidence from success cases brings a consideration about the necessary “pre-conditions” for a Collaborative Knowledge Network to act as a virtual innovation community. Next section analyses such enabling factors and integrates them within a single model.

BUILDING AN INTEGRATED MODEL Open collaboration and distributed intelligence can delivery relevant benefits such as shrinking time to market, reduced product development costs, increased quality and compliance with customer requirements, and reduction of investment risks. The ten cases examined represent successful practices of knowledge democracy in new product development. The cross-case analysis (Table 1) highlights the different success factors and three key building blocks to consider, i.e. (a) technology capabilities; (b) business capabilities; and (c) organization and process models. Next, four sub-sections analyze these three aspects separately,

Knowledge Democracy as the New Mantra in Product Innovation

Table 1. Cases of knowledge democracy in NPD and their characteristics Case

Chinese Motorcycle Industry

Focus

- Design - Development - Testing - Production

Main Actors

Technology capabilities - Web-based Platform - CAx Applications Business capabilities - Cross-enterprise Engineering - System Integration Organization/process models - Communities of Innovation

- Collaborative Testing -Global Collab. Environ. - Reduced Project Costs - System Integration - Reduced Time-to-Market

Technology capabilities - Web-based platform - CAx Applications - MDO Business capabilities - System Integration - Product Lifecycle Management Organization/process models - Learning Networks - Communities of Innovations

- Global network R&D - Knowledge and Competencies - Reduced Develop. Time

Technology capabilities - Web-based Platform, - CAx Applications - Knowledge-based Engineering Business capabilities - System Integration Organization/process models - Learning Networks, - Communities of Innovations

- “Connect and Develop” - Network size - Innovation Costs Reduct.

Technology capabilities - Web-based Platform Business capabilities - Cross-Enterprise Engineering Organization/process models - Learning Networks, - Communities of Innovation

- Web Users

- User Involvement - Cooperative Work - User motivation

Technology capabilities - Web-based Platform Business capabilities - Concurrent Engineering Organization/process models - Learning Networks - Communities of Innovations

- Web Users

- Easy access - User friendly - Internal Funding - Prototypes Promotion

Technology capabilities - Web-based Platform Business capabilities - Configuration Management Organization/process models - Learning Networks - Communities of Innovation

- Hyper-Market Application - Time-To-Market Reduction - Intuitive 3d Tools “Lifelike” Experiences

Technology capabilities - Web-based platform - Simulation and VPD Business capabilities - Configuration Mgt Organization/process models - Learning Networks - Communities of Innovation

- National private companies - Local Suppliers

- Boeing - Partners - Suppliers

BMW

- Monitoring - Design - Testing of Technologies and Components

- BMW - Suppliers - Universities - Research Institutes - Customers

P&G

- New Idea Generation - Research & Development

- P&G - Engineers - Researchers - Public and Private Organizations - Individuals

Kluster

Crowd Spirit

3dswym

- New Products Concept - Design - Launch

- Whole production cycle

- New Products Concept - Design - Testing

Knowledge Democracy Aspects

- Components Integration - Customization - Independent Organization - Modular Design - Reduced Costs - Reduced Time-to-Market

- Product Lifecycle Management

Boeing

Key Success Factors

- Web Users

continued on following page

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Table 1. continued

Fluid Innovation

Philips

IdeaStorm

New Technologies Analysis, Test and Evaluation

- New product design and development

- New product idea generation and design

- Companies

- Forecast of Marketability and Potential Success - Return on Investment Evaluation - “Fair Value” Identification

Technology capabilities - Web-based platform - Simulation and VPD Business capabilities - Configuration Management Organization/process models - Learning Networks, - Communities of Practice

- Philips - High-tech companies

- Open Innovation Campus - Research Cooperation - Time-to-market Reduction - Technologies, Facilities and Intellectual Property Sharing

Technology capabilities - Web-based Platform Business capabilities - Cross-Enterprise Engineering Organization/process models - Learning Networks - Communities of Innovation

- Web Users

- Idea Collection and Ordering - User Friendly system - Reward system

Technology Capabilities - Web-based Platform Business capabilities - Configuration Management Organization/process models - Communities of Innovation

with a brief overview of existing models and applications, and thus integrate them within a single framework to successfully leverage the potential of knowledge democracy in NPD process.

Technology Capabilities A first building block present in almost all the successful cases is related to technology capabilities, i.e. the platforms, systems and tools (along with the associated competencies of people) which can support extended collaboration and virtual interaction in NPD. In fact, the adoption of proper ICT tools can strongly contribute to the successful implementation of a distributed product development strategy. A first enabler is represented by the adoption of web-based applications to allow the creation of global collaboration platforms. Internet portals, as well as company intranets and extranets may provide the backbone for extended collaboration. Many problems in product development are due to communication issues. Ensuring high-quality communication during product development is a key goal, and the implementation

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of collaborative applications that address richer and faster communication may thus be crucial. Among the core phases of product development, design, engineering and manufacturing represent three key areas in terms of technology application. A relevant family of tools is represented by “CA-x” applications, i.e. Computer Aided Design (CAD), Computer Aided Engineering (CAE), and Computer Aided Manufacturing (CAD). CAD allows to create digital 3D models in a faster and less expensive manner than using drawings or physical prototypes, allowing more options to be experimented earlier in the design process. CAD systems can also ensure that the designs of separate sub-systems fit together and allow incremental design changes in real time. CAE enables virtual testing and engineering analysis of CAD models, thereby allowing engineers to undertake large exploration of the design space and reduce the need for building and testing physical prototypes (Danjou & Koehler, 2007). CAM enables digital designs to serve as the basis for software protocols that drive numerically controlled production processes.

Knowledge Democracy as the New Mantra in Product Innovation

Another important technology capability relates to simulation and virtual product development tools. Companies increasingly rely on virtualization technology to speed up and improve product development by replacing expensive physical tests with cost-effective simulations, such as in the case of virtual prototyping. The growing trend to offshore research and engineering, and the early integration of suppliers in production and development, asks teams around the world to conduct simulation in isolation from other teams. There is thus a need to develop platforms which make simulation capabilities available to a broader range of stakeholders, improve inter-organizational collaboration, and ensure consistency and repeatability through best practices and data sharing. High Performance Computing (HPC) are important enablers for these distributed systems. Digital Mock-Up (DMU) tools allow to represent the structure of the product and its position of the geometry, enabling an accurate presentation of the assembly process (design in context) as mounting and collision analysis. In the endeavor of optimizing the reuse and sharing of engineering knowledge, Knowledge Based Engineering (KBE) extends feature-oriented and parametric product modeling by rule-based modeling techniques, know-how, analysis functions, routines, and custom specific optimization processes. KBE can streamline handoffs among different teams to optimize design and engineering. Multidisciplinary Design Optimization (MDO) also works in the sense of increasing efficiency and effectiveness of trade-off between diverse disciplines such as Finite Elements Analysis (FEM) and Computational Fluid Dynamics (CFD). MDO is fundamental within the context of modern engineering design environment, which is characterized by the imperative to reduce time cycles and costs. The integration of many disciplines can be simplified by the use of a “master model” which synthesizes a wide array of design areas (e.g. stress analysis, heat transfer, and maintainability), providing a single representation of the product.

The aim of technology is to optimize products and processes as far as possible. This may also requires to better integrate complex business operations. In the perspective of realizing a Digital Plant, the integration of Product Data Management (PDM) tools with more business-oriented systems such as Enterprise Resource Planning (ERP), Supply Chain Management (SCM), and Customer Relationship Management (CRM) may be a relevant feature. Product development becomes thus more strictly linked with production planning, marketing, procurement and other core processes. Other technology issues are represented by common design standards and languages, and the implementation of a Service Oriented Architecture (SOA) approach.

Business Capabilities If technology capabilities provide the platform for NPD knowledge to be created and shared, business capabilities include all the individual and organizational competencies which support the coordination of a complex and distributed NPD lifecycle. Indeed, although software tools and technology platforms are great enablers, the real challenge for companies is to re-engineer current approaches, strategies and competencies in a way to take full advantage of technology. First of all, a strong system integration capability is required. Cross-enterprise engineering is needed to support the global cooperation of internal and external organization units in the transformation of classic product development into a virtual process. The ultimate goal is to optimize co-ordination, standardization, speed, quality, data consistency, and costs by integrating dispersed product development teams and disciplines into a single overarching system. Concurrent Engineering is a systematic approach to the simultaneous and integrated design and manufacture of products. This causes the developers to consider all elements of the product life-cycle from conceptualization

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through disposal, including management of costs, scheduling, risks and user requirements. In the logic of a more extended product development idea, the Product Lifecycle Management (PLM) represents an integrated approach including a consistent set of methods and models for managing product information, engineering processes and applications integration along the different phases of the product lifecycle. PLM addresses the globally distributed and interdisciplinary collaboration between producers, suppliers, partners and customers. The Integrated Product Development (IPD) approach offers a goal-oriented combination of methodical, organizational and technical methods to realize the product development process. A team-oriented and multidisciplinary product development perspective is achieved at both intra and inter-organizational level. The key point here is a holistic consideration of the whole product life cycle. This requires dynamic project management capabilities, as well as the capacity to ensure consistency of product configuration throughout all the phases of design and development.

Organization and Process Models The third pillar for the success of knowledge democracy is the existence of enabling organizational and process models based on logic of networking, connectivity, parallelization, and economies of scope. At this purpose, communities of practice, communities of innovation and learning networks are among the best candidate approaches to enhance aspects such as building channels of communication from customers to product development teams, integrating product development with global supply chain management, organizing and engaging distributed teams, and exploring best practices. Considering the different product development lifecycle phases, characterizing processes should be crowd-sourcing and assessment of ideas (in the concept generation phase), global networked R&D (research), modular planning and design, competencies and resources integration/

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development, shared design (product and process design), configuration management and concurrent engineering (product engineering), betatesting and virtual testing (prototyping and testing), peer production (production), community-based promotion (sales and marketing), peer-assistance and know-how sharing (service).

THE MODEL Technology and business capabilities, along with organizational models and processes are the building blocks of an integrated framework enabling knowledge democracy. Figure 1 shows the framework and the components discussed in the previous paragraphs. Generally, such complex capabilities are more likely to characterize large organizations which are able to develop global partnership and extended networks. It is instead difficult to find them in a single organization, especially a small or medium company. The integrated model here described could be thus used as a reference framework to support the creation of a virtual innovation ecosystem built on logic of openness, peering, sharing and global action. In this ecosystem, a single organization interacts all “in one place” with different communities and other organizations focused on specific product development steps. All the participants of the ecosystem share the benefits of a bigger audience, expertise and knowledge. Reducing investment costs related to the development of technology infrastructure could be a first motivation for participating in the network. Other reasons are look for the solution of a very specific or complex problem, as well as benchmarking best practices and techniques, identifying outstanding competencies, or starting collaborations around a new idea are other possible motivations. Reputation and rating mechanisms could be integrated to facilitate the identification of most relevant contributors. A community of communities built with this logic

Knowledge Democracy as the New Mantra in Product Innovation

Figure 1. The integrated model

could synthesize all the dynamics of NPD making crowd-sourcing, social networking, collective intelligence and knowledge sharing a real value added for the community.

CONCLUSION The globalization of markets, increasing diversification of products and services, and the centrality of innovation as competitive driver force companies to create new strategies of product development. The joint effect of ICT diffusion of and the affirmation of collaborative networks determined a new paradigm which revolutionized the traditional process of generating new ideas for

products and services. The collaboration economy boosts the principle of democracy which governs the creation and application of strategic knowledge, based on new principles of openness to different stakeholders, peer-based structures, share of strategic assets to favor cross-fertilization, and global mindset and scope. Success cases such as the Chinese Motorcycle Industry, Boeing, BMW, Procter & Gamble Crowd Spirit, and Dell show that the organization which succeeds in product innovation relies not only on internal abilities but rather on the contribution of customers, suppliers, partners, and co-developers involved in activities like market research, product design and development, prototyping, testing and production. In this way, more targeted and customized products

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and services can be obtained in less time and with minimized investments and risks. The idea of democracy of knowledge as a driver of product innovation asks to develop specific competences related to the integration of complex organizational models and processes, and the adoption of technology platforms to support collaborative working. Besides, traditional workflows should be redesigned to allow more open, dynamic and interactive NPD processes. This chapter showed a holistic model to describe the system of business, technological, organizational and process requirements critical to leverage the potentiality of knowledge democracy in product innovation. The model is also proposed as a possible reference framework to build an extended virtual community for product innovation. The ability to continuously introduce new products and services will be one of the most relevant sources of competitive advantage in the next decades. Activities like scanning market opportunities, technology road-mapping, complex system design, and developing integrated technology architectures cannot be undertaken in isolation. Product innovation is likely to become one of the most relevant examples of collective intelligence applied to the world of business.

REFERENCES Argyris, C., & Schön, D. (1978). Organizational learning: A theory of action perspective. Reading, MA: Addison Wesley. Burt, R. S. (2000). The network structure of social capital. In R. I. Sutton & B. M. Staw, Research in organizational behavior. Greenwich, CT: Jai Press. Chesbrough, H. (2006). Open innovation: The new imperative for creating and profiting from technology. Boston: HBS Press.

Cohill, A. M. (2000). Finding the new way: A perspective on the knowledge democracy and community learning. Retrieved from http://www. designnine.com/library/ docs/comm_learning.pdf Cooper, R. G. (2008). Perspective: The stage-gate idea-to-launch process: Update, what’s new, and NexGen systems. Journal of Product Innovation Management, 25, 213–232. doi:10.1111/j.15405885.2008.00296.x Cummings, J., & Cross, R. (2003). Structural properties of work groups and their consequences for performance. Social Networks, 25(3), 197–210. doi:10.1016/S0378-8733(02)00049-7 Danjou, S., & Koehler, P. (2007). Challenges for design management. Computer-Aided Design & Applications, 4(1-4), 109–116. Davenport, T. H., & Prusak, L. (1997). Working knowledge: How organizations manage what they know. Cambridge, MA: HBS Press. Dixon, N. (1994). The organizational learning cycle: How we can learn collectively. London: McGraw-Hill. Drucker, P. (1993). Post capitalist society. Oxford: Butterworth-Heinemann. Enkel, E., Heinold, P., Hofer-Alfeids, J., & Wocki, T. (2000). The power of communities: How to build knowledge management on a corporate level using the bottom up approach. Retrieved from http://en.scientificcommons.org/6553 Ghoshal, S., & Bartlett, C. A. (1997). The individualized corporation. New York: Harper Collins. Gillmor, D. (2003). In the wild west of the internet there are good guys and bad guys. San Jose Mercury News. Retrieved from http://www. siliconvalley.com/mld/ siliconvalley/business/ columnists/6881523.htm Gloor, P. (2006). Swarm creativity. Competitive advantage through collaborative innovation networks. New York: Oxford University Press.

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Gloor, P., Laubacher, R., Dynes, S., & Zhao, Y. (2003, November 3-8). Visualization of communication patterns in collaborative innovation networks: Analysis of some W3C Working Groups. Paper presented at ACM CKIM International Conference on Information and Knowledge Management, New Orleans.

Rupp, T. M., & Steiner, C. P. (2003). Supporting distributed engineering in the aerospace industry by Web-based collaborative applications. In F. Weber, K. S. Pawar & K. D. Thoben (Eds.), Proceedings of the 9th International Conference on Concurrent Enterprising (ICE2003), Espoo (pp. 509-517).

Hildreth, P. M., Kimble, C., & Wright, P. (1998). Computer mediated communications and international communities of practice. In Proceedings of Ethicomp’98 (pp. 275–286). The Netherlands: Erasmus University.

Senge, P. M. (1994). The fifth discipline. San Francisco, CA: Jossey-Bass.

Hiltz, S. R. (1998). Collaborative learning in asynchronous learning networks: Building learning communities. Retrieved from http://eies.njit. edu/~hiltz/collaborative_ learning_in_asynch.htm Huston, L., & Sakkab, N. (2006). Connect and develop: Inside Procter & Gamble’s new model for innovation. Harvard Business Review, 84. Johnson, D. W., & Johnson, R. T. (1996). Cooperation and the use of technology. In David, J. (Ed.), Handbook of Research for Educational Communication and Technology (pp. 1017–1044). Krishnan, V., & Ulrich, K. T. (2001). Product development decisions: A review of the literature. Management Science, 47(1), 1–21. doi:10.1287/ mnsc.47.1.1.10668 Lundvall, B. A., & Johnson, B. (1994). The learning economy. Journal of Industry Studies, 1. Nonaka, I., & Takeuchi, H. (1995). The knowledgecreating company: How Japanese companies create the dynamics of innovation. New York: Oxford University Press. Pawlowsky, P. (2001). The treatment of organizational learning in management science. In Handbook of Organizational Learning and Knowledge. New York: Oxford University Press. Rogers, E. M. (2003). Diffusion of innovations. New York: Free Press.

Tapscott, D., & Williams, A. D. (2006). Wikinomics. London: Portfolio. Toffler, A. (1980). The third wave. New York: Bantam. Trott, P. (2002). Innovation management and new product development. Harlow, UK: Pearson Education Limited. Ulrich, K. T., & Eppinger, S. D. (2000). Product design and development. New York: McGraw-Hill. Von Krogh, G., & Roos, J. (1996). Managing Knowledge. London, UK: Sage. Wenger, E. (1998, June). COPS: Learning as a social system. Systems Thinker. Retrieved from http://www.co-i-l.com/coil/knowledge-garden/ cop/lss.shtml Wilkens, U., Menzel, D., & Pawlowsky, P. (2004). Inside the black box: Analyzing the generation of core competencies and dynamic capabilities by exploring collective minds. An organizational learning perspective. In Handbook of Organizational Learning and Knowledge. London: Sage.

ADDITIONAL READING http://creativecommons.org/ http://jazz.net/pub/index.jsp http://kluster.com/

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http://www.3dswym.com

http://www.ideastorm.com/

http://www.boeing.com/

http://www.openinnovators.net/

http://www.crowdspirit.com/

http://www.philips.com/

http://www.fluidinnovation.com

www.pgconnectdevelop.com/

http://www.huddle.net/

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Chapter 9

Knowledge Management under Institutional Pressures: The Case of the Smartcard in France Rémy Magnier-Watanabe University of Tsukuba, Japan Dai Senoo Tokyo Institute of Technology, Japan

ABSTRACT This chapter explores how knowledge management, an enabler of change due to its knowledge creation capability, is subject to several forces that shape its processes and outcomes. A qualitative analysis based on data from a case study of the first major rollout of smartcard technology in France shows how institutional isomorphic pressures affect not only knowledge management processes but also resulting innovations. Government impetus, legal authorities, and cultural expectations in French society produced coercive isomorphic pressures on the credit card industry, while existing credit card solutions, systems, and standards played the role of mimetic pressures, and professional networks and network externalities acted as normative pressures. The study suggests that a systems perspective which acknowledges these institutional isomorphic pressures can lead to greater strategic alignment and can provide a basis for meaningful differentiation and competitive advantage.

Introduction As Burgelman and Grove (2007) have clearly explained, ‘nonlinear strategic dynamics come about as industry participants – sometimes incumbents, but probably more frequently new entrants – change the rules of the game’ (p. 966). These rules span normative rules based on laws, customs, and administrative principles; technological rules DOI: 10.4018/978-1-60566-701-0.ch009

based on available technical solutions; economic rules reflecting existing bargaining power relationships among the industry players (often captured in contracts); and cognitive rules that are widely shared judgments about key success factors. The authors contend that whether implicit or explicit, the rules of the game usually remain unchallenged for extended periods of time (Grove, 2003), thereby engendering a strong tendency toward strategic inertia among the industry incumbents (Burgelman & Grove, 2007).

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Knowledge Management under Institutional Pressures

Institutions are the sources of such rules that are imposed to the organization and its competitive field. They have the power, as it will be illustrated with a case study, to induce innovations and make use of their strong institutional impetus. In the attempt by governments to stimulate the economy, solving this issue of disconnect between institutional systems legitimized in routine on the one hand and innovation striving on change on the other hand, promises to lead to a newfound virtuous cycle of growth. The importance of knowledge management (KM) lies in the fact that it has been recognized as a source of competitive advantage and has become a necessary practice for innovation, in which ‘the central competitive dimension of what firms know how to do is to create and transfer knowledge efficiently within an organizational context’ (Kogut & Zander, 1992, p. 384). Moreover, when institutions are viewed as ‘the humanly devised constraints that structure human interaction’ (North, 1994, p. 6), institutions can be seen as shaping much of the knowledge of our societies, both as inputs and outputs. And because technology for instance, has long been established as embodying a type of knowledge – ‘technology is the knowledge of the manipulation of nature for human purposes’ (Betz, 1993, p. 374) – or as being the output of a unique knowledge utilization – ‘technology is the application of scientific and engineering knowledge to achieve practical results’ (Roussel et al., 1991, p. 22) – innovation turns out to be an outcome of institutions. Therefore, institutions, which ‘can be powerful sources of both stability and change’ (Jepperson, 1991, p. 159) shape the environment where innovations have the potential to flourish (or perish), and these successful (or failed) innovations provide in return a justification for the aforementioned institutions. This dialectic between institutions and technology brings KM to the foreground in exploring the institutional factors influencing innovation.

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Background and Hypotheses A prevailing definition of knowledge management is the knowledge value-chain approach common to many KM descriptions (Shin et al., 2001). Magnier-Watanabe and Senoo (2008) for instance describe it as ‘the process for acquiring, storing, diffusing and implementing both tacit and explicit knowledge inside and outside the organization’s boundaries with the purpose of achieving corporate objectives in the most efficient manner’ (p. 22). The four stages of knowledge acquisition, storage, diffusion, and application, although not necessarily sequential, are required to achieve the efficiency function of KM within the organization (Alavi & Leidner, 1999). As such, the two goals of KM are productivity gains through efficient decisionmaking and problem-solving, and innovation by way of bringing new ideas to market (Holsapple & Joshi, 2000). First, knowledge acquisition, which can be either focused or opportunistic, is the process of gaining new knowledge, from either inside or outside the organization and in either tacit or explicit form. Even though acquisition supposes that knowledge already exists and is brought in from another location, the fact that this already-existing knowledge becomes part of the organization gives it the status of new knowledge inside the firm. To some extent, knowledge creation is the acquisition of knowledge from within the organization, while knowledge addition is the acquisition of knowledge from outside the organization. Second, knowledge storage deals with the sharing patterns of knowledge within the organization and whether it is stored for individual or collective benefit. In this regard, public storage of knowledge enables knowledge sharing, while private storage hinders it. Besides the firm’s specific policies and practices on knowledge sharing and because knowledge can be both tacit and explicit (Polanyi, 1966), knowledge storage is subject to the firm’s organizational culture which can foster a sense of individual or collective membership (Magnier-Watanabe &

Knowledge Management under Institutional Pressures

Senoo, 2008). Third, knowledge diffusion, which can be either prescribed or adaptive, deals with efficient knowledge flows, which are embedded within the organization’s pattern of systematic relationships as defined in the corporate communication routes and nodes. The purpose of knowledge diffusion is to consolidate and make any knowledge available to and useable by all relevant members of the firm. And last, knowledge application can be viewed in terms of the type or amount of learning pertaining to knowledge exploration and knowledge exploitation (March, 1991; Gupta et al., 2006). Where learning occurs along a trajectory that has already been followed, then it is exploitative or incremental learning. Where learning occurs along an entirely different trajectory, then it is exploratory or experimental learning. And since the learning trajectory is specific to an individual, group or company, what may be viewed as exploitative learning by one group, may be considered exploratory by others (Gupta et al., 2006). These knowledge management processes are assessed on a continuum ranging between two extremes and are rarely of one type or another. The KM value-chain is consistent with the knowledge-creating view of the firm, which defines knowledge as a process of justifying belief toward the truth (Nonaka & Takeuchi, 1995). The conversion processes between tacit and explicit knowledge (Socialization, Externalization, Combination, and Internalization, or SECI) help synthesize subjective values into objective and socially-shared knowledge. The knowledgecreation process starts with socialization where the tacit knowledge of customers and competitors is acquired through field-building. That knowledge is then externalized through dialogue into explicit knowledge to be stored or shared within the firm. Next, the explicit knowledge is in a form appropriate to be diffused throughout the organization and combined with other existing knowledge. Subsequently, these complex sets of explicit knowledge are internalized by the firm

through its workers to determine its most favorable application and to put it in action.

Institution and Isomorphism Institutions are products of the human socialization process. When frequently repeated actions become cast into a pattern leading to an economy of effort and greater efficiency – also called learning curve, this habitualized activity frees up valuable resources for reflection and innovation. In other words, institutionalization enables tensionrelieving predictability allowing the specialization of actors through the division of labor (Berger & Luckmann, 1966). A more operational description recognizes that ‘institutions consist of cognitive, normative, and regulative structures and activities that provide stability and meaning to social behavior. Institutions are transported by various carriers – cultures, structures, and routines – and they operate at multiple levels of jurisdiction’ (Scott, 1995, p. 33). However, as classic research showed (Zucker, 1977; DiMaggio & Powell, 1983), institutions are especially prone to inertia, reducing their capacity to change, while undergoing institutional and competitive isomorphic pressures forcing them to adopt the same structure fitting the environment’s constraints. Zucker (1977) demonstrated that the greater the degree of institutionalization is, then the greater the generational uniformity of cultural understandings (transmission), the greater the maintenance without direct social control, and the greater the resistance to change through personal influence are. DiMaggio and Powell (1983) argue that today, structural change seems no longer driven by competition or the need for efficiency, but rather by processes, making organizations more similar without necessarily making them more efficient. Organizations can in general be categorized as belonging to a recognized area of institutional life called organizational field, i.e. suppliers or consumers for instance. The authors point out that

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once structured into a field, organizations become more similar among them in a process of homogenization called isomorphism. ‘Isomorphism is a constraining process that forces one unit in a population to resemble other units that face the same set of environmental conditions’ (DiMaggio & Powell, 1983, p. 66). There are two types of isomorphism, competitive and institutional, and we focus here on the latter kind which counts three mechanisms of institutional isomorphic change: coercive isomorphism that stems from political influence and the problem of legitimacy; mimetic isomorphism resulting from standard responses to uncertainty; and normative isomorphism, associated with professionalization. Coercive isomorphism is the result of both formal and informal pressures exerted on organizations by other organizations upon which they are dependent and by cultural expectations in the society within which organizations function. Mimetic isomorphism occurs when organizational technologies are poorly understood, when goals are ambiguous, or when the environment creates symbolic uncertainty; then organizations may model themselves on other organizations. Uncertainty is also a powerful force that encourages imitation. Organizations tend to model themselves after similar organizations in their field that they perceive to be more legitimate or successful. Normative isomorphism is a consequence of professionalization. Two aspects of professionalization are important aspects of isomorphism: formal education and legitimation in a cognitive base produced by university specialists on the one hand, and growth and elaboration of professional networks that span organizations and across which new models diffuse rapidly on the other hand. The filtration of personnel is an important mechanism for encouraging normative isomorphism (DiMaggio & Powell, 1983). It is important to note that coercive isomorphic pressures – drawn from political influence, legitimacy and culture – are strongest since they exert prescriptive power over the very existence of the organization.

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Institutional Impetus and Innovation Previous work has looked at the importance of institutional impetus in national systems of innovation, especially in the area of public-private partnerships (Link et al., 2002; Youtie et al., 2006) whereby research centers foster the creation of collaborative networks to eventually support knowledge growth. It has been shown that these public-private partnerships either receive support from a public institution – in the form of direct subsidies, shared use of expertise and laboratory facilities, or tacit licensing agreements – or may have a public institution as a direct or indirect member (Link et al., 2002). More generally, Amadi-Echendu (2007) pointed out that ‘systems of innovation must be energized, linked and sustained by behavioral forms which are generally delineated into public and private agencies, institutions, and organizations of various business persuasions’ (p. 1206). In any case, these institutions need to remain flexible enough to deal with the inherent uncertainty of innovation (Healy, 1997).

Framework and Hypotheses This research examines how institutional forces affect knowledge management and eventually the resulting marketed outcome embodied in a new product or service (Figure 1). Institutions exert isomorphic pressures – coercive, mimetic, and normative – over the organizations under their scope of authority. Under the influence of these forces that ‘constrain the rules of the game’, the knowledge management of the firm is compelled to morph into appropriate practices that comply with the demands of these isomorphic pressures and fit the general institutional environment (a). The resulting product or service will eventually bear the marks of these institutional forces (b). The marketed outcome will then be evaluated against the market’s expectations and tolerance, leading to constant indirect monitoring and legitimation of institutional isomorphic forces (c). Inciden-

Knowledge Management under Institutional Pressures

tally, the market itself is under the social yoke of the same institutional forces and Scott (1995) notes that the three pillars of institution – regulative, normative, and cognitive – bring out three separate but related bases of legitimacy – legally sanctioned, morally governed, and culturally supported – respectively (d). From this framework, we hypothesize that institutional isomorphic pressures – coercive, mimetic, and normative – influence the four knowledge management processes of acquisition, storage, diffusion, and application (Figure 2). Indeed, Turner and Makhija (2007) suggest that ‘any given control mechanism has the capacity to affect both the nature and flow of knowledge in a firm by the manner in which it processes particular attributes of knowledge’ (P. 213) and they contend that these control systems influence each stage of the knowledge management process. In addition, when viewed as a dynamic organizational capability, knowledge management can help the firm achieve congruence with the changing business environment (Teece et al., 1997; Kusunoki et al., 1998; Eisenhardt and Martin, 2000).

Case Study: the ‘Groupement Cartes Bancaires’ This exploratory analysis of the effect of institutional isomorphic forces on knowledge man-

agement calls for qualitative research found to be more appropriate in testing nascent theories (Edmondson & McManus, 2007). Using an interpretive case study methodology (Walsham, 1995), data were gathered from public information and an interview conducted in December 2005 in Paris with Mrs. Martine Briat, Chief Legal Officer of the ‘Groupement Cartes Bancaires’ (GCB), responsible for organizing and managing the ‘Carte Bancaire’ (CB) payment card system. When possible, supplementary data was obtained from ‘key informants’ and archival sources such as annual reports and other related documentation, and helped crosscheck relevant information and verify data reliability.

The Groupement Cartes Bancaires The issuance of payment cards is in France carried out by financial institutions brought together and organized as an ‘Economic Interest Group’ (EIG) named ‘Groupement Cartes Bancaires’ (or CB Bank Card Consortium) and created in November 1984 following an agreement among the three major payment card networks dated July 31, 1984. The foundation of this group composed of all the French payment card issuers represented the outcome of a trend among banks to pull together and share costs and efforts in managing that new payment instrument. In 1967, French consumers discover the first payment cards when 6 banks (Banque Nationale

Figure 1. Effect of institutional impetus on KM and the resulting marketed outcome

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Figure 2. The influence of institutional pressures on knowledge management

de Paris, Crédit Commercial de France, Crédit du Nord, Crédit Industriel et Commercial, Crédit Lyonnais, and Société Générale) launch the ‘Carte Bleue’ to compete with American cards like American Express and Diners Club. The Carte Bleue differentiates itself using immediate debit requiring no additional payment order as opposed to payment on credit for its competitors. In 1971, the first automated teller machines (ATMs) enabling cash withdrawals are made available to the public using the magnetic strip technology, and two more banks (Crédit Agricole with the ‘Carte Verte’ and Crédit Mutuel) launch their own card network. The following year in 1972, the 6 issuing banks of the ‘Carte Bleue’ found the ‘Carte Bleue’ EIG, which counts 71 members. In 1976, the ‘Carte Bleue’ EIG becomes a member of Visa, and the ‘Carte Verte’ network a member of EuroCard/MasterCard, enabling cardholders to use their card abroad. In 1980, the first electronic point of sale terminals (POS) are installed on merchants’ counters. In 1983, three more banks join the ‘Carte Bleue’ EIG (Banques Populaires, Caisse d’Epargne, Chèques Postaux); there are now three competing bank card networks: the ‘Carte Verte’ (Crédit Agricole) with 7.7 million cards, that of the Crédit Mutuel with 1.3 million cards, and the ‘Carte Bleue’ with 5 million cards. The founding of the ‘Groupement Cartes Bancaires’ on July 31, 1984,

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combines the three previously competing networks consisting of the original 11 banks, leading to the issuance of a single payment card labeled ‘Carte Bancaire’ or ‘CB’1. The 1984 Memorandum of Understanding laying the foundations for CB inter-banking enables all CB cardholders to withdraw cash from any of the 7,000 ATMs which were then in service, whatever the card issuer’s bank, and to pay for purchases at the 300,000 CB affiliated merchants. Thus, the CB system made the three systems, at the time – Carte Bleue, Crédit Agricole and Crédit Mutuel – interoperable.

The Development of the CB Smartard A payment card is a card enabling both payments and withdrawals and is in France alternatively called a bank card. Such card equipped with a functional chip used for payments with a PIN (Personal Identification Number) is called a ‘smartcard’. The first smartcard experiments start in three locations – Lyon with Schlumberger, Caen with Philips, and Blois with Bull CP8 – and the Bull CP8 solution is eventually selected. Even as the constraint of the government vanishes when the banking industry is privatized in 1986, the banks stick together within the GCB. From the beginning, experience builds on with the CB magnetic strip-only card network operated by the GCB.

Knowledge Management under Institutional Pressures

Experimentations and investments continue and the details of the project are validated in 1990, including chip’s specifications, pin-enabled terminals, and retrofitting of old ATMs. From 1988 to 1992, the GCB focuses on increasing the number of points of acceptance of the CB cards, in other words the number of payment terminals. Retail giants, for whom high volumes are crucial, are quick to equip their points of sales since the card represents a viable solution to cash-register bottlenecks. To convince small retailers to put a terminal on their sales counter, the GCB agrees to provide a payment guarantee and its irrevocability. In 1992 all CB cards adopt the chip technology and become smartcards, allowing cardholder to take advantage of off-line PIN verification and therefore significantly reduce the level of fraud. The endeavor of the GCB leads to the development of a French industry at the forefront of the smartcard technology today (Axalto, formerly Schlumberger; Oberthur; GemPlus; Sagem). Mrs. Briat explains that ‘our goal has always been to decrease the unit cost by increasing volumes’, which, looking at the 55 million CB smartcards in circulation today, has been achieved. In addition, the installation of new ATMs, the retrofitting of old ones into inter bank-enabled ATMs are planned from the beginning. Only one appendix dealing with the fee paid by the merchants to the banks is later dropped because it was the source of legal proceedings with the Antitrust Council between merchants and banks. Also, the retail giants’ early decision to adopt the CB system to handle high volumes of transactions played an important role in reinforcing the position of the GCB and its technology. Especially in the case of the early adoption of new technology, companies take on a ‘wait-and-see’ attitude before embracing an innovation or new standard. The successful lobbying of the GCB with giant retailers acted as a springboard for the smartcard whose acceptance kept growing until its current ubiquitous status. Recently, continued support by public institutions – acceptance of CB cards

for citizens purchasing public services in public administrations; new law of October 2004 authorizing the payment of public expenses by CB card; Bank of France supporting the GCB system with the European Central Bank – reinforces the institutionalization of the CB smartcard.

The Mission of the Groupement Cartes Bancaires By signing the 1984 agreement, the three card networks commit to building among them a total inter-bank card system for the functions of cash withdrawal and payment, enabling any customer holding a card issued by one of its member banks and financial institutions to use all the ATMs as well as pay at any merchant affiliated with the CB network. The compatibility of all withdrawal and payment systems is achieved at the end of 1985. The broad mission of the GCB is to promote, develop and ensure the security of the CB system; it represents today more than 150 members from French and foreign banking and financial institutions, mostly operating in France. The role of the GCB consists of four main activities. First, it is responsible for establishing the rules applicable to CB members by overseeing the CB system and managing the common resources of its members, who are represented on the Board of Directors by the 11 founding banks. Second, it is in charge of defining the system’s general architecture, the standards and interbanking procedures required for its operation, and of ensuring compliance, acting as the guarantor of the rules defining each of the system functions – card issuance, cash withdrawal management, and payment acceptance – as well as the technical specifications and tools for data exchange. Third, it manages transaction authorizations, inter-banking services and fees, providing its member banks with common tools for data exchange such as the e-rsb (CB authorization switching network) which switches the payment and cash withdrawal authorizations between the member institutions,

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Knowledge Management under Institutional Pressures

and the SICB (CB Information System). Last, the GCB is accountable for certifying the compliance of equipments to CB standards, working closely with manufacturers of electronic payment systems. In France today, domestic cards can only be used within the CB system and international cards bearing the CB logo and that of an international partner network, MasterCard or Visa, can also be used anywhere in the world. The GCB does not have the status of a credit institution and does not issue payment instruments nor manages customer accounts. All operational aspects related to CB cards are the reserve of the member banks and credit institutions. The GCB is a separate organization from the Carte Bleue EIG which handles the relations with Visa, and from the Europay France organization that handles relations with MasterCard. The CB system interconnects four stakeholders – the cardholder, the merchant (or ATM), the cardholder’s bank known as the issuing bank, the merchant’s bank (or the ATM bank) known as the acquirer bank – and also involves other actors such as equipment manufacturers, clearing and settlement bodies, and gateways with other international systems. A bankcard transaction involves several steps that range from the payment order given to purchase goods or a service, through to clearance and settlement (transfer of amounts between banks), as well as the transaction authorization. The entire system relies on an advanced banking and electronic payment infrastructure that are maintained through major investments by the various banking establishments forming the CB system. It is these investments that guarantee the quality of service, such as network response time and availability, for cardholders and merchants alike. This open-ended system is in fact constantly upgrading to adapt to new needs of users, technological progress and its members’ developing requirements (Groupement Cartes Bancaires, 2008).

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Recent Figures of the Groupement Cartes Bancaires The GCB is responsible for the first major roll out of smartcard technology in the banking industry, using a chip embedded in the card and off-line PIN verification for all its cards. In 2007, the network counted over 55.7 million CB smartcards (+4% vs. 2006) held by 87% of the French aged 18 and over (Expertise CB, 2007), more than 50,000 ATMs and 1.2 million points of acceptance (Expertise CB, 2008). In 2007, 7.26 billion transactions were realized for a total of EUR 381.8 billion, with payments, as opposed to withdrawals, representing more than 80% of the total in value (Table 1). The 5.85 billion CB card payments in 2007 represent more payments by CB cards than payments by checks in Europe (Expertise CB, 2008). The number of payments by CB smartcard continues to increase, to the detriment of payments by check or cash, and they now represent 30% of consumer expenditures in 2004 in France and 36.5% of card payments in the Euro zone (Groupement Cartes Bancaires, 2005). As stressed by Mrs. Briat, ‘97% of CB cardholders are satisfied and 69% are very satisfied2‘.

Isomorphic Pressures at The Groupement Cartes Bancaires Isomorphic pressures were found to apply to the knowledge management processes pertaining to the creation and maintenance of the CB smartcard.

Coercive Isomorphism On the topic of coercive isomorphism, DiMaggio and Powell (1983) report how Pfeffer and Salancik (1978) observed that politically constructed environments have two characteristic features: political decision makers often do not experience directly the consequences of their actions; and political decisions are applied across the board to

Knowledge Management under Institutional Pressures

entire classes of organizations, thus making such decisions less adaptive and less flexible. In the case of the GCB, coercive institutional pressures include the impetus and support of the government, the judgment of the Antitrust Council in its favor, and cultural expectations specific to the French society. Impetus and Support of the Government In 1984, all French banks are state-owned and their presidents are appointed by the Ministry of Finance. Both technology and patents (Innovatron and Bull between 1970 and 1979) are French and because the government is looking for applications, it encourages the banks under its control to develop the smartcard system to create a showcase for French technology. Thus, under the impetus of the government, banks have to pool together information related to R&D, relationships with technological partners, customer data, and retailer data, within the GCB. The GCB has the mission to store and protect the data that has been provided by the banks. In return, each member-bank and

partner is owed relevant and timely information by the GCB. The very structure of the GCB requires that all CB card applications remain open to member-banks to facilitate technical integration. ‘As the GCB was created as an Economic Interest Group’, claims Mrs. Briat, ‘it brings together competing banks whose purpose is to share tools and resources. (…) In the 1984 draft agreement, everything is already laid out, whether it is about the chip, the pin-enabled terminal, the RCB authorization network and the supporting SICB information system’. Direct institutional support to the banks and the GCB was central to the achievement of the GCB. As Mrs. Briat points out, ‘the government gave the initial impetus in 1984 to build a French technology showcase; (…) even after the privatization of the banking industry in 1986, the public administration has always indirectly supported the GCB’. Interchange is the principle that holds the GCB together since banks have an incentive to support the inter-bank system while remaining competitors. The interchange of payment and

Table 1. Number of transactions handled by the Groupement Cartes Bancaires and number of cards and transactions per card from 2001 to 2007 2001

2002

2003

2004

2005

2006

2007

07/06

Payments

170.2

190

203.9

219.6

236.8

257.3

283.3

+10.1%

Withdrawals

68.1

75.9

80.5

85.2

88.6

92.2

98.5

+6.8%

Total

238.3

265.9

284.4

304.8

325.4

349.5

381.8

+9.2%

Payments

3.67

4.1

4.34

4.65

4.98

5.34

5.85

+9.6%

Withdrawals

1.14

1.21

1.25

1.26

1.29

1.33

1.41

+5.8%

Total

4.81

5.31

5.59

5.91

6.27

6.67

7.26

+8.8%

Average payment (in euros)

46.4

46.4

47.0

47.2

47.6

48.2

48.4

+0.5%

Average withdrawal (in euros)

59.9

62.6

64.7

67.6

68.5

69.5

70.1

+0.9%

Number of CB cards (in millions)

43.3

45.4

47.6

49.1

51.2

53.6

55.7

+4%

Number of payments / card / year

92.4

97.9

97.8

101.1

103.5

105.3

110.5

+4.9%

Number of withdrawals / card / year

26.3

26.7

26.1

25.7

25.3

24.8

25.2

+1.8%

Volume of transactions (in billion euros)

Number of transactions (in billions)

Number of CB cards and transactions per card

Source: Expertise CB, 2008

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Knowledge Management under Institutional Pressures

withdrawal is the fee that the card issuer’s bank pays to another bank whenever the cardholder is using that other bank’s services, like its ATM for example. They bear that extra cost based on the fact that they cannot control where a card is used: one day it may withdraw money in an ATM of its bank and also pay for a purchase at a merchant that has a different bank. This need for inter-bank capability was recognized as necessary to pool resources together and realize economies of scale. The payment card system is a two-sided market that needs to attract both merchants and cardholders, and where banks have very little control. The utilization of the RCB/e-rsb authorization switching and SICB Information System networks, which are shared, de facto characterizes a structure where knowledge is exchanged, shared and diffused within the GCB. Then, heavy lobbying conducted by the GCB during the development and launch phases of the CB smartcard - regional promotion committees visiting local chambers of commerce and industry, associations and retailers; legal expert panels of judges, lawyers on electronic data and security – secured additional institutional support paving the way for a faster technology adoption. In 1984, France Telecom which is then state-owned adopts the chip technology for all its phone cards and in 1986 a court decision recognizes electronic data as proof of purchase. Antitrust Council Judgment Early on, the particular statute of the GCB casts doubts over its customers, merchants, accusing this association of banks of unfair competitive practices and price cartel. In 1986, citing the first paragraph of article 81 of the 1957 Treaty of Rome (Treaty of Rome, 2002), which is the founding treaty of the European Union, the National Trade Association files suit against the GCB claiming that banks are fixing the prices of fees charged to retailers in the context of its founding principle

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of interchange. These lengthy court proceedings lead to the 1988, 1989 and 1990 judgments (#88-D-37 of October 11, 1988; #89-D-15 of May 3, 1989; #90-D-41 of October 30, 1990) by the Antitrust Council ruling that the GCB has to change its pricing system, and sentencing it to a fine of FRF 6 million (about USD 1 million) for having failed to change its pricing system in due time as previously requested. However, the court also recognizes that the principle of interchange is necessary to the smooth functioning of the payment card system, even if it somehow warps the free play of competition. The Antitrust Council motivates its decision in part based on an exception mentioned in the third paragraph of the same article in the Treaty of Rome (Treaty of Rome, 2002), which states that such associations or cartels are permitted if they ‘contribute to the promotion of technical or economic progress while reserving some profits to their users, imposing no further unnecessary conditions to their stakeholders, and allowing for acceptable residual competition’ (p. 33), without giving any further detail about the aforementioned ‘profits to their users’. According to Martine Briat, this encounter with the judicial system is the event which brought the GCB closer together since it had gotten an official approval of its very specific nature and the acknowledgement of its contribution to the French technological advancement with the spread of the smartcard. The judgment of the Antitrust Council acts as a coercive pressure since it legitimizes the technology and business model of the GCB with all the stakeholders – partners, merchants, cardholders – in France. The judgment of the Antitrust Council, by ruling that the GCB can continue its activities provided it changes its pricing system, is coercive to both the plaintiffs who have to accept the business practices of the GCB, and to the GCB itself which must comply with a new pricing system in order to maintain its existence.

Knowledge Management under Institutional Pressures

Cultural Expectations in The French Society The features of the technology alone have been designed to not only fit the functional needs of the market, but also to coincide with its cultural expectations. For example, the wide acceptance of the CB system by merchants was made possible by the payment guarantee ensured by the immediate debit of the cardholder’s account and its irrevocability. The security feature of the smartcard’s embedded chip, compared to the magnetic strip-only card widely used in the USA, addresses a specific requirement from the French market. The difference lies in the fact that cards in the USA are credit cards carrying interest rates of 20% minimum and generating enough revenues for the banks to compensate for the cost of fraud, while in France, the CB cards are debit cards in the sense that the amount is directly debited from the cardholder’s account. For that reason, fraud and counterfeiting can offset the margin of the banks if not controlled. Even if the smartcard is five times more expensive than the magnetic strip-only card, it fits the particularity of the French banking industry. In 2007 for instance, the level of fraud remains very low at 0.0335% (Groupement Cartes Bancaires, 2007). Also, the smartcard’s off-line authorization chip technology fits the French conception of electronic data privacy protected by the French electronic data privacy agency (CNIL). Founded in 1978, the CNIL independently watches over the protection of personal data, especially electronic information, and endorses the CB’s off-line authorization system. The American payment card system, because of historically much lower telecommunication costs, relies on on-line payment authorization using personal information databases. The French have always been very cautious about databases of personal information and have decided to avoid them altogether in the payment card system by adopting the chip; in addition to added security,

the chip allows for off-line authorization limiting the transfer of information. It is important to note that this condition of minimum data transfer is consistent with the nature of the GCB where its members, the banks, otherwise compete against each other. During the transaction are transmitted only the time of the operation, card number, amount, currency, authorization number and business tax ID number, and not even the name of the cardholder. Because the authorization is off-line, the issuing bank bears the risk to settle any unpaid debts. However, the issuing bank guarantees the payment to the merchant’s bank.

Mimetic Isomorphism On the topic of mimetic isomorphism, DiMaggio and Powell (1983) explain how modeling is a response to uncertainty: ‘Models may be diffused unintentionally, indirectly through employee transfer or turnover, or explicitly by organizations such as consulting firms or industry trade associations, and innovation can be accounted for by organizational modeling’ (p. 69). In the case of the GCB, mimetic institutional pressures include the format of the CB smartcard, and the compliance with the EMV standard.

Card Format In the case of the GCB, the Carte Bancaire keeps the same form and attributes of existing payment cards, as those from American Express and Diners Club, but only in the transitional period until the launch of the embedded chip. The card is recognized as a successful means of payment and is for that reason retained in its existing form. A change in format or standard would have forced a rethinking of the existing production processes for payment terminal suppliers and the internationalization of the smartcard. Also, the Carte Bancaire retains the same features as those of the Carte Bleue, after which it was first modeled, using immediate debit as opposed to payment on credit

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Knowledge Management under Institutional Pressures

for its American competitors. Interestingly, the GCB adopts the same legal statute of Economic Interest Group as that of another EIG created in 1979 called ‘Memory Cards’ counting hardware and software companies and the license holders (Innovatron) which were also looking at applications. And because the security of the card is only as good as its weakest link, namely the magnetic strip on its back used when paying or withdrawing money abroad, the GCB is urging the European Central Bank to adopt its security standard in the rest of the European Union.

EMV Standard In 1997, Europay, MasterCard and VISA create the EMV standard for interoperation of smartcards and corresponding POS terminals, for authenticating credit and debit card payments. The banking community including the GCB endorses it and in 2001, a migration agenda to the EMV standard for withdrawal and payment transactions was signed by the banking and trade industries. In 2002, the first testing of EMV-enabled smartcards took place and from 2008, all CB systems, cards, ATMs and POS terminals are EMV-enabled in France, and Europe. According to Mrs. Briat, ‘nobody is talking about it because the transition has been smooth and completely transparent for the users; cardholders have a marvel a technology in their wallet with a smartcard holding four different applications in a single chip: the B0’ (CB application); the EMV (CB application); the international application (Visa, MasterCard); and Moneo (electronic wallet)’. Moreover, the former authorization switching network (RCB) was replaced in 2005 by the e-rsb (Banking Service Network) which now uses Internet protocols. As the smartcard-based EMV becomes the new European standard, it represents an explicit acknowledgement of the GCB’s technology and its legitimization at the European level with the ongoing creation of the Single European Payment Area (SEPA)3.

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Normative isomorphism On the topic of normative isomorphism, DiMaggio and Powell (1983) interpret professionalization as ‘the collective struggle of members of an occupation to define the conditions and methods of their work, to control the production of producers and to establish a cognitive base and legitimation for their occupational autonomy’ (p. 70). In the case of GCB, normative institutional pressures mainly consist of movements of personnel to and from the banks and GCB and network externalities created by the rise in the number of points of acceptance.

Professional Networks The GCB is born from the merger of three existing card networks that bring their own work and management methods to the table. The personnel transferred from the member-banks to the GCB apply their tried-and-true knowledge and methods drawn from their own experience and existing norms in the financial and information technology industries. This effect can still be found in the management and work methods epitomized in decision committees. Moreover, there are many patents shared among a handful of companies like Bull, Innovatron, and the GCB itself owns three of them. Mrs. Briat argues that she ‘considers the patents to be jointly owned’, because later-derived patents granted for further application created a scattering of intellectual property making further individual development difficult. Since the CB card has spread to the majority of businesses, its institutionalization has triggered an increase in shared knowledge and practices which now play the role of normative forces. All banks are members of the GCB which acts as a powerful professional network in the diffusion and maintenance of the CB card.

Network Externalities Last, the CB system benefits from a network effect where the value of the CB smartcard rises

Knowledge Management under Institutional Pressures

with the increase in the number of points of acceptance and terminals, as well as the number of other cardholders. As the number of points of acceptance soars, non-cardholders are encouraged to join the existing network and benefit from its ubiquitous offering.

Isomorphic Pressures and KM at the Groupement Cartes Bancaires Isomorphic pressures were found to shape the knowledge management processes in the development of the CB system. Government impetus, legal

authorities and cultural expectations of French society produced coercive isomorphic pressures, while existing credit card solutions, systems and standards played the role of mimetic isomorphic pressures, and professional networks and network externalities functioned as normative isomorphic pressures (Table 2). In this particular instance of intense inter-organizational collaboration, several key factors have been shown to influence the extent of knowledge sharing, the stability of the relationship, and the ability to get a competitive advantage from the alliance (Levina, 1999). First, knowledge

Table 2. The influence of institutional isomorphic pressures on knowledge management in the development of the CB smartcard Knowledge Acquisition Coercive

Government impetus ■ Banks pool their data together through the GCB.

Mimetic

N/A

Normative

Professional networks ■ Merger of the three existing card networks.

Knowledge Storage/Diffusion Coercive

Government impetus ■ The GCB has the mission to store and protect the data that have been provided by the banks, supported by the principle of interchange. ■ Each member owed relevant and timely information by the GCB. Antitrust Council ■ Judgment recognizes the interchange as necessary for the GCB to operate. Cultural expectations ■ Off-line authorization with limited transfer of information. ■ off-line authorization system supported by historically higher telecom costs.

Mimetic

EMV standard ■ Migration to the EMV standard complies with rest of the financial industry.

Normative

Professional networks ■ Personnel transferred from the member-banks to the GCB act as intermediaries.

Knowledge Application Coercive

Government impetus ■ The French government wants a new application for French chip patents. ■ CB card applications have to remain open to members for technical integration.

Mimetic

Card format ■ Smartcard fits current cart standards and suppliers’ production processes. ■ CB card starts with magnetic strip and switches gradually to the chip. e-rsb ■ RCB switching network replaced in 2005 by the e-rsb (Banking Service Network) using Internet protocols.

Normative

Network externalities ■ Rising number of points of acceptance. ■ Benefit from the network’s ubiquitous offering.

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sharing was found to be influenced by the alliance contract and governance structure (Kogut, 1988; Mowery et al., 1996), as well as the collaborative strategy (Hamel, 1991; Larsson et al., 1998); in this regard, the legal status of the GCB, as an economic interest group including all financial institutions operating on French soil, contributed to a higher degree of knowledge sharing because of its government-sponsored mandatory membership for card-issuers. Second, the stability of the relationship was proven to depend on the bargaining power of its members (Pfeffer & Salancik, 1978) whereby the possession or control of key resources by one entity may make other organizations depend on that entity; here, the membership of each financial institution with the GCB guarantees an equal access to information for a fee based on the usage of the authorization switching system. Last, strong management processes designed to protect the alliance and encourage knowledge sharing, and a network structure holding the promise of new opportunities (Dyer & Singh, 1998) were among the factors influencing the ability of alliance partners to get a competitive advantage from their relationships; the GCB, in managing the CB inter-bank system and the common tools available to its members such as the CB network and e-rsb authorization-switching system, is at the heart of a knowledge-sharing network with its members while the GCB itself doesn’t compete with its members since it doesn’t have the status of credit institution. Moreover, the steady increase in the use the CB smartcard and the related CB network has continuously proven the value of the system to its members.

Isomorphic Pressures and Causal Loops The case study of the Groupement Cartes Bancaires illustrates how institutional isomorphic pressures greatly affect knowledge management and the resulting innovation. Using causal loops,

170

we now look at these institutional isomorphic pressures from a systems perspective and examine the interdependence cycle with organizations in markets under their influence. Continuous causal circuits (Weick, 1979), also referred to as causal loop diagrams (Sterman, 2000), are used here to examine the action of institutional isomorphic pressures with an emphasis on interdependent variables, causal loops, and the presence or absence of control. These causal circuits have been successfully used in previous research to investigate from a systems perspective the challenges that organizations face when harnessing knowledge (Garud & Kumaraswamy, 2005). Loops that are deviation-counteracting – with an odd number of minus signs – generate stable systems which include integrated controls that dissolve randomness; loops that are deviation-amplifying – with an even number of minus signs – produce unstable systems, whether constructive or destructive, devoid of built-in controls. According to Weick (1979), the tactic of using arrows and plus and minus signs is simply a means to portray situations of complex interdependence in such a way that one can then ask better questions about these situations. The cycling back of loops means that what was originally a cause is now suddenly an effect. This is a prominent feature of any structure of causal circuits. When the strength of isomorphic pressures – coercive, mimetic, or normative – increases, the resulting variety among the organizations under the scope of institutional authority decreases. In turn, this limited variety generates greater inertia as existing players and new entrants are all the more encouraged to conform to the prevalent model as the number of complying organizations is important. This heightened organizational inertia therefore legitimizes the institutional status quo and reinforces the prevalent isomorphic pressures. This causal loop (inner cycle) is found to be deviation-amplifying, suggesting that the system may explode unless a sign is changed (Figure 3).

Knowledge Management under Institutional Pressures

Figure 3. The institutional isomorphic cycle

titioner with a sharp awareness of the institutional status quo can get out of the ‘iron cage’ to guide the organization towards that much needed differentiation. The aforementioned awareness can result from a thorough ‘institutional diagnostic’ analyzing the current institutional isomorphic system.

Institutional Pressures over Time

Once the system has reached a critical stage where organizational inertia is such that the market is saturated with comparable competitors and product/service offerings, a rogue organization has the opportunity to differentiate itself by moving away from the accepted norm, thereby creating a source of competitive advantage. This differentiation is a source of innovation and in this case, the rogue organization will most likely take its distance from the mimetic and normative isomorphic pressures since coercive forces drawn from political influence, legitimacy and culture exert a prescriptive power over the very existence of the firm. If the rogue player’s decision is successful and eventually followed by others, existing isomorphic pressures will become obsolete and lose some of their legitimacy (outer cycle), therefore transforming the system into a stable deviation-counteracting cycle. Over time, a set of new isomorphic pressures consistent with the new market order will emerge and eventually replace the previous one until new rogue differentiation occurs again. We argue that, in reference to Weber (1952) as quoted by DiMaggio and Powell (1983, p. 63), institutional isomorphic pressures lock up organizations in an ‘iron cage’ since they make them ‘more similar without making them more efficient’ (DiMaggio & Powell, 1983, p.64). While the size and perspective of organizations is greater than the distance between the bars of the iron cage, only an insightful prac-

Although French institutions have brought legitimacy to the CB smartcard’s technology, either explicitly – with the 1988, 1989, and 1990 legal proceedings and favorable judgments of the Antitrust Council on the structure of the GCB, and the EMV Standard – or tacitly – with the adoption of the chip and PIN technologies by the EMV and later by the European Union in 2005 for the project of Single European Payment Area (SEPA) – institutional support is not guaranteed since a change in law, policy or even political balance can alter the stability of the system. For instance, the creation of SEPA – which requires a single currency, a single set of euro payment instruments (credit transfers, direct debits, and card payments), common technical standards and business practices, efficient processing infrastructures for euro payments, one harmonized legal basis, and the ongoing development of new customer-oriented services – will turn the individual national retail payment markets into one pan-European market, therefore increasing business opportunities and competition among providers of payment services (European Central Bank, 2008). Consequently, this institutional change will create new coercive, mimetic, and normative pressures that will affect the business model of the GCB and its dominant position in France. Furthermore, since November 2002, the European Commission has been challenging the GCB on a secret agreement made between its memberbanks to share out the market for the issuance of CB cards in France and to restrict competition from new entrants, such as the banking arms of

171

Knowledge Management under Institutional Pressures

large retailers and certain small and medium-sized banks, including foreign banks. In the European Commission’s view, the agreement severely limits the possibility for lower CB smartcard prices and technical innovation. Following the decision announced on 17 October 2007 by the European Commission, the executive board of the GCB has decided to withdraw the four measures which were notified in 2002 and have been suspended since June 2004. These four measures relate to the mechanism for regulating the acquisition function, the membership fee and a possible additional fee per card issued, and a fee applicable to ‘sleeping members’. Because of the wide range that exists in the yearly fee charged by banks within the European Union for the issuance of a payment card and in the one paid my merchants to the same banks for each transaction, Brussels has given an ultimatum to all financial institutions to urgently address the issue and adopt fairer practices for the consumer.

Conclusion This qualitative analysis based on data gathered from the case study of the first major roll out of smartcard technology in the French banking industry showed how institutional isomorphic pressures affected not only knowledge management processes, but also the development and maintenance of the resulting innovation. The government impetus, legal authorities and cultural expectations in the French society produced coercive isomorphic pressures onto the credit card industry, while existing credit card solutions, systems and standards played the role of mimetic isomorphic pressures, and professional networks and network effects functioned as normative isomorphic pressures. As Weick (1979) previously noted, there is host of influencing factors lying beyond the boundary of the firm: ‘The word organization is a noun, and it is also a myth. (…) Just as the skin

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is a misleading boundary for marking off where a person ends and the environment starts, so are the walls of an organization. Events inside organizations and organisms are locked into causal circuits that extend beyond these artificial boundaries’ (p. 88). Indeed, a host of factors lying inside the organization, such as organizational climate (Chen & Huang, 2007) and organizational structure (Chen & Huang, 2007; Magnier-Watanabe & Senoo, 2008) and outside the organization, for instance national culture (Möller & Svahn, 2004; Martinsons & Davison, 2007) have been found to constrain and influence the nature of knowledge management. Beyond these factors directly affecting the management of knowledge at the organizational or individual level, this research has linked institutional isomorphic pressures to knowledge management using an interpretive case-study approach. Therefore, it is critical that knowledge management not be isolated in a functional department (Hansen et al., 1999) where institutional isomorphic forces are seldom considered a source of influence. Knowledge management can remain under corporate control insofar as these institutional forces are first acknowledged, identified, and their effect carefully studied. On the one hand, congruence between the institutional environment and knowledge management practices ensures legitimacy and acceptance, but on the other hand, it can reduce the capacity of the firm to innovate through differentiation. Consequently, while organizational knowledge has been considered a valuable strategic asset (Zack, 1999) whose strategic alignment can strengthen the firm’s competitive position (Davenport and Prusak, 1998; Zack, 1999), the firm should examine whether to distance itself from and whether to opt for deliberate misalignment with the institutional status quo in practicing knowledge management. We suggest here that a systems perspective of institutional isomorphic pressures can help organizations draw an ‘institutional diagnostic’ to identify the forces that affect knowledge management. This investigative process is a necessary

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step towards overcoming institutional isomorphic pressures and achieving differentiation.

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ENDNOTES 1



2



Source: French Antitrust Council’s judgment #88-D-37 of October 11, 1988. Phone survey by Taylor Nelson-Sofres conducted in November 2006, sample of 1,105 individuals aged 15 and older (of whom 931 cardholders aged 18 and older) representative of the French population,

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results analyzed using only individuals ages 18 and older. The SEPA (Single Euro Payments Area) project is a European project that rounds off the changeover to euro banknotes and coins. Its goal is to create a single, European set of euro-denominated payment instruments.

With these new European payment instruments, consumers, companies, merchants and public administrations will be able to make payments under the same conditions throughout Europe, as easily as in their country (SEPA France, 2008).

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Chapter 10

Does Knowledge Management Really Work? A Case Study in the Breast Cancer Screening Domain V. Baskaran Ryerson University, Canada R.N.G. Naguib Coventry University, UK A. Guergachi Ryerson University, Canada R.K. Bali Coventry University, UK H. Arochen Coventry University, UK

ABSTRACT Contemporary organizations, including those involved with healthcare, are constantly under pressure to produce and implement new strategies for delivering better products and/or services. Knowledge Management (KM) has been one of the paradigms successfully applied in such business environs. However, a lack of proper application of KM principles and its components have reduced the confidence of new adopters of this paradigm. KM-based healthcare projects are moving forward, and innovation is the driving force behind such initiatives. This chapter sets the scene by outlining the KM’s core elements, facets and how they can be appropriately applied within an innovative, real-time healthcare project. It further enumerates a case study which targets the screening attendance issue for the NHS’ breast screening program. The case study not only discusses the need of a balanced approach to address both the technological and humanistic aspects of KM, but also answers the question “Does knowledge management really work?” A questionnaire-based study was conducted with the General Physicians (GPs) on the KM’s aspects and its relationship to the interventions proposed in the study. The study DOI: 10.4018/978-1-60566-701-0.ch010

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

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provided ample proof that a balanced approach will definitely increase the efficacy of such initiatives. Such studies can increase the confidence of future KM adopters in healthcare domain. This chapter provides credibility for such balanced KM-based initiatives and highlights the importance of a focused approach on the various facets of KM to maximize benefits.

KNOWLEDGE MANAGEMENT Knowledge Management (KM) has been acknowledged to be an integral part of management culture which provides methodologies through models, frameworks and approaches with appropriate objectivity via rigorous studies (Wickramasinghe et al., 2007). The following section provides a brief introduction to KM and its focus areas. The core of KM is knowledge; KM identifies how knowledge is created and shared among different stakeholders in a business paradigm. KM is a multidisciplinary management science and every organization has come to appreciate the importance of knowledge and its management. Modern understanding of knowledge has its basis on the teachings of Plato and Aristotle (Pemberton, 1998). Organizations adopting KM have come a long way in their quest for managing knowledge. Taylor started to view knowledge in a scientific perspective and the Hawthorne’s experiments highlighted the humanistic nature of KM in modern management (Kwon, 2004). Drucker coined the term “Knowledge worker” (Ellingsen, 2003) and experts who followed (such as Porter, Cohen, Senge and Nonaka) defined and redefined on this idea of knowledge and how to best manage it (Kwon, 2004). A reasonable understanding of the core elements and the different facets of KM will not only assist in better application of this paradigm but also would provide sufficient expertise to adapt KM proactively while tackling the current business challenges.

Core Elements of KM In spite of exhaustive research interest shown in KM, we are yet to evolve a universal approach

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towards the KM paradigm; it has at least been established that KM is desirable and cannot be disregarded and we are clear about the tacit and explicit nature of knowledge (Hildreth and Kimble, 2002). Knowledge is dichotomized based on its existence; it is termed tacit (when coupled with cognition) and explicit otherwise. In simplistic terms, KM paradigm focuses on three core elements namely people, process and technology (Gillingham and Roberts 2006). Even though other facets of KM have been identified and justified (to mention a few, culture, IT, content, infrastructure, politics, etc). Each of these facets can be mapped to these core elements (Lehaney et al. 2003, Milton 2008). Technologies as simple as email, web blogs, enotice boards, fora etc. to much more sophisticated tools such as AI, knowledge discovery and data mining tools are part of the technology element in KM. Technology plays the least significant role in KM but technology-based KM solutions are easy to procure and implement. They fail to contribute to their potential unless the other two core components are properly aligned with the rest of the KM components. Process is related to internal mechanisms that the organization has established collectively over years of existence. Process represents a summary of best practices which really work in that specific organizational environment. It is common that the contexts for creating such processes are lost over time. Yet they preserve what is good and is practicable for the betterment of the organization. Communities of Practices (COPs), social structure, cultural aspects, social capital and so forth play a vital role in the process element. People are perhaps the most fundamental element in KM and is the most significant factor in a knowledge-based

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strategy. They can, in fact, be said to drive the other two elements. KM thrives only with proper individual’s acceptance of KM’s fundamental concepts. Unhindered exposition of KM procedures can assist in rapid KM deployment. The authors’ accentuation of the people element in KM does not necessarily attenuate the importance of the other two elements. In fact a balanced approach with the correct coalescence would provide the best of KM.

Facets of KM Managing knowledge would encompass the continuous creation of new knowledge, avoid knowledge silos, exchange knowledge freely across boundaries and exploit the available knowledge for all oncoming challenging scenarios. Within KM, Knowledge Creation (KC) and Knowledge Sharing (KS) are the two main facets and Knowledge Engineering (KE) relates to the technology-based tools and techniques associated with both the facets. The initial part of this subsection describes how KC can be initiated within organizations, how KS can play a vital role to leverage the created knowledge and the role played by KE for KC and KS. Knowledge Creation (KC) has been a core issue of the KM paradigm. KC in a cognitive sense is yet to be clearly understood. Incidentally, experts do agree that knowledge is created by various stimuli and managing these stimuli is one of the focus areas of KM (Nonaka et al., 2001). Managing KC literally means managing the knowledge-creating stimuli such as explicit knowledge, interaction, information and personal insight. Explicit knowledge stimuli range from hand written notes to well defined and established standards which, when viewed from the KC context, offer the best stimuli for new KC (Burns, 2003, Koskinen, 2003). Interaction stimuli are often related to the iterative cycles of interactive communication between individuals and can culminate in a strong KC stimulus. This should not be viewed as a form

of explicit knowledge as the iterating interaction is to facilitate individuals to align their perceptions in assisting tacit-to-tacit knowledge transfer (Miller, 2002). An information stimulus is a major source of KC. This represents all the information without the KC context and depends greatly on the individual to comprehend the information in order to create new knowledge. These three types can be unified as external (Timo, 2001). Personal or self-insight stimuli is the final step for KC. Regarded as an internal stimulus, it is unique when compared to the rest and potentially becomes the final stimulus which can trigger new KC. It is an inherent characteristic of an individual’s capability, which relies mainly on the individual’s earlier, acquired, knowledge (Tissen, 2000). KC in the context of stimuli, explains the importance given to the Knowledge Engineering (KE) aspects in earlier KM projects. KE is related to the physical aspects of KM in the following ways: 1. KC facilities/infrastructure for capturing not only information but also to encapsulate it with its creators’ context e.g. knowledge bases, training neural networks, fuzzy logic, Artificial Intelligence for prediction, pattern recognition and image processing etc. relate to explicit knowledge stimuli (Rodrigo, 2001); 2. Providing communication for close interaction between individuals corresponds to KS through e-mails, telephones, mobile devices, discussion fora and chat rooms etc, assists in the betterment of interaction stimuli (Smith and Preston, 2000); 3. Information access (KS) to a wealth of journals and publications, data, books, portals, data mining and data warehouses and the like forms part of the information stimuli (Rodrigo, 2001). All these KE aspects often motivate KC; hence the legacy support of KE in KM projects

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(Dwivedi et al., 2003). KC, or rather stimuli to KC, has been the focus of the early part of KM. This has unnecessarily deprived the focus on the human-centric aspects of KM (Timo, 2001). KM hinges not only on an individual’s knowledge but also on organizational knowledge. Organizational knowledge resides within a virtual network of experts who can be viewed as sharing knowledge as an implicit function of their routine activities (Dwivedi et al., 2003). The organizational context crystallizes an individual’s knowledge into organization knowledge (Sharkie, 2003).

Challenges of KM The acknowledged pitfall of any network analogy (as in computer network) is its communication path. This also holds true for any communication path in a knowledge-networked organization (Dwivedi et al., 2002). KS relies on the communication path available in a particular organization. This in turn is largely dependent on the hierarchic nature of organizational management, structure and style of functioning of the organization. Knowledge gaps can be avoided if there is less bureaucracy and formality in the path of KS (Baskaran et al., 2006). Proper knowledge leveraging would eliminate knowledge islands created by large knowledge gaps (Breen, 1997). The next equally important factor is the inclination to communicate and share knowledge which can be affected by the culture (Ellingsen, 2003). A knowledge-evolved culture would encourage individuals to readily share experiences and knowledge (Burns, 2003). Current KM frameworks find it difficult to offer ready-made solutions to these problems (Hildreth and Kimble, 2002). This calls for innovative approaches where tacit knowledge sharing would be the centre of KM initiatives and not only concerned with knowledge conversion (as earlier perceived by Nonaka’s knowledge creation company and the Socialization Externalization Combination Internalization (SECI) model of KM- Nonaka and

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Takeuchi, 1995). The above section provided a brief explanation of the KM’s core components and its facets that are to be addressed for successful KM implementation. The following section presents KM in healthcare.

KM in Healthcare Knowledge creation and managing knowledge has become the prime focus in the twenty-first century (Milner, 2001). Like many abstract entities, the concept of knowledge also differs according to the context in which it is interpreted – it may be viewed as an object that might be identified, created, captured, stored and accessed or as a process that has a strong human-centric orientation in culture, trust, beliefs and values (Miller, 2002). Healthcare institutions have now realized the true potential of knowledge and are trying, in various ways, to move forward in the new “knowledge era” (Dwivedi et al., 2003). Whether knowledge is interpreted as an object or as a process (McElroy, 2003), the business world has accepted that knowledge is the way forward (Hildreth and Kimble, 2002), especially in organizations where the prime deliverable is service-oriented (such as healthcare). Knowledge richness makes healthcare the most receptive domain for KM-based improvements (Dwivedi et al., 2003). Due to the fact that a large volume of healthcare knowledge is being lost because of its tacit-bias, even the smallest effort for managing this tacit knowledge can result in huge resource savings (Burns, 2003). Currently, healthcare management views KM as a holistic concept and not as another routine management process. KM has also penetrated a wide spectrum of organizations. Healthcare has been particularly successful with its KM initiatives, perhaps because it often embodies the most knowledge-intensive activities and its critical interaction with the public directly makes it a prime candidate for KM based improvements (Baskaran

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et al., 2006). Physicians and medical experts agree that every patient scenario is a unique experience and it ranges from less critical to life threatening (Ellingsen, 2003). Hence the knowledge gaps can be crucial in making the right decisions during a healthcare intervention (Open Clinical, 2000). Current healthcare organizations are experiencing huge shortage in healthcare experts in every field of medicine, this required new strategies to cope up with the increased pressures from the government and the public (Suliman, 2002). KM can make a huge impact in creating effective training processes. This could be extended to create a realistic environment for knowledge transfers like training, workshops, communities of practices and all knowledge sharing activities (Dwivedi et al., 2002). Coupled to this, the healthcare informatics revolution has handed down new challenges such as information deluge and information overload. This has resulted in a slow shift from a pull strategy to push strategy; hence KM is often viewed as a savior to deliver from the perils envisaged by healthcare organizations (Burns, 2003, Gray, 2002).

Does KM Really Work? Having described what knowledge is and how KM can be applied for various business challenges, this section asks: “Does KM really work?”. When KM was reinvented and rebranded recently; a separate identity was bestowed which renewed interest on KM. Globally, organizations were facing new challenges, such as increased competitions, globalization, mergers, economic volatility, need for reducing costs, expected increase of quality of service with less or no additional resources. Such challenges forced the organization to innovate. Innovations, such as KM were often perceived as silver bullet solution which offered immediate deliverance against such perils. A lack of professionalism, misinterpretations of KM fundamentals and a focus on non-critical areas has forced early KM adopters to question its credibility. Organi-

zations should understand that ability to address such critical issues will have a direct impact on the degree of success of KM-based initiatives. Healthcare was not one of the early adopters of KM due to the domain’s inherent complexity and hesitancy in applying unproven concepts. It should be borne in mind that healthcare itself was not bereft of new challenges. Increased life expectancy, baby boomers, increase in literacy resulted in people’s desire for better healthcare, need for early disease detection, discovery of new diseases, evolving technologies, resource constraints have been important catalysts for immediate adoption of KM in healthcare. Currently KM projects have been actively pursued by many organizations (including healthcare). But does KM deliver what it has promised? To answer this question, a healthcare case study can illustrate the efficacy of the approach. We now introduce a case study-based research project which highlights the required balance of the core elements and the need to appropriately address the multiple facets of KM. Such an approach not only alleviates the challenges previously enumerated but also would increase the confidence to adopt KM in more areas within the healthcare domain.

CASE STUDY IN BREAST CANCER SCREENING Breast cancer is the most common cancer for women population across the world (Roder et al., 2008, Gorin et al., 2006). In many developed and developing countries the mortality rate due to breast cancer for women is one of the highest (Mettlin, 1999). Over forty five thousand women were being diagnosed with the disease each year in the UK (Cancer Research UK, 2005). Although many causes had been identified for breast cancer, the knowledge of finding a preventive drug is still not within the reach of modern medicine. Hence, breast cancer has to be diagnosed at the earlier stages of its development. Possible treatments

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include removing or destroying the cancer cells to avoid the spread of the affected cells. Breast self examination is an effective and nonintrusive type of checking for any lumps in the breast tissue; unfortunately this greatly depends on the size of the lump, technique and experience in conducting a self examination by the women (Oikonomou et al., 2003). An ultrasound test using sound waves can be used to detect lumps but this is usually suited for women aged below thirty-five owing to the higher density in breast tissue (American Cancer Society, 2008). Having a tissue biopsy via a fine needle aspiration or an excision is often used to test the cells for cancer. These tests are mostly employed in treatments or post-treatment examination and as second rung diagnostic confirmation methods (Marcela, 2004). Performing a Computed Tomography (CT) or a Magnetic Resonance Imaging (MRI) scan would result in a thorough examination of the breast tissue but this technique is not favored due to the following reasons, it is uneconomical, needs preparation, noisy, time consuming and images may not be clear (Marcela, 2004). Mammography is a technique for detecting breast tissue lumps using a low dosage of X-ray. This technique can even detect a three millimeter sized breast abnormalities. The X-ray image of the breast tissue is captured and the image is thoroughly read by experienced radiologists and specialist mammogram readers (Marcela, 2004). Preliminary research suggests that women aged fifty five and above are more susceptible of getting breast cancer and incidentally, mammography is more suited to the women aged fifty and above (due to the lower density of breast tissue) (Blanks et al., 2000). Even though mammography has its own criticizing community mainly due to its high rate of false positives and false negatives (Epstein, 1979, Burton, 1997) it has still become the standard procedure for screening women by the UK’s National Health Service’s (NHS) National Breast Screening Programme (Forrest, 1986).

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Mammography is the best and most viable tool for mass screening to detect cancer in the breast at an early stage (Medicine net, 2008); however the effectiveness of the diagnosis through screening is directly dependent on the percentage of women attending the screening programme (Pirjo et al., 2001). The NHS Breast Screening Programme, catering to the entire eligible women population is funded by the UK’s Department of Health and is first of its kind in the world. It covers nearly four million women and detected more than fourteen thousand eight hundred cancers in the screened population for the year 2005/6 (NHS Cancer Screening Programmes, 2007). Currently the screening programme routinely screens women between the ages of fifty and seventy, and employs two views of the breast, medio-lateral and cranio-caudal (Breast imaging and diagnostic service, 2007). It is evident that the earlier that malignant lesions are detected, the better the quality of life rendered to the patients, and hence the importance of mammography-based screening. Earlier cancer detection has a profound effect on the longevity of the breast cancer patients. Several studies including randomized controlled trials have found strong evidence that a minimal attendance of 70% has to be achieved for creating an impact on the breast cancer mortality rates by population-based screening initiatives (Pirjo et al., 2001).

THE PREDICAMENT Even though the NHS breast screening program achieves more than the minimum benchmark (70%), the current attendance rate at 75% has been stagnant for the past ten years (NHSBSP annual review 1999-2007). This sizeable nonattendance could result in missed cancer detection for nearly four thousand women (based on the cancer detection rate within the screened women). This large percentage of non-attendance would not only result in loss of life due to breast cancer

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but also loss of screening resources through idle costly imaging equipment, under-utilization of specialist-imaging expertise, wasted screening slots etc. Screening units are unable to arrange buffered attendees for the idle slots, since the units do not know, a-priori, which women will attend and which will not. In addition there is a sizeable cost factor involved in sending repeat screening appointments letters to the non-attending women. This prompted researchers to investigate possible solutions to this predicament. Reasons for non-attendance may be largely attributed to disinterest in attending a mammography session, prior or current medical problems, and fear of X-rays. The said reasons can be negated by proper education provided to the women. Education has to be directed at explaining the advantages and importance of screening and assist in removing the socio-cultural and personal barriers (Cassandra, 2006). Also possible options including convenience in terms of time, place and dates are to be provided to the women for encouraging the women’s attendance. In spite of all the expediency provided to the women, non-attendance has been a grave concern for the national screening programme. This scenario can be properly addressed if we can identify the women who probably may not attend in advance

and focus additional resources to educate such women and thereby increase attendance. Earlier study by Arochena (2003) concentrated on designing an artificial intelligence based attendance predicting algorithm for breast screening domain. This study was funded by and tested at the Warwickshire, Solihull and Coventry Breast Screening Unit (WSCBSU). The algorithm has been evaluated with manual statistical methods like Logistic Regression (LR) and was found to be relatively accurate in predicting non-attendance (Arochena, 2003). This algorithm can assist the Breast Screening Unit (BSU) to effectively schedule the screening programme and eventually result in the efficient use of limited available resources. Even if the attendance percentage were increased marginally, a significant number of lives may be saved. Arochena’s (2003) research resulted in the formulation of the required artificial intelligencebased attendance prediction algorithm and was trained and tested on data sets collected during a ten-year period at WSCBSU (Arochena, 2003). The test results were encouraging especially from the second episode onwards, warranting the algorithm to possess enough potential to be incorporated in a breast screening programme framework.

Figure 1. Proposed protocol (BSAMP) and its architecture

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The data set collected during the ten-year period had many versions or styles and they have to be regularized towards a common platform before being put to use within the algorithm (Arochena, 2003). This data processing was carried out using many software data analysis tools. These tools included Microsoft Excel®, Visual Basic® routines and Statistical Package for the Social Sciences (SPSS®) and Clementine® to extract the data and adapt to the AI models. The algorithm itself was modeled in a visual modeling environment called Clementine® produced by SPSS® (Arochena et al., 2000). The algorithm for its proper functioning depends on all the aforementioned software. This posed a challenge in implementing the algorithm within the routine process of the breast screening office environment. The primary challenge was that they do not possess the required licenses for the said software nor the expertise in linking all the software to work seamlessly for predicting on a monthly basis for every screening batch. Also the earlier research by Arochena (2003) was limited to identify whether the non-attendees can be predicted with reliable accuracy and was not aimed at the various facets of implementing the algorithm nor on envisaging how to effectively use the gained knowledge of probable non-attendance. In addition, the National Screening Programme has been constantly striving to provide better services to the public and one of the new enhancements offered by the screening services is to increase the screening age limit from sixty four to seventy (Patnick et al., 2006). This effectively increased the number of screening episodes and resulted in augmenting the need for effective use of the already stretched NHS resources. All the above factors pertain to how effectively the predicted knowledge can be leveraged and managed.

and create prototype software based on open source technologies. The prototype software was automated to produce the pre-processed data and eventually normalized the data for neural network assimilation. The activities were performed sequentially without human involvement for repeatability, reliability and accuracy. The AI model itself was simulated on the open source technology platform and incorporated all additional transformations happening within the screening process (including the change in screening upper age limit). The prototype framework also incorporated the AI model’s output (i.e. list of predicted non-attending women). It then combined the demographic data pertaining to the non-attending women and information related to her General Physician (GP) as a package. This package triggered the generation of an electronic message based on the Health Level 7 (HL7) version 3 standards and utilized web services as the message delivering technology.

A NEW PROTOCOL

A SOLUTION

The utilization of AI through neural networks (for predicting breast screening attendance) as a knowledge creator and the application of various technological aspects such as web services, SOAP, XML and multi-tier architecture for knowledge sharing to initiate interventions can all be associated to the technology component of KM. To summarize the messaging and the practicality of the proposed solution, a detailed architecture utilizing the existing technologies and proposed components were described as a new Breast Screening Attendance Messaging Protocol (BSAMP). The same architecture can be mapped for other message paths in the future. The BSAMP and its messages pertaining to the prototype model focused on the two approaches and they are:

The current study proposed to find a solution in unifying the software on to a single platform

• •

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Pre-screening (predicted non-attendee); Post-screening (actual non-attendee).

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The driving component for both the approaches is the infrastructure required for the knowledge sharing. The IT revolution occurring under the NHS’ national initiative not only provided the path for faster implementation but also reduced the required overheads by utilizing the in-place system for knowledge exchange. The proposed BSAMP protocol leveraged these technologies. This illustration also points at message flow path associated with the said screening domain. This path is for sharing the knowledge of non-attendance gathered from pre-screening (prediction) and post –screening report. The national breast screening software system communicates the dataset to the AI layer for prediction, and the outcome is appropriately packaged as HL7-based SOAP messages by the wrapper layer and then via the Data Transfer Service (DTS) client interface, the message is sent to the DTS layer. This populates the respective GP mail boxes. Typically, on regular intervals (3 to 5 times per day), the GP application layer executes a batch process of uploading and downloading the GP mail box through the DTS client interface for GPs. When the screening report message arrives at the GP application layer, after due authentication, the individual women’s clinical records are updated. When the woman approaches the GP for consulting, the GP retrieves the women’s record and this triggers the automated prompts. The prompts are colored differently to signify the prediction and actual screening non-attendance. The changes suggested to the screening activities (which include the creation of the screening batch, initiating the prediction and sharing the results with healthcare stake holders) can all be mapped to the process component of the KM. The BSAMP protocol is not only restricted to the GPs but could be easily extended to include other healthcare stakeholders such as diabetes specialist, optometrist, pharmacist, cardiologist and other special care deliverers. This exhibits that there is flexibility to the protocol and with only minor modifications, can initiate multiple

interventions thus improving the probability of increasing screening attendance. This effectively underlines the changes to the existing processes that are necessary to make this study a complete success. The application of standards like HL7 for the current study can also be viewed as a process component which captures the best practices that have been time-tested in healthcare messaging scenario. Such focus on all the core components enabled the proposed strategy to achieve maximum effectiveness.

DISCUSSION Even though a fleeting glance at the case study would give an impression that this project is Knowledge Engineering (KE) based, careful analysis demonstrates the balance achieved on the KM’s core components i.e. people, process and technology. This study not only balances the core components utilizing the right scale but also addresses the different facets of KM for ensuring a complete success of the said project. Even before the new predicting algorithm was designed, exhaustive qualitative analyses through semi-structured interviews with the screening unit’s staff and the earlier researcher (Arochena, 2003) were conducted (people) to understand the changing perspective of the screening unit, available technologies and in general the evolving preventive care in the National Health Service. Such analysis not only provided a sound base for the study but also addressed the people and process components of KM. The authors strongly agree that KC is a cognitive process (people), but repetitive and complex data modeling are best suited for automating with an Artificial Intelligence (AI)-based environment. Such KC processes inadvertently originate in human cognition of the person who conceived the idea and applied the AI as a tool initially to predict the screening attendance. In the current scenario, the AI-based tools are looked upon as secondary knowledge

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capturing mechanisms rather than as primary knowledge creators. Figure 2 depicts the KC stimuli applied for designing and implementing the BSAMP protocol. This figure highlights the importance of the people aspect (the fundamental core component) in knowledge creation. The concept of knowledge sharing with GPs can be identified as a people component. The current study would be deemed incomplete if it had not considered the GP’s opinion and views on how the knowledge would be dispensed through interventions. As part of the current work, a questionnaire-based study was conducted with the GPs who were associated with the WSCBSU. The questionnaire covered areas such as screening women, attendance prediction, electronic data interchange, impact and details of interventions, additional information regarding need for recognition and additional resources, miscellaneous details such as the physician and surgery’s demography. Important information related to the timeframe of knowledge sharing before and after the scheduled appointment was also realized through the questionnaire. Figure 2. Knowledge creation stimuli employed for the current study

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The majority of GPs (80%) felt that some form of recognition through monetary compensation or awards were good strategies for encouraging the GPs to deliver the interventions. This gains importance as the GPs in the UK are functioning as small business units and it is logical to expect them to focus on maximizing the returns on their investments. It should also be taken into account that GPs are not currently compensated for the time spent in increasing the screening attendance. Such aspects discovered by the questionnaire ensured that the challenges at the knowledge delivery end (GP) have to be addressed appropriately before the full scale implementation of the BSAMP protocol. This highlights the criticality of the people component and its role played in making the current study a complete success.

CONCLUSION GPs’ perceptions on primary care and their apprehensions on how their care services delivery have been transformed into small business domains and their impacts on healthcare were highlighted. This has placed a lot of undue pressure that can result in counter productivity. GPs are the backbone of health services and are in direct contact with the public and their efforts are to be valued. Due recognition, even simple appreciation, can be leveraged as great motivators. These fundamental factors are all KM-driven concepts of which people are the core of such knowledge-related activities and services. It was also highlighted how primary care providers can be crucial in making preventive care a total success or failure. From the KM perspective, GPs are viewed as knowledge workers who are strategically positioned to interact with the population, thereby increasing the efficacy of such preventive healthcare services. Even inexpensive approaches, such as appropriate recognition, can yield positive results. One such approach of employing recognition (in the form of public appreciation of GPs efforts specific to the

Does Knowledge Management Really Work?

value added services provided, such as trophies, commendations and awards) were proposed by the GPs. This study also proved that clinical messaging can be employed for efficient knowledge sharing. During the execution of this case study, it was found that convincing the stakeholders with regards to the importance of KM in healthcare was challenging. The GPs were reluctant to spend their resources on non-remunerative services i.e. addressing screening attendance. Future screening initiatives on screening attendance will need to address these issues at a core level; failing to do so will force such initiatives to fail from achieving their true potential. Technology plays a relatively trivial role in this case study; however, the complex mechanisms for knowledge sharing and the urge to communicate and share the knowledge is exclusively people reliant. Failure to acknowledge this aspect will derail the smooth functioning of any KM-based project in healthcare. The case study enumerated a much focussed application of the individual KM components. Utilizing KM strategies for increasing breast screening attendance has opened new vistas in this field and proved that this project was able to achieve its planned objectives. Viewing projects through KM has additional advantages. KM-based initiatives could orient the goals not only towards the physical benefits but also aligns the focus on people who are the core component for any project. Such alignment to the important KM facets not only delivers successful projects but also makes it a complete solution. The study not only proves that KM does work but also ensures better results. Due consideration to the core components and the multiple facets of the KM are fundamental for the success of such initiatives and the authors would boldly recommend that any project (irrespective of which domain they are associated with) can be successfully completed if properly viewed from KM perspective.

ACKNOWLEDGMENT We would like to thank Julietta Patnick CBE, Director, NHS Cancer Screening Programmes, UK.

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Epstein, S. S. (1979). The politics of cancer (p. 537). New York: Doubleday. Forrest, P. (1986). Breast cancer screening: A report to the health ministers of England, Scotland, Wales and Northern Ireland. London: HMSO. Gorin, S. S., Ashford, A. R., Lantigua, R., Hossain, A., Desai, M., Troxel, A., & Gemson, D. (2006). Effectiveness of academic detailing on breast cancer screening among primary care physicians in an underserved community. Journal of the American Board of Family Medicine, 19, 110–121. doi:10.3122/jabfm.19.2.110 Gray, M. (2002). Retrieved September 13, 2005, from http://dataforeningen.no/ostlandet /km/2003 0313/haines.pdf Hildreth, P. M., & Kimble, C. (2002). The duality of knowledge. Information Research- International electronic journal, 8(1). Koskinen, K. U. (2003). Evaluation of tacit knowledge utilization in work units. Journal of Knowledge Management, 7(5), 67–81. doi:10.1108/13673270310505395 Kwon, D. (2004). Knowledge management for turbulent times: Performance impacts of IT valuation. Paper presented at the ASAC Conference, Quebec City, Canada. Marcela, B. V. (2004). The system does work. Journal of the American College of Radiology, 1(6), 438–440. doi:10.1016/j.jacr.2004.01.028 McElroy, M. W. (2003). The new knowledge management (pp. 13–18). Burlington, MA: Butterworth- Heinemann. Medicine net. (no date). Mammogram. Retrieved August 10, 2008, from http://www.medicinenet. com/ mammogram/article.htmaccessed Mettlin, C. (1999). Global breast cancer mortality statistics. CA: a Cancer Journal for Clinicians, 49, 138–144. doi:10.3322/canjclin.49.3.138

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Miller, F. J. (2002). I = 0 (Information has no intrinsic meaning). Information Research - International electronic journal, 8(1). Milner, B. (2001). Knowledge management: The challenge of the twenty-first century. Problems of Economic Transition, 43(10), 65–78. Nonaka, I., & Takeuchi, H. (1995). The knowledgecreating company (pp. 61–94). Oxford: Oxford University Press. Nonaka, I., Teece, D., Kikawada, K., & Holtshouse, D. (2001). Managing industrial knowledge (pp. 306–314). Thousand Oaks, CA: Sage. Oikonomou, A., Amin, S. A., Naguib, R. N. G., Todman, A., & Al-Omishy, H. (2003). Breast self examination training through the use of multimedia: A prototype multimedia application. Proceedings of the 25th Annual International Conference of the IEEE, 2, 295-98. Open Clinical. (2000). The medical knowledge crisis and its solution through knowledge management. White Paper, DRAFT- v.3-15. Patnick, J. [advisory committee on breast cancer screening]. (2006). Screening for breast cancer in England: Past and future. Journal of Medical Screening, 13, 59–61. doi:10.1258/096914106777589678 Pemberton, J. M. (1998). Knowledge management (KM) and the epistemic tradition. ARMA Records Management Quarterly, 32(3), 58–62. Pirjo, I., Kauhava, L., Parvinen, I., Helenius, H., & Klemi, P. (2001). Customer fee and participation in breast cancer screening. Lancet, 358, 1425. doi:10.1016/S0140-6736(01)06520-5

Roder, D., Houssami, N., Farshid, G., Gill, G., Luke, C., & Downey, P. (2008). Population screening and intensity of screening are associated with reduced breast cancer mortality: Evidence of efficacy of mammography screening in Australia. Breast Cancer Research and Treatment, 108(3), 409–416. doi:10.1007/s10549-007-9609-5 Rodrigo, B. C. (2001). Using information technology to support knowledge conversion processes. Information Research - International electronic journal, 7(1). Sharkie, R. (2003). Knowledge creation and its place in the development of sustainable competitive advantage. Journal of Knowledge Management, 7(1), 20–31. doi:10.1108/13673270310463590 Smith, L., & Preston, H. (2000). Information management and technology strategy in healthcare: Local timescales and national requirements. Information Research- International electronic journal, 5(3). Suliman, A. H. (2002). Knowledge management: Re-thinking information management and facing the challenge of managing tacit knowledge. Information Research - International electronic journal, 8(1). Timo, K. (2001). Knowledge management process model. Technical Research Centre of Finland VTT Publications, 455, 101–104. Tissen, R., Andriessen, D., & Deprez, F. L. (2000). The knowledge dividend (pp. 184–202). Harlow, UK: Financial Times- Prentice Hall. Wickramasinghe, N., Bali, R. K., & Geisler, E. (2007). The major barriers and facilitators for the adoption and implementation of knowledge management in healthcare operations. International Journal of Electronic Healthcare, 3(3), 367–381. doi:10.1504/IJEH.2007.014554

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Chapter 11

Knowledge Management: The Key to Delivering Superior Healthcare Solutions Nilmini Wickramasinghe RMIT University, Australia

ABSTRACT The proliferation of ICT (information communication technologies) throughout the business environment has lead to exponentially increasing amounts of data and information generation. Although these technologies were implemented to enhance and facilitate superior decision making, the result is information chaos and information overload; the productivity paradox (O’Brien, 2005; Laudon & Laudon, 2004; Jessup & Valacich, 2005; Haag et al. 2004). Knowledge management (KM) is a modern management technique designed to make sense of this information chaos by applying strategies, structures and techniques to apparently unrelated and seemingly irrelevant data elements and information in order to extract germane knowledge to aid superior decision making. Critical to knowledge management is the application of ICT. However it is the configuration of these technologies that is important to support the techniques of knowledge management. This chapter discusses how the process oriented knowledge generation framework of Boyd and the use of sophisticated ICT can enable the design of a networkcentric healthcare perspective that enables effective and efficient healthcare operations.

INTRODUCTION Healthcare is an information rich, knowledge intensive environment. In order to treat and diagnose even a simple condition a physician must combine many varied data elements and information. Such multispectral data must be carefully integrated DOI: 10.4018/978-1-60566-701-0.ch011

and synthesized to allow medically appropriate management of the disease. Given the need to combine data and information into a coherent whole and then disseminate these findings to decision makers in a timely fashion, the benefits of ICT to support decision making of the physician and other actors throughout the healthcare system are clear (Wickramasinghe et al., 2006). In fact, we see the proliferation of many technologies such as

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HER (health electronic records), PACS (picture archive computerized systems) systems, CDSS (clinical decision support systems) etc. However and paradoxically, the more investment in ICT by healthcare the more global healthcare appears to be hampered by information chaos which in turn leads to inferior decision making, ineffective and inefficient operations, exponentially increasing costs and even loss of life (Wickramasinghe et al, 2005; 2006). The reason for this lies in the essentially platform centric application of ICT to date within healthcare, which at the micro level do indeed bring some benefits but at the macro level only add to the problem by creating islands of automation and information silos that hinder rather than enable and facilitate the smooth and seamless flow of relevant information to any decision maker when and where such information is required. To remedy this problem and maximize the potential afforded by ICT and consequently alleviate the current problems faced by healthcare, the adoption of a networkcentric approach to healthcare operations would appear to be prudent. Such a networkcentric approach is grounded in a process oriented view of knowledge generation and the pioneering work of Boyd (von Lubitz & Wickramasinghe, 2006ab; von Lubitz & Wickramasinghe; 2005; Boyd, 1987).

BACKGROUND: knowledge creation The processes of creating and capturing knowledge, irrespective of the specific philosophical orientation (i.e. Lockean/Leibnitzian versus Hegelian/Kantian), has been approached from two major perspectives; namely a people-oriented perspective and a technology-oriented perspective.

The People-Oriented Perspective This section briefly describes three well known people-oriented knowledge creation frameworks: namely, Nonaka’s Knowledge Spiral, Spender’s and Blackler’s respective frameworks. According to Nonaka (Nonaka, 1994; Nonaka & Nishiguichi; 2001): (1) Tacit to tacit knowledge transformation usually occurs through apprenticeship type relations where the teacher or master passes on the skill to the apprentice. (2) Explicit to explicit knowledge transformation usually occurs via formal learning of facts. (3) Tacit to explicit knowledge transformation usually occurs when there is an articulation of nuances; for example, as in healthcare if a renowned surgeon is questioned as to why he does a particular procedure in a certain manner, by his articulation of the steps the tacit knowledge becomes explicit and (4) Explicit to tacit knowledge transformation usually occurs as new explicit knowledge is internalized it can then be used to broaden, reframe and extend one’s tacit knowledge. These transformations are often referred to as the modes of socialization, combination, externalization and internalization respectively (ibid). Spender draws a distinction between individual knowledge and social knowledge (yet another duality), each of which he claims can be implicit or explicit (Newell et al., 2002). From this framework we can see that Spender’s definition of implicit knowledge corresponds to Nonaka’s tacit knowledge. However, unlike Spender, Nonaka doesn’t differentiate between individual and social dimensions of knowledge; rather he merely focuses on the nature and types of the knowledge itself. In contrast, Blackler (ibid) views knowledge creation from an organizational perspective, noting that knowledge can exist as encoded, embedded, embodied, encultured and/ or embrained. In addition, Blackler emphasized that for different organizational types, different types of knowledge predominate, and highlights the connection between knowledge and orga-

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nizational processes (ibid). Blackler’s types of knowledge can be thought of in terms of spanning a continuum of tacit (implicit) through to explicit with embrained being predominantly tacit (implicit) and encoded being predominantly explicit while embedded, embodied and encultured types of knowledge exhibit varying degrees of a tacit (implicit) /explicit combination. In trying to integrate these various perspectives (Figure 1), what we see is that Spender’s and Blackler’s perspectives complement Nonaka’s conceptualization of knowledge creation and more importantly do not contradict his thesis of the knowledge spiral wherein the extant knowledge base is continually being expanded to a new knowledge base, be it tacit /explicit (in Nonaka’s terminology), implicit / explicit (in Spender’s terminology), or embrained / encultured / embodied / embedded / encoded (in Blackler’s terminology). What is important to underscore here is that these three frameworks take a primarily people-oriented perspective of knowledge creation. In particular, Nonaka’s framework, the most general of the

three frameworks, describes knowledge creation in terms of knowledge transformations as discussed above that are all initiated by human cognitive activities. Needless to say that both Spender and Blackler’s respective frameworks also view knowledge creation through a primarily people oriented perspective. Typically, Hegelian and Kantian inquiring systems would incorporate knowledge creation that is consistent with peopleoriented perspectives (Malhotra; 1997).

The Technology-Oriented Perspective In contrast to the above primarily people-oriented perspectives pertaining to knowledge creation, knowledge discovery in databases (KDD) (and more specifically data mining), approaches knowledge creation from a primarily technologyoriented perspective. In particular, the KDD process focuses on how data is transformed into knowledge by identifying valid, novel, potentially useful, and ultimately understandable patterns in

Figure 1.The people perspective of knowledge generation

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data (Fayyad et al., 1996). KDD is primarily used on data sets for creating knowledge through model building, or by finding patterns and relationships in data. How to manage such newly discovered knowledge and other organizational knowledge is at the core of knowledge management. Figure 2 summarizes the key steps within the KDD process; while it is beyond the scope of this chapter to describe in detail all the steps which constitute the KDD process, an important duality to highlight here is that between exploratory and predictive data mining. Typically, Lockean and Leibnizian inquiring systems would subscribe to a technology-oriented perspective for knowledge creation (Malhotra; 1997).

The Process-Oriented Perspective Within knowledge management then, the two predominant approaches to knowledge creation as discussed above are the people centric and the technology centric perspectives (Wickramasinghe, 2006; von Lubitz & Wickramasinghe; 2006c). Essential to the perspective of knowledge creation is that knowledge is created by people and that new knowledge or the increasing of the extant knowledge base occurs as a result of human cognitive activities and the effecting of specific knowledge transformations [ibid, Figure 1]. In contrast, a technology driven perspective

to knowledge creation is centred around the computerized technique of data mining and the many mathematical and statistical methods available to transform data into information and then meaningful knowledge [Figure 2] (Fayyad et al., 1996; von Lubitz & Wickramasinghe; 2006c; Adriaans and Zantinge,1996; Cabena et al., 1998; Bendoly, 2003). A process centric approach to knowledge creation not only combines the essentials of both the people centric and technology centric perspectives but also emphasises the dynamic and on going nature of the process of knowledge creation itself and supports simultaneously the Lokean/Leibnizian and Hegelian/Kantian systems of inquiry. Process centred knowledge generation is grounded in the pioneering work of Boyd and his OODA Loop, a conceptual framework that maps out the critical process required to support rapid decision making and extraction of critical, germane knowledge (Boyd, 1987; von Lubitz & Wickramasinghe; 2006c). The Loop is based on a cycle of four interrelated stages essential to support critical analysis and rapid decision making that revolve in both time and space: Observation followed by Orientation, then by Decision, and finally Action (OODA). At the Observation and Orientation stages, implicit and explicit inputs are gathered or extracted from the environment

Figure 2. The technical perspective of knowledge generation

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(Observation) and converted into coherent information (Orientation). The latter determines the sequential Determination (knowledge generation) and Action (practical implementation of knowledge) steps [ibid, Figure 3]. The outcome of the Action stage then affects, in turn, the character of the starting point (Observation) of the next revolution in the forward progression of the rolling loop. Given that healthcare is such a knowledge rich environment that requires rapid decision making to take place that has far reaching consequences, a process-centred approach to knowledge generation is most relevant and forms the conceptual framework for network-centric healthcare operations.

NETWORK-CENTRIC HEALTHCARE OPERATIONS Healthcare, like all activities conducted in complex operational space, both affects and requires the functioning of three distinct entities, i.e. people

process and technology. To capture this dynamic triad that continually impacts all healthcare operations, the doctrine of healthcare network-centric operations is built around three entities that form mutually interconnected and functionally related domains. Specifically these domains include (von Lubitz & Wickramasinghe, 2006a,b, 2005): 1) a physical domain that: a. represents the current state of healthcare reality; b. encompasses the structure of the entire environment healthcare operations intend to influence directly or indirectly, e.g., elimination of disease, fiscal operations, political environment, patient and personnel education, etc.; c. has data within it that are the easiest to collect and analyze, especially that they relate to the present rather than future state; d. is also the territory where all physical assets (platforms) such as hospitals, clinics, administrative entities, data

Figure 3. The Process Perspective of Knowledge Generation

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management facilities, and all other physical subcomponents (including people) reside. 2) an information domain that: a. contains all elements required for generation, storage, manipulation, dissemination/sharing of information, and its transformation and dissemination/ sharing as knowledge in all its forms; b. within the information domain, all aspects of command and control are communicated and all sensory inputs gathered; c. while the information existing within this domain may or may not adequately represent the current state of reality, all our knowledge about that state emerges, nonetheless, from and through the interaction with the information domain; d. all communications about the state of healthcare take place through interactions within this domain; e. the information domain is particularly sensitive and must be protected against intrusions that may affect the quality of information contained within domain. 3) A cognitive domain that: a. constitutes all human factors that affect operations; b. is within the cognitive domain that deep situational awareness is created, judgments made, and decisions and their alternatives are formulated; c. also contains elements of social attributes (e.g., behaviours, peer interactions, etc.) that further affect and complicate interaction with and among other actors within the operational sphere. In essence, these domains cumulatively serve to capture and then process all data and information from the environment and given the dynamic

nature of the environment new information and data must always be uploaded. Thus, the process is continuous in time and space captured by the ‘rolling nature’ of Boyd’s OODA Loop (i.e. is grounded in the process oriented perspective of knowledge generation).

ICT USE IN HEALTHCARE NETWORK-CENTRIC OPERATIONS The critical technologies for supporting healthcare network-centric operations are not new, rather they are reconfigurations of existing technologies including web and Internet technologies. The backbone of the network is provided by WHIG (world healthcare information grid) (von Lubitz & Wickramasinghe, 2006ab; von Lubitz & Wickramasinghe; 2005). WHIG consists of three distinct domains that are each made up of multiple grids all interconnecting to enable complete and seamless information and data exchange throughout the system. Figure 4 depicts the WHIG with its distinct yet interconnected domains each made up of interconnecting grids. The three essential elements of the grid architecture are the smart portal which provides the entry point to the network, the analytic node and the intelligent sensors [ibid]. Taken together these elements make up the knowledge enabling technologies to support and effect critical data, information and knowledge exchanges that in turn serve to ensure effective, efficient healthcare operations. In network-centric healthcare operations the entry point or smart portal must provide the decision maker with pertinent information and germane knowledge constructed through the synthesis and integration of a multiplicity of data points; i.e. support and enable OODA thinking. Unlike current web pages in general and especially current medical webportals and on-line databases such as MedLine, that provide the decision maker with large amounts of information that he/she must then synthesise and determine relative and general relevance; i.e.

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they are passive in nature, the smart portal enables the possibility to access the critical information required to formulate the Action (practical implementation) stage of Boyd’s Loop. In addition, the smart portal includes the ability to navigate well through the grid system; i.e. the smart portal must have a well structured grid map to identify what information is coming from where (or what information is being uploaded to where). In order to support the ability of the smart portal to bring all relevant information and knowledge located throughout the grid system to the decision maker there must be universal standards and protocols that ensure the free flowing and seamless transfer of information and data throughout WHIG; the ultimate in shared services. Finally, given the total access to WHIG provided by the smart portal to the decision maker it is vital that the highest level of security protocols are maintained at all times; thereby ensuring the integrity of WHIG. Figure 5 captures all these key elements of the smart portal. The analytic nodes of the WHIG perform all the major intelligence and analysis functions and Figure 4. The WHIG Node Structure

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must incorporate the many tools and technologies of artificial intelligence and business analytics including OLAP (on-line analytic processing), genetic algorithms, neural networks and intelligent agents in order to continually assimilate and analyze critical data and information throughout the grid system and/or within a particular domain. The primary role of these analytic nodes is to enable the systematic and objective process of integrating and sorting information or support the Orientation stage of Boyd’s Loop. Although we discuss the functional elements of the analytic node separately, it is important to stress that the analytic node is in fact part of the smart portal. In fact, the presence of the analytic node is one of the primary reasons that the smart portal is indeed “smart” or active rather than its more passive distant cousin the integrated e-portal that dominates many intranet and extranet sites of ebusinesses today. The final important technology element of WHIG is the intelligent sensor. These sensors are essentially expert systems or other intelligent detectors programmed to identify

Knowledge Management: The Key to Delivering Superior Healthcare Solutions

changes to WHIG and data and/or information within a narrow and well defined spectrum, such as for example, an unusually high outbreak of anthrax in a localized geographic region, which would send a message of a possible bio-terrorism attack warning to the analytic node, or perhaps the possibility of spurious or corrupt data entering the WHIG system. The sensors are not necessarily part of the smart portal and can be located throughout WHIG independent of the analytic nodes and smart portals Figure 5 depicts the three essential technical components of WHIG. In explanation of Figure 5, data, information, or queries from WHIG enter through the portal where they are subjected to security/standards/protocol screening then various intelligence techniques (e.g. data mining and business intelligence) are performed. The latter provides detailed sorting and redirection via intra and extra nets, and/or Internet/ Web to other locations within the node, e.g., patient records, information storage sites, analysis and knowledge generating sites, etc. (unidirectional arrows.) All sites within the node are capable of multidirectional communication (not indicated for the sake of clarity). Their output are transmitted

to the Knowledge Manipulation and Generation site which, in turn, generates final output stored within the node and also disseminated throughout the network (Out). If needed, the node can distribute additional WHIG-wide queries. Replies are collected, manipulated at the KM level, and incorporated into the final node output. Although neither the portal nor individual functional aspects of the node need be collocated, their operations are conducted as a single, self-contained unit, i.e., none of the constituting elements can participate individually in the functions of another node. Self-containment of each node adds to its security and reduces the risk of inadvertent network-wide dissemination of integrity-compromising factors (e.g., viruses, spurious data, etc.).

KNOWLEDGE DEVELOPMENT, SUPPORT AND DISSEMINATION Research by von Lubitz and Wickramasinghe (2006a,b) has pointed out that healthcare information quality depends inversely on its range,

Figure 5. The Node and Smart Portal

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i.e., the shorter the distance between the source and recipient, and the lesser degree of information content manipulation, the higher the quality. Similar observations have been made by other authors in the context of military activities whose complexity closely matches that of healthcare (Alberts et al., 2000). At the moment, and even more so in the future, the highest quality of healthcare information reposes within medical libraries associated with major medical centers around the globe. However, despite over a twenty year long history of IAIMS (Integrated Advanced Information Management System) initiative (Matheson, 1995) and increasing need for a drastic change of operational philosophy (Kronenfeld, 2005; Blansit and Connor; 1999), the majority of medical libraries continue to function as the repositories for print-based knowledge (or its electronically disseminated substitute) whose participation in healthcare operations is driven by customer demand (essentially passive) rather than operate as dynamic, knowledge developing and disseminating entities capable of actively shaping the healthcare world. As pointed out by several authors [37-39] (Blansit, 1999; duVal, 1967; Fuller et al., 1999) future medical libraries must “filter, focus, and interpret information” (Stead, 1998) and “distribution of information, not control, is key to establishing, and maintaining power” (Martin, 1997). In the context of networkcentric healthcare operations the role of medical libraries transforms even further – the library becomes a node. Presently, major strides are made toward practical incorporation of the IAIMS concept in reality (McGowan et al., 2004; Guard et al., 2004). However, global scale ‘network-centricity’ demands capabilities extending beyond “reliable, secure access to information that is filtered, organized, and highly relevant to specific tasks and needs…” (McGowan et al., 2004). In addition to these essential requirements, networkcentric operations demand merging of multispectral information streams into coherent, operation-centered knowledge bases, development of real-time or near real

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time operational space awareness, and predictive capabilities that are beyond the current scope of medical library operational profiles. Thus, contrary to the technologically advanced library of today, the library-node of tomorrow must adopt Boyd’s Loop principles of interaction with the environment as the principal philosophy of its interaction with the information world within which it functions (von Lubitz & Wickramasinghe, 2006b). Adaptation of such philosophy is also the critical step in transforming operational profile of the existing medical libraries from essentially passive repositories which, with varying degree of efficiency and reliability, transform the reposited information into coherent knowledge-base blocks, into active information seeking entities (nodes) that conduct their exploratory work not only within their pre-determined domain of healthcare, but also within all other domains whose content may be potentially relevant to healthcare itself. There is no doubt that the proposed change is fundamental. On the other hand, it is the change that moves the medical library beyond its current notion of the institutional “networked biomedical enterprise” [40] into a global-level knowledge development, -management and -dissemination center. Most significantly, aligning such centers within the WHIG structure will lead to a massive enhancement of their overall operational power which (von Lubitz & Wickramasinghe, 2006a), accordingly to Metcalf’s law, increases in proportion to the square of the nodes connected to the network.

INFORMATION INTEGRITY Given the significance of WHIG to network-centric healthcare and the importance of high quality information to support the rapid decision making activities and the reduction of information asymmetry relative to the environment, it is essential that the information that flows through WHIG is reliable. One technique in order to ensure that the

Knowledge Management: The Key to Delivering Superior Healthcare Solutions

information and/or knowledge accessed from the grid structure is indeed reliable, relevant and of a high quality is to ensure that all information and knowledge stored throughout the network is structured so that it meets key criteria of information integrity and quality aims. Such criteria include that all information accessed should display the attributes of accuracy, consistency, and reliability of content and processes as well as the dimensions of usefulness, completeness, manipulability and usability (Wickramasinghe & Fadlalla, 2004; Huang et al., 1999). Table 1 highlights these dimensions of information integrity: Implicit in taking an Information Integrity perspective is the shift from viewing information as a byproduct to viewing it as an essential product (Wickramasinghe & Fadlalla, 2004). Such a perspective is paramount in a networkcentric healthcare domain given its goal of the attainment

of information superiority in order to enable the delivery of quality healthcare delivery in the healthcare space. The following four key principles should be adhered to at all times when information exchanges (either accessing or uploading of information to the grid) at the node take place; namely the information must 1) meet the consumers information needs, 2) be the product of a well defined information production process 3) be managed by taking a life-cycle approach and 4) be managed and continually assessed vis-à-vis the integrity of the processes and the resultant information (Cebrwoski & Garstka, 1998; Wickramasinghe & Fadlalla, 2004; Huang et al., 1999). This in turn requires that specific protocols must be enforced at the design stage of the node and that sensors within the WHIG can be used to detect information and data that is both spurious or failing to meet one or more of the

Table 1. Information Integrity (Wickramasinghe & Fadlalla, 2004; Huang et al., 1999) Component

Key dimensions

Accuracy

Information must be correct: • Information Content Accuracy • Process Logic Correctness and Accuracy • System Accuracy to Specifications

Reliability

Information must be from a sound source and verifiable: • Information Currency • Information Auditability

Consistency

Information must not change unless the circumstances themselves change: • Content Consistency • Temporal/Spatial Consistency • Relational Consistency • Process/System Consistency • Standardization

Completeness

Information should contain all available data element: • Collectively Exhaustive • Minimal missing data points

Usefulness

Information that is stored and accessed must be required for a specific tasks: • Information Relevancy • Germane knowledge • Value Added Characteristic

Usability

Information that is stored and accessed must be in a form that it can be applied to a given context easily: • Information Simplicity • Information Portability

Manipulability

Information should be able to support understanding, decision making and analysis: • Content Richness • Contextual Coverage

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criteria for information integrity on an on going basis.

DISCUSSION AND CONCLUSION At its most fundamental (and maybe also the most naïve) healthcare is about assuring and maintaining individual’s adequate level of health necessary to function as a fully capable member of the society. In reality, healthcare, particularly in its global context, became a business growing at an unprecedented rate, where global disparities in healthcare delivery become increasingly more apparent, where technology emphasizes them rather than assists in their obliteration, and where the current expenditure of trillions of dollars yearly appears to have no impact at all. Part of the problem rests with the fact that the majority (if not all) solutions to the healthcare crisis are, essentially, ‘platform-centric’, i.e. concentrate on the highly specific needs of a specialty (e.g., molecular biology), an organization (e.g., hospital) or a politically defined region (e.g., US or EU). Hence, most of the technology-based solutions, while highly functional and of unquestionable benefit to their users, fail to act as collaborative tools assisting in the unification rather than subdivision of effort. Highly useful information generated within individual systems is, for all practical purposes, lost since it is inaccessible to others either because of its incompatibility with different operational platforms or simply because others are not even aware of its existence! The latter issue becomes particularly significant when relevant information exists within healthcareunrelated domains. Particularly apt and very recent example of such failure were the recovery efforts after the tsunami disaster of 2004, where the world dispatched badly needed medical supplies to the affected regions but failed to relate the transport to on site distribution. The supplies piled up at major airports while healthcare workers in the field were short of the most basic commodities.

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The currently practiced approach to healthcare informatics supports reoccurrence of similar events: for all practical purposes healthcare informatics limits its sphere of activity only to subjects strictly related to medicine, its practice, and administration at the healthcare organization level. Yet, healthcare relates to a number of other elements of life – political structure of the region, its stability, its economy, even its weather. By taking a myopic platform centric perspective to healthcare delivery the current problems and challenges facing healthcare delivery should not be a surprise. These challenges include inability to transfer critical information seamlessly throughout the healthcare network, inability to have the best available information and knowledge to support decision making, escalating costs due to inherent inefficiencies, inferior treatment outcomes and even deaths. In today’s knowledge economy where ICT use is a necessity for conducting and enabling effective business operations and a global business perspective is essential it would appear that to enable and support superior healthcare operations a network-centric approach that is integrally connected to the process perspective of knowledge management and reliant on a complex technology grid (WHIG) may provide the key. What is certain is that without a radical redesign of current healthcare operations the healthcare industry will continue to be a laggard and healthcare delivery will always be suboptimal. In closing, it is important to note that the proposed network centric approach to healthcare delivery also serves to underscore the inextricable connection and intertwining of e-health and e-government which to date has rarely been researched if at all. Moreover, for such a model to become adopted successfully it requires governments to develop polices and protocols which will in turn facilitate its usability. This will include at least four key areas that will have an important impact on the development of these necessary policies and protocols; namely, the following factors:

Knowledge Management: The Key to Delivering Superior Healthcare Solutions

1) IT education: ◦⊦ A sophisticated, well educated population boosts competition and hastens innovation; ◦⊦ One of the key factors to a country’s strength in an industry is strong customer support; ◦⊦ The health consumer is the key driving force in pushing e-health initiatives; ◦⊦ a more IT educated healthcare consumer would then provide stronger impetus for e-health adoption. 2) Morbidity: ◦⊦ There is a direct relationship between health education and awareness and the overall health standing of a country; ◦⊦ A more health conscious society, which tends to coincide with a society that has a lower morbidity rate, is more likely to embrace e-health initiatives; ◦⊦ Higher morbidity rates tend to indicate the existence of more basic health needs and hence treatment is more urgent than the practice of preventative medicine and thus e-health could be considered an unrealistic luxury; ◦⊦ Thus, the modifying impact of morbidity rate is to prioritize the level of spending on e-health versus other basic healthcare needs. 3) Cultural/social dimensions: ◦⊦ Healthcare has been shaped by each nation’s own set of cultures, traditions, payment mechanisms and patient expectations; ◦⊦ While the adoption of e-health, to a great extent, dilutes this cultural impact, social and cultural dimensions will still be a moderating influence on any countries e-health initiatives;

◦⊦

Another aspect of the cultural/social dimension relates to the presentation language of the content of the ehealth repositories; ◦⊦ The entire world does not speak English so the e-health solutions have to be offered in many other languages; ◦⊦ The e-health supporting content in web servers/sites must be offered in local languages, supported by pictures and universal icons; ◦⊦ Therefore, for successful e-health initiatives it is important to consider cultural dimensions. 4) World economic standing: ◦⊦ Economies of the future will be built around the Internet; ◦⊦ All governments are very aware of the importance and critical role that the Internet will play on a country’s economy; ◦⊦ This makes it critical that appropriate funding levels and budgetary allocations become a key component of governmental fiscal policies so that such initiatives will form the bridge between a traditional healthcare present and a promising e-health future. Thus, the result of which would determine success of effective e-health implementations and consequently have the potential to enhance a country’s economy and future growth. Interestingly enough, however these areas also impact the development of many e-government initiatives. Therefore, while knowledge management driven ICT innovations for healthcare delivery hold the key to enabling the delivery of superior healthcare operations it must not be forgotten that such innovations will only be successful if the necessary policy and government infrastructure is in place to support the correct levels of competition and collaboration between and within the healthcare web of players.

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REFERENCES Adriaans, P., & Zantinge, D. (1996). Data mining. Boston: Addison-Wesley. Alberts, D. S., Garstka, J. J., & Stein, F. P. (2000). Network centric warfare: Developing and leveraging information superiority (pp. 1-284). Washington, DC: CCRP Publication Series, Department of Defense. Retrieved from http://www.dodccrp. org/publications/ pdf/Alberts_NCW.pdf Bendoly, E. (2003). Theory and support for process frameworks of knowledge discovery and data mining from ERP systems. Information & Management, 40, 639–647. doi:10.1016/S03787206(02)00093-9 Blansit, B. D., & Connor, E. (1999). Making sense of the electronic resource marketplace: Trends in health related electronic resources. Bulletin of the Medical Library Association, 87, 243–250. Boyd, J. R. (1987). [Unpublished briefing]. Retrieved as “Essence of Winning and Losing” from http://www.d-n-i.net

Fuller, S. S., Ketchell, D. S., Tarczy-Hornoch, P., & Masuda, D. (1999). Integrating knowledge resources at the point of care: Opportunities for the librarians. Bulletin of the Medical Library Association, 87, 393–403. Guard, J. R., Brueggeman, R., Hutton, J. J., Kues, J. R., Marine, S. A., Rouan, W., & Schick, L. (2004). Integrated advanced information management system: A twenty-year history at the University of Cincinnati. Journal of the Medical Library Association, 92, 171–178. Haag, S., Cummings, M., & McCubbrey, D. (2004). Management information systems for the information age (4th ed.). Boston: McGraw-Hill Irwin. Huang, K., Lee, Y., & Wang, R. (1999). Quality information and knowledge. Upper Saddle River, NJ: Prentice Hall. Jessup, L., & Valacich, J. (2005). Information systems today (2nd ed.). Upper Saddle River, NJ: Prentice Hall.

Cabena, P., Hadjinian, P., Stadler, R., Verhees, J., & Zanasi, A. (1998). Discovering data mining from concept to implementation. Upper Saddle River, NJ: Prentice Hall.

Kronenfeld, M. R. (2005). Trends in academic health sciences libraries and their emergence as the “knowledge nexus” for their academic health centers. Journal of the Medical Library Association, 93, 32–39.

Cebrowski, A. K., & Garstka, J. J. (1998). Network-centric warfare: Its origin and future. US Nav. Inst. Proc., 1, 28–35.

Laudon, K., & Laudon, J. (2004). Management information systems (7th ed.). Upper Saddle River, NJ: Prentice Hall.

DuVal, M. K. (1967, June 14). The changing role of the library. Public address at “The Emerging Role of Medical Libraries” session of the 66th Annual Meeting of the Medical Library Association, Miami, FL.

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Martin, C. (1997). Digital estate: Strategies for competing, surviving, and thriving in an internetworked world. New York: McGraw Hill.

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Matheson, N. W. (1995). Things to come: Postmodern digital knowledge management and medical informatics. Journal of the American Medical Informatics Association, 2, 73–78.

von Lubitz, D., & Wickramasinghe, N. (2006b). Networkcentric healthcare: Outline of entry portal concept. International Journal of Electronic Business Management, 1, 16–28.

McGowan, J. J., Overhage, J. M., Barnes, M., & McDonald, C. J. (2004). Indianapolis I3: The third generation Integrated Advanced Information Management Systems. Journal of the Medical Library Association, 92, 179–187.

von Lubitz, D., & Wickramasinghe, N. (2006c). Creating germane knowledge in dynamic environments. International Journal of Innovation and Learning, 3(3), 326–347. doi:10.1504/IJIL.2006.009226

Newell, S., Robertson, M., Scarbrough, H., & Swan, J. (2002). Managing knowledge work. New York: Palgrave. Nonaka, I. (1994). A dynamic theory of organizational knowledge creation. Organization Science, 5, 14–37. doi:10.1287/orsc.5.1.14 Nonaka, I., & Nishiguchi, T. (2001). Knowledge emergence. Oxford: Oxford University Press. O’Brien, J. (2005). Management information systems (6th ed.). Boston: Irwin-McGrawHill. Stead, W. W. (1998). Positioning the library at the epicenter of the networked biomedical enterprise. Bulletin of the Medical Library Association, 86, 26–30. von Lubitz, D., & Wickramasinghe, N. (2005). Networkcentric healthcare and bioinformatics. International Journal of Expert Systems with Applications, 30, 11–23. von Lubitz, D., & Wickramasinghe, N. (2006a). Healthcare and technology: The doctrine of networkcentric helahtcare. International Journal of Electronic Healthcare, 4, 322–344.

Wickramasinghe, N. (2003). Do we practise what we preach:Are knowledge management systems in practice truly reflective of knowledge management systems in theory? Business Process Management Journal, 3, 295–316. doi:10.1108/14637150310477902 Wickramasinghe, N. (2006). Knowledge creation:A meta-framework. International Journal of Innovation and Learning, 3(5), 558–573. Wickramasinghe, N., Bloomendal, H., de Bruin, A., & Krabbendam, K. (2005). Enabling innovative healthcare delivery through the use of the focused factory model: The case of the spine clinic of the future. International Journal of Innovation and Learning, 1, 90–110. doi:10.1504/IJIL.2005.006086 Wickramasinghe, N., & Fadlalla, A. (2004). An integrative framework for HIPAA-compliant I*IQ healthcare information systems. International Journal of Health Care Quality Assurance, 2, 65–74. Wickramasinghe, N., Geisler, E., & Schaffer, J. (2006)... International Journal of Healthcare Technology and Management, 7(3-4), 303–318.

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Chapter 12

The Use of ‘Web 2.0’ and Social Software in Support of Professional Learning Communities Alan Eardley Staffordshire University, UK Lorna Uden Staffordshire University, UK

ABSTRACT This chapter examines the ‘happy convergence’ of two emerging social and technological trends. The first is the evolution of educational processes and methods from a traditional didactic approach towards a paradigm that seeks to empower the learner and enable a more involving learning experience to take place. This paradigm includes such approaches as student-centred learning, collaborative learning and problem-based learning. The second is the development of IT-based systems that enable the democratic involvement of end-users in their development and use and that encourage computer-mediated collaboration between individuals and groups having a common interest in a domain. Initially, at least, the main purpose of such software was for social networking and leisure purposes, but the chapter identifies a number of instances of its use in practice for professional education purposes. The chapter then highlights some examples of professional learning communities in practice in UK educational institutions. It concludes by speculating on and discussing some possible future trends in the use of social software for professional learning and by summarising the phenomenon and identifying the factors that distinguish it from other approaches to learning.

INTRODUCTION Social software is normally defined as a range of web-based software programs that allow users

to interact and share data with other users (i.e. computer-mediated communication of CMC). Social software supports a ‘lifestyle’ in which users employ CMC to maintain virtual contact and maintain social relationships. ‘Social’ sites

DOI: 10.4018/978-1-60566-701-0.ch012

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The Use of ‘Web 2.0’ and Social Software in Support of Professional Learning Communities

(e.g. Facebook and MySpace) ‘personal media’ sites (e.g. Flickr and YouTube) and a variety of on-line forums and discussion groups are enjoying a rapid growth in use, firstly for social networking and then as professional and learning support tools. Social (or conversational) technologies are now used in many organisations to enable the process of knowledge creation and storage (i.e. the ‘knowledge cycle’) that is enacted through collaborative writing. Constructivist learning theorists (e.g. Leidner & Jarvenpaa, 1995) explain that the process of expressing knowledge in an explicit form aids its creation and that conversation based on knowledge (i.e. discussion critical review) aid the refinement of knowledge. Social software fulfills this purpose because conversations on predetermined topics (e.g. a discussion forum) become a valuable source of knowledge in a community and contributions to the knowledge base by members of the community become a useful form of reference (Hasan & Pfaff, 2006a). Social software that supports conversation and encourages democratic contribution in this way (e.g. ‘wikis’, blogs and forums) provides support to the individual ‘knowledge worker’ and to the ‘online community’ which results from its use. (Hasan & Pfaff, 2006b). The word ‘professional’ originally had connotations of payment for work produced (this still applies in some field such as sport), but over time has gained a definition based on a body of knowledge and a system of regulation. A ‘professional’ is often required to possess a large body of knowledge in common with others in the same profession. This knowledge will be derived from training based on both experience and academic study (usually at the tertiary level), with a formal level of attainment almost always specified. Professionals are to a degree subject to self-regulation in that their professional body usually controls the evaluation processes that admit new professionals, administers the training and assessment processes and judges whether the work done by its members is up to ‘professional standard’.

Thus, professionals are subject to internal control, whereas in other kinds of work regulation (if it exists at all) is imposed externally. Professionals usually have autonomy in the workplace - they are expected to use their independent judgment and exercise ethical standards in carrying out their work. This implies a tension between the self-regulated group, the common body of knowledge and the democratic rights and independence of the individual. This tension is the basis of the Professional Learning Community. In terms of the learning process, the last decade has included some fundamental changes in the way the nature and purpose of education is perceived (Estes, 2004), with a growing emphasis on supporting learners, not by a teacher (or teaching surrogate) providing knowledge and information to the learner but by developing in the learner the necessary resources and skills to engage constructively with continuous change (Iyoshi et al., 2005) and to promote ‘life-long learning’. This view tends to place the learning experience in a social context – and makes a ‘constructive conjunction’ of collaborative learning and social software not only possible but almost inevitable. The phenomenon of student-centred learning is based on the epistemology of constructivism theory (which is generally attributed to Jean Piaget) which attempts to describe how knowledge is ‘internalised’ by learners through processes called assimilation and accommodation. Piaget contended that individuals continually construct new knowledge from their personal experiences, and when individuals assimilate they ‘add’ the new experience to an existing framework of learning and construct a perception of the world (or of a body of knowledge) based on the accumulation of those experiences without necessarily changing that framework. This process may be enhanced or augmented by cognitive processes such as reflection, discussion and critical review. It is significant that at least two of these processes are collaborative – discussion by definition and critical review by implication.

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According to constructivism, accommodation involves the learner in reframing this mental representation of the external world (i.e. the learned framework) to fit in with new experiences. Simplistically, therefore, the learner’s knowledge base is created by assimilation and modified (or updated) by accommodation and so it is the learners who actually construct knowledge through a process of active learning. This process, it is contended, can be enhanced by engaging with other learners who are faced with the same problems, involved in similar activities and having common experiences (Anhuradha, 1995). This is ‘collaborative learning’, which is learning approach in which the learners work together toward a common goal and are responsible for each another’s learning activities (i.e. assimilation and accommodation) as well as their own. It is enlightening to compare the group learning approach and the promotion of self-generating learning practices with the traditional ‘didactic’ approach. Proponents of collaborative learning claim that the active exchange of ideas within small groups not only increases interest among the participants but also promotes discussion and critical thinking (Atherton, 2005). Although much of Piaget’s original work concentrated on the learning processes of children, subsequent thinkers have extended his model to include adult or professional learners or developed more sophisticated professional models based on constructivist principles (e.g. Kegan, 1982).

PROFESSIONAL LEARNING Hord (1997) notes that there is no universally agree definition of a professional learning community (PLC). In fact, the definition of a PLC has proved difficult to capture, even though there are claims (e.g., Morrissey, 2000) that the term PLC is selfdefining. Toole and Louis (2002) propose that a professional learning community (PLC) is a group (virtual or real) of professionals who are engaged in common work; share a common set of values

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and who operate collaboratively using systems that foster interdependence among the members (Carpenter & Matters, 2003). Accordingly, we suggest that the concept of a PLC is composed of three inter-dependent domains, as follows: •

•

•

Professional - a community that emphasises personal connections and commonly accepted (or self-regulated) standards; Learning - learning culture that emphasises a learner-centred, knowledge based approach and an emphasis on inquiry and reflection; Community - a framework of conditions, structural supports and human and social resources that enable and support the interactions between learners.

Additionally, Hord (1997) suggests that an ‘ideal’ type of PLC can exist in practice, which can be used as a model and Eraut (1994) points out that the law and medicine have for some time been regarded as ‘ideal type’ professions, while Etzioni (1969) regarded teaching and nursing as ‘semi-professions’. Significantly, each of these are good examples of CoPs and PLCs, but it is not always possible to identify a direct link between a profession and its community in terms of practice or learning. Grossman et al. (2000) note that a group of professional staff is not necessarily a ‘community’ if it lacks a purpose (e.g. learning) and Dufour (2004) comments that people use the term PLC to describe every imaginable combination of individuals with a shared interest (in this case education). There are varieties of PL called professional learning from the workplace’ (PLW) or ‘work integrated learning’ (WIL) that are described as ‘pedagogic approaches concerned with integrating academic studies and professional practices so that students, staff, employees and employers can develop their understanding of the reciprocal relationship between education and the world of work’ (see http://www.wmin.ac.uk/page-12991).

The Use of ‘Web 2.0’ and Social Software in Support of Professional Learning Communities

PLW is claimed to encompass, ‘learning for work, learning at work and learning through work’ (Brennan & Little, 1996), aims to prepare learners for professional life using the knowledge and skills acquired in the workplace and is a key part of Continuing Professional Development (CPD) for established professionals. The importance of KM in supporting specialists who carry out similar practices within a shared ‘body of knowledge’ is called a community of practice (CoP). Lave and Wenger (1991, p. 98.) describe a community of practice as, ‘… a set of relations among persons, activity and world, over time and in relation with other tangential and overlapping CoPs’and regard the use of knowledge to promote and enable learning in a CoP as ‘an intrinsic condition for the existence of knowledge’ (p. 98). The following definition of CoPs has been offered, ‘At the simplest level, they are a small group of people... They are peers in the execution of “real work”. What holds them together is a common sense of purpose and a real need to know what each other knows’ (Seely Brown & Solomon Grey, 1995, p. 78). The application of this approach to problem-solving and learning in a CoP involving photocopier technicians is studied by Orr (1990). In this case, the participation in the CoP as identified by Lave & Wenger (1991) is observed as the technicians gain experience by solving repair problems and legitimise their contribution to the CoP by the value of their ‘war stories’ in spreading knowledge that’s helped the other technicians to solve problems. The theme of learning was therefore a major driver of the original concept of a CoP in its initial form, and it is this concept that is of interest in this chapter, extending the idea to a more specialist professional learning community (PLC). Three things can be learned from the case reported by Orr (1990) that relate to PLCs. First, there is the gathering of domain knowledge (e.g. how to solve a particular problem). Secondly, there is the development of the knowledge of practices specific to the CoP (for example knowledge of

the features of an individual photocopier). Thirdly, there is the knowledge that the CoP holds about the potential value of its members in the problemsolving or learning experience (e.g. through their ‘war stories’). In the CoP if a problem had to be solved the members would gather the domain knowledge by interaction and working together to solve the problem – this is the basis of PLC, and it is possible to identify formal examples of PLCs in ‘learning intensive’ areas such as healthcare and education.

SOCIAL SOFTWARE TOOLS The tools used in social software applications can be broadly divided into the categories of communication tools and interaction tools. Communication tools are generally asynchronous and usually capture, store, and disseminate person-to-person and group communications, usually in textual form but increasingly including through audio and video streaming. Examples of such tools are the democratic, user-centred web applications that make up what O’Reilly (2005) calls ‘Web 2.0’: (http://www.oreillynet.com/pub/a/oreilly/tim/ news/2005/09/30/what-is-web-20.html?page=1) Some typical examples of social software include the following: •

Instant Messaging: Instant messaging (IM) applications allow two or more people to communicate privately over a network in real time. Popular applications include Skype, Meebo, Yahoo! Messenger, MSN Messenger and AOL Instant Messenger. A registered user can add friends to a ‘buddy list’, by entering their email or ID and if the ‘friend’ is online at a given time, their name will be listed as available for chat. Clicking on their user name will activate a ‘chat window’ with a text box in which to write to the other person, as well as read

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•

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their reply on a ‘line by line’ message and response basis. Text chat: Internet Relay Chat (IRC) allows registered users to join ‘chat rooms’ and potentially to communicate with many people at once, publicly. Users may join a pre-existing chat room or create a chat room and once ‘inside’, may add messages that all other users in the room can read, as well as respond to messages from the others. Often there will be a steady stream of people entering and leaving, as with a real public room. Internet forums: Originally modeled after the bulletin or notice boards that would be used to make announcements in institutions such as universities, internet forums are usually dedicated to a particular topic (and therefore appeal to a community of interest) and allow registered users to post a “topic” for others to read and respond. A ‘thread’ will be formed as other users view the topic and post their own comments.. Most Internet forums are public, allowing anyone to register, although some impose a ‘probationary period before postings are allowed. A few are private or ‘gated’ and new members may be expected to pay a fee to join. Forums often contain a number of specialist topic areas and many include the ability to post images or files or the ability to quote another user’s post in a response. The larger Internet forums often include several thousand members addressing tens of thousands of topics. The more advanced forums may include translation and/or spelling correction software tools and may be supported by advertising or sponsorship (e.g. Yahoo! Groups and Google Groups). Sub-types of forums include the following: ◦⊦ ‘Blogs.’ A blog (an abbreviation of ‘web log’) is in effect a personal online journal. The ‘blogger’ will post a ‘diary entry’ for public viewing,

◦⊦

often allowing others to add comments. Topics often include the blogger’s daily life and personal views on subjects of importance to him or her or of general interest to readers. Blogs appear in different forms, ranging from an online diary to a personal website that is updated regulary. As with most other types of social software, blogs have gained functionality and increased sophistication. The ‘comments’ area of some blogs is in effect a discussion forum and some also have blog-rolls (i.e., links to other blogs), and may indicate the bloggers links other bloggers (e.g. by using the XFN social relationship standard). Pingback and trackback allow one blog to link to another blog, creating an inter-blog conversation. By making use of these features, Blogs continue to engage readers and can some can sustain a virtual community around a particular person or area of interest (e.g. Slashdot, and BlogSpot). ‘Wikis.’ A ‘wiki’ is a web-based application, the content of which can be created, added, linked and organised by its users, usually for reference purposes. Originally a very basic form of database and then an encyclopaedia, Wikis are now often used to create collaborative and community websites. These ‘wiki websites’ are being used in many organisations to provide affordable and effective Intranets and for Knowledge Management purposes. Wikis are generally designed with the philosophy of making it easy to correct mistakes. So, while wikis are open, they provide a means to check the validity of recent additions and changes, usually through a spe-

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cific list of recent edits. Some critics of wikis argue that the content can easily be corrupted, which is sometimes countered with the view that users will easily identify unwanted content and quickly correct it. Many wiki communities are private, particularly within organisations (e.g. professional communities) and are often used as learning applications. There are also WikiNodes (i.e. pages on wikis) that describe and link to related wikis. One way of finding a wiki on a specific subject is to follow the wiki-node network from wiki to wiki; another is to take a ‘wiki bus tour’ (e.g. the Wikipedia Tour Bus). For those interested in creating their own wiki, there exists publicly available ‘wiki farms’ software (e.g. Socialtext, Wetpaint, and Wikia). For those interested in how to build a successful wiki community, and encourage wiki use, Wikipatterns is a guide to the stages of wiki adoption and a collection of community-building and content-building strategies. This could be ideal for developing learning applications. Social software supports a voluntary community in which the trust and respect of other members is earned, and the community’s direction is defined and its governance is carried out by the members themselves. Communities formed by these ‘bottom-up’ processes may be contrasted to those imposed by IT systems supported by centrally-controlled software, which are developed by an external authority (e.g. ‘the IT department’) and controlled by rigid mechanisms (e.g. access rights). It is worth pointing out that the differences in outcomes between these two types of software can be greater than their differences in design or function. For instance, the open source groupware

content management system Tikiwiki uses the gACL control system to control access and editing (http://info.tikiwiki.org/tiki-index.php) whereas Mediawiki does not have individual controls, allowing most of its content to be edited by most of its users, and uses ‘recent changes’ pages to indicate editorial authorship. This difference can have implications fore the type of social software that a community will prefer to use, depending on the social network paradigm that it embraces. Social software therefore reflects the traits of the social network that uses it and is often designed consciously to exploit the characteristics of that network. All social software systems create links between users, in effect forming a formal, practicing community out of an informal ‘community of interest’. This has important implications for the learning process and for changes that have taken place in that process. It is important to note that the use of social software is becoming increasingly popular for work as well as leisure purposes (indeed, there would appear to be a blurring of the boundaries between the two, as both Sabre Travel Network and Cisco have added social networking applications to their existing software. This type of development to existing Web-based applications is made easier by Asynchronous JavvaScript and XML (AJAX) which enables richer and more powerful applications (e.g. images and maps, etc.) to achieve satisfactory performance using existing Web technology.

EXAMPLES OF PROFESSIONAL LEARNING COMMUNITIES An example of such a PLC in the UK would be the Open University’s Practice-based Professional Learning Centre (OUPBPL) which includes the following among its objectives: •

Use online methods and partnerships to increase opportunities for OU students to engage in effective practice-based learning;

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Apply new advances in mobile technologies and e-learning to practice-based courses.

The Surrey Centre for Excellence in Professional Training and Education (SCEPTre) at the University of Surrey is a partly Governmentfunded centre to support professional learning. The Centre claims to have developed one of the UK’s first campuses dedicated to teaching and collaborative learning. It provides a ‘technologyrich environment to facilitate good conversation, interaction and collaborative learning between tutors, students on campus and in the work place, practitioners in the work place and visiting experts’ (See http://www.surrey.ac.uk/sceptre/index.htm) The Centre’s facilities include Tektura conversation walls, is wireless throughout the campus and features an Access Grid Node (Internet group video conferencing facility), IP videoconferencing with virtual whiteboards (accessed through tablet PCs) to facilitate the demonstration, sharing if ideas and collaborative learning. The Centre has a wiki and a comprehensive suite of learning software. The range of functions and applications is expanding continuously, but includes the following: •

• • • • • •

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A programme of activities and events to support Professional Development and Learning; Information resources to give guidance to professional learning facilitators; A network support base for personnel involved in helping students in work placements; Partnerships with employers to enhance students’ experiences of work placements; A fellowship scheme to promote good practice in professional education practices; A curriculum innovation scheme; A knowledge management scheme to gather and disseminate new knowledge for better practices in professional learning;

•

Partnerships with other Centres of Excellence through the Work Integrated Learning Alliance.

(http://www.surrey.ac.uk/sceptre/index.htm) Another example of a PLC community is the Centre for Excellence in Professional Learning from the Workplace (CEPLW) at the University of Westminster. The work of the Centre is to understand what makes workplace learning effective and rewarding and to explore and develop innovative ways of capturing this learning for individuals or the wider group. The Centre team works with students, academic colleagues, employers and independent practitioners to understand, promote and reward excellence in a variety of areas such as: •

• •

•

How students prepare themselves for the professional work environment, including understanding and matching employer expectations, and developing their abilities through entrepreneurship; Developing inquiry and reflection for selfmanaged and sustainable learning through work; Engaging employers in programme development, as well as mentoring, projectwork and assessment of learning; Working with organisations to support their organisational learning needs and the continuing professional development (CPD) of their staff and members.

CEPLW at the University of Westminster is locally important in enabling lifelong learning, in maintaining professional standards and in individual and organisational professional development. The Centre works as a development and consultancy service unit within the University and with external partners. Its role is to integrate the professional work and learning from the workplace into the academic environment for students and staff of the University. It aims to ‘establish beneficial relationships between academics and

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employers and the service sector that support the lifelong learning of individuals and organisations and the transfer of research and expertise in both directions’. The CEPLW method of operation does not appear to stress the importance of the technology in the collaboration and learning process. (see http://www.wmin.ac.uk/page-13732). The activities that CEPLW undertakes focus on events such as Symposia (local and international) such as the World Association for Co-operative Education (WACE) Symposium in, which brought together many UK-based stakeholders involved in work-integrated learning from many different fields in education and industry, including graduate recruiters and policy-makers in professional training and education. The aim of the symposium was the ‘to debate issues around developing work integrated learning for sustainable futures whilst sharing best practice and also to open networks for solutions’ (see http://www.wmin.ac.uk/page12250). In addition the Centre offers a programme of monthly workshops on different themes and hosted a Work Integrated Learning, Enterprise and Employability event in May 2007. The National Centre for Work Based Learning Partnerships (NCWBLP) at Middlesex University is one of the leading PLCs in the UK, and has embraced learning technology completely. The Centre offers a wide range of higher education courses for over 1,200 students registered worldwide in areas as diverse as Cyprus, the United States, South Africa and Hong Kong. When it was set up, the Centre found that the cost of moving lecturers to the students was prohibitive. It implemented ‘first generation’ learning technology based on video conferencing and web conferencing systems, but found that it did not meet its demands. NCWBLP therefore adopted Adobe® Acrobat® Connect™ Professional and Adobe Captivate™ software to provide a range of interactive eLearning courses with rich multimedia applications (see http://www.adobe.com/ uk/showcase/pdfs/200709_middlesexuniversity. pdf). It is significant in the light of the forego-

ing discussion on collaborative learning in CoPs that Adobe ®, which provides the technology on which the NCWBLP learning environment is based, stresses integration as a major advantage, ‘Adobe Acrobat Connect Professional is an entire system, whereas the other products are just tools.... The Adobe solution has a range of tools available and is an eLearning system in its own right. Microsoft NetMeeting, Yahoo! Messenger, MSN or the flavour of month, Skype, might have something like a whiteboard but Acrobat Connect Professional is an integrated system supporting all the functionality...’ (see http://www.adobe.com/ uk/showcase/pdfs/200709_middlesexuniversity. pdf).

FUTURE TRENDS Future developments in the use of social software in professional learning are likely to centre on improving performance and harnessing newlyemerging technologies, including the following examples: •

•

Peer-to-peer social networks: This technology includes a combination of webbased social networking tools, instant messaging technologies and peer-to-peer file sharing, with a view to improving user-driven file sharing. Peer-to-peer social networks generally allow users to share blogs, files (especially images and video) and instant messages. Some examples are Wirehog, SpinXpress, and Soulseek. Also, KeriKa, Groove and WiredReach have similar functions. The former category of examples appears to be targeted at leisure use, while the latter would appear to offer greater potential to professional and distributed learning applications. Virtual presence: Virtual presence implies that individuals or groups can interact with each other through mediating technolo-

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gies. It usually indicates apparent physical appearance (i.e. a degree of realism) and is likely to improve the learning experience by replacing many of the social cues (e.g. ‘body language’ and subtle gestures) that are lost in most of the current learning technologies. This has the potential to improve group synergy and integrity and improve the learning experience. Personal Learning Environments (PLEs): This type of learning environment is becoming increasingly popular as learners take responsibility for their learning. Many respondents saw these as a key feature of the future. The idea of a Personal Learning Environment acknowledges that learning is continuing and seeks to recognise the role of the individual in organising their own learning (Atwell 2006). Pressures for the further development of PLEs are based on the idea that learning takes place in different contexts and situations and will possibly not be provided by a single learning provider. In addition, the episodes of practical and informal learning may be exploited. Ubiquitous computing is likely to change the way in which learning is approached. (Atwell, 2006). This is likely to apply more to professional learning than any other type. In a survey jointly commissioned by the Research and Policy Advice and the Knowledge Sharing Services Projects in Australia respondents were invited to articulate their visions for the future relating to the use of social software in Personal Learning. Some of the more interesting responses: ◦⊦ ‘Unconferences’ for networking and collaboration, empowering participants as they develop programs together; ◦⊦ Better use of social bookmarking and ‘folksonomies’ for knowledge sharing and delivery;

◦⊦

◦⊦ ◦⊦

More sophisticated personal content management, including federated searching capability; Robust e-portfolio systems that support life-long learning; Greater emphasis on the use of mobile technologies as the means of interfacing with these environments.

CONCLUSION Professional learning, through its principles of collaboration (i.e. PLCs) and its links with and practical procedures and interactions (i.e. CoPs) has specific technological requirements that differ significantly from those of conventional academic learning and teaching environments (e.g. universities) for the following reasons: •

•

•

The discussion of distributed and professional learning suggests that the common interests of members of a profession and their practical interests constitute a sound basis for CoPs as learning communities, so existing CoPs (e.g. in healthcare, the law and education) can easily be developed into PLCs; The way that professions are organised make the idea of life-long learning and practice-based professional learning almost axiomatic, and most professions have training programmes, but these tend to be ‘traditional’ and do not take advantage of the latest developments in learning technology. The examination of the characteristics of professions shows that they are, by nature, democratic and self-regulating. These are also features of social software, which suggests that the application will match the technology well; It is suggested that the lifestyles and location of professionals make it almost compulsory for a PLC to employ and distrib-

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•

uted infrastructure to support the learning process. Conventional learning and teaching processes (i.e. based on the ‘classroom’) are unlikely to support the PLC adequately’. Distributed learning systems are mandatory in this environment, but often the in-house development of such systems would incur considerable cost. Social software applications appear to offer significant cost/performance advantage; The new developments in social software appear to offer the potential to offer improved levels of acceptance by users in support of PLCs, as their use in professional learning mirrors their use for social and leisure purposes.

These are compelling reasons for further investigating the adoption of learning applications based on ‘Web 2.0’ social software to implement practice-based professional learning in support of professional learning communities.

REFERENCES Anuradha, A. G. (1995). Collaborative learning enhances critical thinking. Journal of Technology Education, 7(1), 22–30. Atherton, J. S. (2005). Learning and teaching: Piaget’s developmental theory. Retrieved March 31, 2008, from http://www.learningandteaching. info/learning/piaget.htm Atwell, G. (2006). Personal learning environments [Presentation]. Retrieved April 2, 2008, from http://www.slideshare.net/GrahamAttwell/ personal-learning-environments-46423/ Brennan, J., & Little, B. (1996). A review of workbased learning in higher education. London: Quality Support Centre and OU Press.

Centre for Excellence in Professional Learning from the Workplace (CEPLW). University of Westminster. Retrieved April 2, 2008, from http:// www.wmin.ac.uk/page-13732 Dufour, R. (2004). What is a “professional learning community”? Educational Leadership, 61(8), 6–11. Eraut, M. (1994). Developing professional knowledge and competence. London: Falmer Press. Estes, C. (2004). Promoting student-centered learning in experiential education. Journal of Experiential Education, 27(2), 141–161. Etzioni, A. (1969). The semi-professions and their organization. New York: Free Press. Good, R. (2007). Social software and its possible future uses. Retrieved April 2, 2008, from http:// www.masternewmedia.org/online_ collaboration/ social-software/future-of-social-software-forlearning-and.-education-20070531.htm Grossman, P., Wineburg, S., & Woolworth, S. (2000). What makes a teacher community different from a gathering of teachers?Seattle, WA: Center for Teaching and Policy. Hasan, H., & Pfaff, C. C. (2006, September 2728). Emergent conversational technologies that are democratizing information systems in organizations: The case of the corporate Wiki. Paper presented at the Information Systems Foundations (ISF): Theory, Representation and Reality Conference, Australian National University, Canberra, Australia. Hasan, H., & Pfaff, C. C. (2006a). The Wiki: An environment to revolutionise employees’ interaction with corporate knowledge. ACM International Conference Proceedings, 206, 377-380. Hord, S. M. (1997). Professional learning communities: What are they and why are they important? Issues about. Change, 6(1).

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Iyoshi, T., Hannafin, M., & Wang, F. (2005). Cognitive tools and student-centered learning: Rethinking tools, functions and applications. Educational Media International, 42(4), 281–296. doi:10.1080/09523980500161346

Orr, J. (1990). Sharing knowledge celebrating identity: War stories and community: Memory in a service culture. In Middleton, D. S., & Edwards, D. (Eds.), Collective remembering: Memory in society. Beverley Hills, CA: Sage Publications.

Kegan, R. (1982). The evolving self. Boston: Harvard University Press.

Seely Brown, J., & Solomon Gray, E. (1995). The people are the company (pp. 78–82). Fast Company. [Premier issue]

Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. Cambridge: University of Cambridge Press. Leidner, D. E., & Jarvenpaa, S. L. (1995). The use of information technology to enhance management school education: A theoretical view. Management Information Systems Quarterly, 19(3), 265–291. doi:10.2307/249596 Morrissey, M. S. (2000). Comprehensive school improvement: Addressing the challenges. Issues about. Change, 9(1). O’Reilly, T. (2005). What is Web 2.0? O’Reilly network. Retrieved April 1, 2008, from http:// www.oreillynet.com/pub/a/oreilly /tim/ news/2005/09/30/what-is-web-20.html?page=1

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Success story: National Centre for Work Based Learning Partnerships, Middlesex University. Retrieved April 1, 2008, from http://www.adobe. com/uk/s howcase /pdfs/200709_middlesexuniversity.pdf Surrey Centre for Excellence in Professional Training and Education (SCEPTrE). Surrey University. Retrieved April 2, 2008, from http://www.surrey. ac.uk/ sceptre/index.htm TikiWiki CMS/Groupware [Computer software]. Retrieved April 2, 2008, from http://info.tikiwiki. org/tiki- index.php Toole, J., & Louis, K. S. (2002). The role of professional learning communities in international education. In K. Leithwood & P. Hallinger (Eds.) The second international handbook of educational leadership (pp. 245-279). Dordrecht: Kluwer. Retrieved April 2, 2008, from http://www.education. umn. edu/carei/ Papers/JULYFINAL.pdf

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Chapter 13

Knowledge Sharing in the Learning Process:

Experience with Problem-Based Learning Lorna Uden Staffordshire University, UK Alan Eardley Staffordshire University, UK

ABSTRACT Knowledge is the most important resource of an organisation. The exchange of knowledge and knowledge management enhance organisational learning that in turn leads to innovation. Central to knowledge management is the concept of knowledge sharing. The future of knowledge sharing is not technical, but social. Knowledge sharing is fundamental to learning among students. This paper begins with a brief review of knowledge sharing, followed by the importance of knowledge sharing for learning, especially in problem-based learning. The authors then describe how successful knowledge sharing can be achieved for students to share knowledge in problem-based learning. The paper concludes with implications for effective knowledge sharing for student learning.

INTRODUCTION Knowledge management systems have been used by companies to manage the vast array of hidden knowledge of employees. However, the management of knowledge is not trivial. Central to this is the issue of knowledge sharing. Knowledge sharing is defined as a set of behaviours involving exchange of knowledge or assistance to others. Any large and complex organisation such as a university owns a considerable amount of knowledge, such as DOI: 10.4018/978-1-60566-701-0.ch013

the methods of developing its courses and learning materials and ways of delivering better services to its learners and ‘customers’. Until the late 20th Century, knowledge would often be held locally within such an organisation (e.g. by individuals and within departments) and would be strictly segregated according to organisational level (e.g. ‘management’ and lecturers would have different knowledge assets). In this type of environment knowledge would often be ‘hoarded’ or closely guarded as a way of achieving or maintaining personal power or security (Sveiby, 1997). Today, by contrast, knowledge has come to be considered

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as an organisational asset (Carneiro, 2000) and it has long been assumed that every experience an individual has is potentially of use to many others in the same organisation, not necessarily only those in the same ‘job category’ (Basili & Rombach, 1991). Usually such organisations aim to exploit this knowledge by developing systems and processes to create new knowledge and to aid the effective sharing and utilisation of existing knowledge (Abell & Oxbrow, 2001), sometimes to great effect. ‘Identifying, managing, and transferring knowledge and best practices has worked for some companies, sometimes saving or earning them literally billions’ (O’Dell & Grayson, 1998). The potential value of this knowledge to the organisation, and the difficulty the organisation may have in acquiring and replicating the knowledge, makes it a strategic commodity in many sectors (e.g. Sharkie, 2003; Susarla et al., 2003). It is sometimes pointed out that it is not the simple act of possessing the knowledge that gives the organisation an advantage, but the way in which ‘added value’ can be given in terms of its future use by sharing (Teece, 1998). Although the knowledge is often acquired by (and often held by) individuals, the possession of knowledge by individuals cannot guarantee this ‘added value’. Individuals often may not share their knowledge, integrate the knowledge they hold with that held by others, or may move between firms taking the knowledge with them (Grant, 1996). There is also the problem of change in technology-driven organisations, as the ‘rate of change in technologies exceeds the time to develop subject matter experts, training courses, and human resource interventions’ (Marler, 1999). Clearly, there are serious implications in this for technological universities, and knowledge management (KM) emerged as a discipline to overcome the problems of acquiring and sharing knowledge and to gain maximum ‘added value’ from it. Knowledge sharing is not only vital for firms, but important for students in their learning too. For students to learn effectively, they must con-

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struct knowledge and collaborate with their peers. This is especially fundamental to problem-based learning (PBL). However, knowledge sharing is not easy for students. How do we promote knowledge sharing among students in PBL? This paper discusses a case study involving the experiences of one of the authors in her work in PBL to promote knowledge sharing among students. The paper briefly reviews knowledge and knowledge sharing. This is followed by a brief review of Problem-Based Learning and its benefits. The next section describes how students share knowledge in PBL. Factors affecting students knowledge sharing are then discussed. The paper concludes with suggestions for further research.

KNOWLEDGE AND KNOWLEDGE MANAGEMENT The basic problem with KM is the ambiguity and lack of definition of the concept of ‘knowledge’ itself. It is difficult to discuss the topic without arriving at a general agreement of the definition and characteristics of the subject. Clearly, a categorisation of KM, with suitable definitions, is necessary, and this has been attempted on a number of occasions. Knowledge is defined as a dynamic human process of justifying personal belief towards the truth (Nonaka & Takeuchi, 1995). It can also be defined as ‘know-why’, ‘know-how’ and ‘know-who’, or an intangible economic resource from which future resources will be derived (Rennie, 1999). Knowledge is built from data, which is first processed into information (i.e. relevant associations and patterns). Information becomes knowledge when it enters the system and when it is validated (collectively or individually) as a relevant and useful piece of knowledge to implement in the system (Carrillo et al., 2000). Besides the meaning of knowledge is the identification of the kind of knowledge that is to be managed. Polanyi (1967) originally identified

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categories of knowledge that he called ‘explicit and tacit’. A definition of explicit knowledge is relatively straightforward as this category consists of knowledge that is easily articulated and structured and such knowledge is therefore often recorded in written documents and computer records. Analysis of these documents and records will allow examples of this explicit knowledge to be captured and codified with relative ease, as this ‘organisational’ explicit knowledge is usually held within and disseminated by formal information systems (IS), which are designed to be used and managed in structured ways. Sharing tacit knowledge, however, is much more problematic as such knowledge is usually of a personal and unstructured nature. Tacit knowledge is therefore difficult to articulate and may be difficult to communicate (and therefore to share). Exploitation of this type of knowledge is sometimes held to be more important than explicit knowledge in ensuring the success of an organisation (Nonaka, 1998). Tacit knowledge is usually based on knowledge that is acquired personally, such as experience and expertise and acquired technical skills. It usually shows its value in response to unforeseen or informal events and in situations where rapid, flexible and unpredictable action has to be taken. Sharing is therefore often ‘ad hoc’. Other researchers identify a further category of knowledge termed implicit knowledge (Newman & Conrad, 2000), which is knowledge that can be made explicit (e.g. codified or formalised) from tacit knowledge sources, but which has not yet been treated in this way. Leonard and Sensiper (1998) identify the additional category of ‘implicit knowledge’, which is knowledge that is not yet available or shared to good effect. These categories are not necessarily mutually inclusive, as knowledge is obviously in the minds of individuals (May & Taylor, 2003), and is capable of being integrated into the organisation in a way that makes it more effective (Grant 1998) and recorded in a way that enables it to be interpreted more meaningfully (Kelleher & Levene, 2001).

KNOWLEDGE SHARING Knowledge sharing is generally acknowledged as the transferring of knowledge objects similar to information being transferred in the conduit model (Shannon & Weaver, 1949). The second perspective views knowledge that only resides in the minds of people and cannot be separated from its human actors and is only meaningful and actionable to those who are already knowledgeable (Hanson et al., 1998). Knowledge sharing is the exchange of information in order to yield knowledge. It is considered a private good, owned by the individual and its development and exchange occurs through one-to-one interaction (Wasko & Farad, 2000). Technology such as e-mail is often used to support this kind of knowledge sharing. A third perspective of knowledge sharing views knowledge as a public good that is socially generated, maintained and exchanged within emergent communities of practice (Lave & Wenger, 1991; Brown & Duguid, 1991). It suggests that knowledge supersedes any one individual and is highly context-dependent. This perspective suggests that knowledge can be managed as a public good. Knowledge sharing should consequently be considered as a social process through which individuals try to establish a shared understanding about reality, by using diverse combinations of signs and tools. Knowledge sharing in this perspective emphasizes that knowledge only has meaning within the context of interacting actors. The view of hard or explicit knowledge as being capable of being codified has led to attempts to extract and store knowledge from one group of ‘experts’ so that it can be used to increase the knowledge of others in a similar area of knowledge use. While the effectiveness of such systems appears to be mixed (Davenport and Prusak, 1998), such systems are well established and many tools and frameworks are available to support this type of KM. This has led to the importance of KM in supporting specialists who carry out similar practices within a shared ‘body of knowledge’

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or what is now called a community of practice (CoP). In knowledge management, one of the most challenging issues is to get people to share their knowledge. Skyrme (2002) lists the following barriers to knowledge sharing: • • • • • • •

Knowledge is power. Not invented here syndrome. Not realizing how useful particular knowledge is to others. Lack of trust. Poor means of knowledge capture. Inadequate technology. Internal competition and top-down decision-making.

Several psychological processes impact on knowledge sharing. Among these are trust, top management support, organizational culture and social networks. Trust is crucial for knowledge management. It can enable or inhibit knowledge sharing, depending on the level of face-to-face interaction (Davenport & Prusak, 2000). The greater the level of trust, the higher the level of knowledge sharing. There must be top management support for any project to be successful in business. Organizations with successful KM functions are those with senior executives carrying out the chief knowledge officer’s role (Gopal & Gagnon, 1995). Organisational culture can impact on the success of knowledge sharing. Employees who seek out and are not afraid to share their knowledge without fear of losing their jobs are crucial in fostering a positive knowledge-oriented culture (Davenport & Prusak, 2000). With increased globalisation in business, to overcome geographical barriers, it is important for organsiations to be connected by social networks to locate subject matter experts. Sharing of knowledge may occur at various levels in organizations. However, the sharing of tacit knowledge typically occurs not only in electronic documents, e-mails, etc., but also in social interaction (i.e. face to face interaction) where employees interact on a regular basis. 218

This is common within teams when members with various knowledge and skills work together trying to accomplish a common goal. Focusing on members in teams would enable us to assess individual factors such as motivation and trust and their effect on the level of knowledge sharing within the team. Teams also allow us to examine the social processes such as conflicts and decision making that may affect the team’s willingness and ability to share tacit knowledge.

How to Encourage Knowledge Sharing The above factors that are causing barriers to collaboration and knowledge sharing do not reflect the nature of knowledge and humans, including their motives and willingness to collaborate (Krackhardt & Hanson, 1993). Community of practice exists because people desire to share experience and understanding and to solve problems collectively. It would seem that people in the community of practice value communal activities in their own right and not exclusively as a means for achieving individual goals. Communities of practice (COPs) are self-organizing networks of people dedicated to sharing knowledge in an area of common interest or expertise (Brown & Duguid, 1991). According to Brown, Collins and Duguid, learning does not take place inside individual heads, but embedded in social practice (Brown et al., 1989). According to Brown and others (1989), knowing and doing are inseparable. Knowledge is context dependent and co-produced through activity. Because the creation and deployment of knowledge is inseparable from activity, contexts of activity create knowledge boundaries (Blackler, 1993). Community members create, hold and share knowledge collectively because of their shared practice (Hutchins, 1991). Practice is the coordinated activities of individuals and groups in doing their real work as it is informed by particular organizational or group context (Cook & Brown 1999, p. 386). This view of knowledge leads not only to the way in which

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work is accomplished, but also the way in which knowledge is created and used (Brown & Duguid, 2001). Lave and Wenger (1991) describe a community of practice as, ‘… a set of relations among persons, activity and world, over time and in relation with other tangential and overlapping CoPs and regard the use of knowledge to promote and enable learning in a CoP as ‘an intrinsic condition for the existence of knowledge’ (p98). They refer to the practice of acquiring knowledge by engaging in a CoP as Legitimate Peripheral Participation (LPP), which has three inter-linked and integrated aspects; legitimation, peripherality and participation. According to Lave and Wenger, LPP is not just about learning in practice but learning as an integral part of that practice. Legitimation is the aspect that is concerned with relationships of power and authority in the CoP and is not necessarily formal, depending on the social ‘rules’ that apply. Peripherality is not a physical concept (it does not imply distance from a centre or place in the ‘hierarchy’ of the CoP nor is it a simple measure of the amount of knowledge that has been acquired by an individual, but to the degree of participation or engagement that the individual has with the CoP. For Lave and Wenger (1991) participation provides the key to understanding CoPs, as a CoP does not imply the existence of a visible or identifiable social group, but implies an activity where participants have a common ontology, framework of understanding and desire to share knowledge to the benefit of the CoP as a whole. In this view the degree of an individual’s participation in the CoP is inseparable from the degree of involvement in its practice and therefore with the extent of the individual’s knowledge. The following definition of CoPs has been offered, ‘At the simplest level, they are a small group of people... They are peers in the execution of ‘real work’. What holds them together is a common sense of purpose and a real need to know what each other knows’ (Brown & Grey, 1995, p. 78). The application of this approach to problem-solving and learning in a CoP involving

photocopier technicians is studied by Orr (1990). In this case, the participation in the CoP as identified by Lave & Wenger (1991) is observed as the technicians gain experience by solving repair problems and legitimise their contribution to the CoP by the value of their ‘war stories’ in spreading knowledge that has helped the other technicians to solve problems. The theme of learning was therefore a major driver of the original concept of a CoP in its initial form, and it is this concept that is of interest in this paper, extending the idea to problem-based learning (PBL). Three things can be learned from the case reported by Orr (1990). First, there is the gathering of domain knowledge (e.g. how to solve a particular problem). Secondly, there is the development of the knowledge of practices specific to the CoP (for example knowledge of the features of an individual photocopier). Thirdly, there is the knowledge that the CoP holds about the potential value of its members in the problemsolving or learning experience (e.g. through their ‘war stories’). In the CoP, if a problem had to be solved, the members would gather the domain knowledge by interaction and working together to solve the problem – this is the basis of PBL, which is described below.

PROBLEM-BASED LEARNING (PBL) Problem-based learning (PBL), according to Barrows (1992) is, ‘... the learning which results from the process of working towards the understanding of, or resolution of, a problem.’ PBL is a way of constructing and teaching courses using problems as the stimulus and focus for student activities. The courses start with problems rather than the exposition of disciplinary knowledge. They move the learners towards the acquisition of knowledge and skills through a staged sequence of problems presented in context, together with associated learning materials and support from teachers. Barrows (1992) describes the main educational goals of PBL as:

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•

•

To develop learners’ thinking/reasoning skills (problem solving, meta-cognition, critical thinking); To help the learners become independent, self-directed learners (learning to learn, learning management).

The purpose of PBL, according to Barrows (1992) is to produce learners who will: •

•

•

• •

Engage in a challenge (problem, complex task, and situation) with initiative and enthusiasm; Reason effectively, accurately, and creatively from an integrated, flexible, usable knowledge base; Monitor and assess their own adequacy to achieve a desirable outcome given a challenge; Address their own perceived inadequacies in knowledge and skills effectively and efficiently; Collaborate effectively as a member of a team working to achieve a common goal.

PBL is a challenging and motivating way to learn because students take ownership of their problem and work in real-world situations. They perceive learning as important and relevant to their own lives. PBL is centred on the learning that emanates from a real problem. In PBL, students spend time in learning – by identifying what they need to know, by finding out, by talking to each other and by applying their new knowledge. The primary aim is learning itself not the completion of the project – the project is the means to the end. Note that this is different from standard project work in that the ways in which the students are encouraged to tackle the problem are designed to encourage learning in a structured manner (albeit with substantial learner control). It is also different from apprenticeship and ‘learning on the job’ where the focus is on completing the work

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and learning as a by product. In short, the key ingredients of PBL are: • • • •

the problem as the focus of learning; learning as the purpose of the problem; the problem as the integrator of concepts and skills; commitment to self-learning.

In addition, PBL is typically used in team and small group situations as it encourages the development of reflective abilities, which can be achieved individually, but is often easier in a group situation.

THE PBL TUTORIAL PROCESS There are many strategies for implementing PBL. The particular PBL model adopted was that of Barrows (1992). There are four phases to the PBL process, as shown in Figure 1. The tutor presents the problem ‘cold’ to the students who do not know what the problem will be until it is presented. The students discuss the problem, generate hypotheses based on experience they have, identify relevant facts in the case, and learning issues. The learning issues are topics of any sort deemed of potential relevance to the problem and which the group members’ feel they do not understand as well as they should. To help students to structure their thought processes, a four-column chart shown in figure 2 is used (adapted from Duffy (1994). A session is not completed until each student has an opportunity to verbally reflect on their beliefs about the problem, and assume responsibility for particular learning issues that were identified.

Starting a New Problem When the students have gone as far as they can with the problem, they determine what resources they will use (faculty experts, library, Internet, etc.) to gain the knowledge and skills needed. They also

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Figure 1. The four phases of PBL

assign learning issues to different members of the group to work on. A time limit would also be set for the completion of the task. After the session, the students all engage in self-directed learning where they work independently of the tutor, consult resources and work collaboratively. The student group, depending on the extent and depth of issues they have elected to pursue negotiates the length of this phase. After self-directed learning, the students meet again. They apply the newly gained knowledge back to the problem, critique their prior thinking and knowledge, and refine their understanding of the problem and its management. They then synthesize what they have learned, relate it to prior problems and anticipate how it might help with future problems. They also evaluate resources what is most useful and what is not so useful. This cycle may repeat itself if new learning issues arise. The students also assess themselves individually

in the following areas: problem solving skills, knowledge acquisition, self-directed learning and support of the group. The last phase is the presentation of the solution by groups to peers and the tutor. While self-directed learning is an important element, PBL is not an independent study curriculum. Each student works as a member of the tutorial group, and the group works together in resolving the problem. As a result, teamwork is an essential ingredient in PBL. During the tutorial process, the tutor guides the students in reasoning their way through the problem. Significant findings are recorded by the group, along with their hypotheses and learning issues, knowledge needed to better understand and further pursue the problem. Using tutorial skills, the tutor facilitates student access to their own prior knowledge as well as the identification of the limitations of their knowledge. The tutor also guides students to articulate their knowledge

Figure 2. PBL tutorial chart (After Duffy 1994)

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of the relevant disciplines as they relate to the problem at hand. As students progress through the curriculum, they learn to reason through the problem effectively and efficiently. The need for information required in understanding the problem generates learning issues for further study. For the learning issues identified, an action plan, consisting of a list of activities that students need to do to achieve the learning issues, is worked out. The action plan lists the types of resources, which are needed to solve the problem. Resources may be books, journals, the Internet, etc. If the Internet is involved, students must work out exactly what they want to look for, based on the learning issues identified. Each student is assigned tasks based on the learning issues to be carried out. For essential learning issues, all students would have to do these tasks. Once the tasks have been allocated, students go their separate ways to conduct their research. When they have finished their investigation they return to the next tutorial with the rest of the team and the tutor for another tutorial session. During this session, students are expected to present what they have found out and share with the rest of the team and the tutor. During this session, students are challenged as to what they have done, why they did the things the way they had, etc. Students must articulate their findings and critique them along with those of their peers. The tutor never volunteers any information except to help bring out students’ ‘metacognitive’ skills by asking questions. Students are not expected merely to present their findings, they must really understand and articulate their thoughts. Before the session ends, students have to reflect on their work and share with the team members and the tutor their own reflection of the investigation conducted. During the course of the session, further problems or issues will be identified. These will then be taken up for further research by students. The process continues until the problem is solved.

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BENEFITS OF PBL Although PBL originated from the teaching of university students, it is increasingly being used in high schools, middle schools and elementary schools in many different countries. More and more tutors are taking on this method as a way of improving students’ learning. It works well with educationally disadvantaged and minority students who traditionally have not done well in conventional educational settings. PBL provides an equal and exciting opportunity for learning to all students. The PBL method is seen by many teachers as the answer to many of the problems of teaching in schools. It enables teachers to add many things to their traditional teaching, including problem-solving activities, critical thinking exercises, collaborative learning and independent study, and to put these into context and give them meaning. It is generally accepted by researchers that PBL offers many benefits to learning. Among these are: •

•

• •

•

• •

The PBL learning environment is more stimulating and human (Albanese & Mitchell, 1993); Learning and teaching is more enjoyable for students and teachers in PBL (Albanese & Mitchell, 1993; Vernon & Blake, 1993); PBL promotes interaction between students and faculty (Finucane et al., 1998); Self-directed learning skills are enhanced and retained in PBL learning (Barrows & Tamblyn, 1980; Dolmans & Schmidt, 1996; Blumberg & Michael, 1992); PBL fosters self-directed learning skills (Barrows & Tamblyn, 1980; Norman & Schmidt, 1992; Blumberg & Michael, 1992; Dolmans & Schmidt, 1994); PBL promotes interaction between different disciplines (Finucane et al., 1998); PBL promotes collaboration between students (Banta et al., 2000);

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• •

•

PBL enables reflection-in-action (Schön, 1983); PBL enables students to spend more time on self-directed learning activities using more information resources (Vernon & Blake, 1993); PBL enables staff to have more contact with students (Albanese & Mitchell, 1993).

There are also many studies conducted by researchers showing that PBL students performed better than traditional class students.

Knowledge Sharing in PBL In PBL the learners discuss the problem, generate hypotheses based on experience they have, identify relevant facts in the case, and learning issues. The learning issues are topics of any sort deemed to be of potential relevance to the problem and which the group members feel they do not understand as well as they should. For effective learning, learners must work in groups and share knowledge. One of the authors has experience of implementing PBL since 1996 (Uden, 2004; Uden, 2005; Uden & Dix, 2004). Central to the idea of PBL tutorial sessions is that learners should work in groups to gain insight of the problem, to identify learning issues and to determine solutions. Group working and knowledge sharing are crucial to the success of the problem. In PBL, to solve problems, collaboration by the learners in a group requires knowledge sharing. Knowledge sharing is where one disseminates one’s acquired knowledge with the other learners in the team. People only share their knowledge if they think that the knowledge would be useful and important to others. Each of the learners participating in the PBL groups came from a different background, had different interests, and possessed different knowledge. Although they were different, all felt they had participated in a CoP - an activity system in which they shared understanding concerning what they were doing and what this meant in their lives

and for their groups (Lave & Wenger, 1991, pp. 97-98) - so it makes sense to use CoPs to study the relationship between trust and knowledge sharing. The PBL group acts as a community of practice comprised of individuals who are related to each other by virtue of their common engagement with an activity – the solving of the problem. To each individual member of the group, the sharing of knowledge and the trust that sharing both requires and creates were important parts of their community life. One of the authors has conducted PBL for over 10 years in teaching of various subjects. Based on the experience gained, she has identified several factors that are important for knowledge sharing in learning. Among these are solidarity, sociability and trust. Solidarity is the measure of the members of the community or organisation to pursue shared objectives, regardless of personal ties (Goffee & Jones, 1996). A joint sense of purpose or objective is vital. Even if members did not know each other, a sense of solidarity of objective brings them together to act as one. Learners must take ownership of their own learning by recognising that they share the same objective, that is, to solve the problem. This gives them a sense of response to competitive encroaches and a low tolerance of poor performance. It is important that students take responsibility for their own learning and that each of them shares the same joint objective of getting the problem solved. This ownership gives the student a feeling of belonging to the same community. This helps to promote knowledge sharing for the benefit of the shared goal of doing well in the work. Sociability within the tutorial groups also appears to be an important success factor. Sociability is the amount of ‘friendliness’ among members of the group (Goffee & Jones, 1996). Social supports must be presented to create a dynamic climate for positive growth in groups. It is important to show a caring attitude towards each individual in the group. This can be achieved by taking a personal interest in monitoring the progress of each

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member of the group a well as taking interest in activities outside of the tutorial sessions. This also helps members to develop trust. This social aspect had great impact in knowledge sharing because members began to view each other as friends and were happy to share ideas and knowledge as well as sustain a high level of unarticulated reciprocity. Each member of the group was motivated and committed to work for the benefit of the group. Trust is the most important precondition for knowledge exchange (Rolland and Chauval, 2000, p. 239). It is a prerequisite for tacit knowledge sharing. (Ford, 2002). Although the transfer of knowledge is a voluntary behaviour, we often need to share values and/or establish a common shared objective with someone before we are willing to transfer any knowledge. Conversely, trust is also voluntary; especially to trust initially is a voluntary act of faith. Another factor that links trust and knowledge is that of equality in meeting expectations (Ashleigh et al., 2003). If inequality or imbalance is perceived in a relationship, this can lead to mistrust (Sitkin and Roth, 1993). Identification of perception of fairness between two parties is a prerequisite for transferring knowledge (Nieoff and Moorman, 1993). Trust facilitates cooperative behaviour (Shneiderman, 2000) as individuals do not share knowledge where there is no trust. People are afraid to share because they do not trust others (Standing & Benson, 2000). Trust can be an enabler or disabler of KM, depending on how individuals interact. Fukuyama (1995) defines trust as the expectations that arise within a community of regular, honest and cooperative behaviour, based on commonly shared norms on the part of the members of the community. Trust is central to defining the informational needs of an individual in meeting expectations within and between relationships (Luhmann, 1979). Wicks and others (1999) argue that there is an inverse relationship between trust and the need for knowledge, and that trust is an alternative to knowledge. It is perceived by many researchers that trust and knowledge exchange

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are positively related (Standing & Benson, 2000; Edwards & Kidd, 2003). It is our belief that successful transfer of knowledge depends on the direct participation in sharing of both the giver and the recipient of knowledge. Although there are different definitions given to trust, it has several common characteristics such as risk, willingness to place oneself at risk with the assumption and expectation that no harm will come to oneself and one’s expectations or beliefs (Rosseau et al., 1998). A common definition given to trust is that of Mayer and others (1995, p. 712). According to these authors, trust is the willingness of a party to be vulnerable to the action of another party based on the expectation that the other will perform a particular action important to the trustor, irrespective of the ability to monitor or control that other party. Trust is also discussed on the basis of how it is derived (Shapiro et al., 1992; Williamson, 1993). The level of trust between individuals, organizations and within society as a whole influences the nature of trust in online sharing, both in terms of the contributions made by individuals and the productivity of knowledge sharing. People do not share knowledge when there is no trust. They are afraid to share because they do not trust others (Standing and Benson, 2000, p. 343). Trust can be an enabler or disabler of knowledge management, depending on how individuals interact. It facilitates knowledge exchange, leading to more extensively shared knowledge, which in turn facilitates the development of trust. It is also multifaceted, with rational, affective, instrumental and moral components. Since trust is so important a factor to knowledge management, it is important to understand how we can help to promote trust in knowledge sharing or transfer. Trust is a complex construct with different bases, multiple levels and determinants (Rosseau et al., 1998). From our experiences working with learners in PBL, we have identified several kinds of trust that are relevant for knowledge sharing among learners. Firstly, ‘competence-based trust’ is essential for the group

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to work effectively. It is trust that describes a relationship in which an individual believes that another person is knowledgeable about a given subject area. Besides competence-based trust, there is ‘benevolence-based trust’. This is trust in which an individual will not intentionally harm another when given the opportunity to do so. There is also ‘capacity trust’, which is related to the degree to which it is believed that an individual is capable of using the knowledge correctly. The group may possess the required expertise in a certain area, but it may believe that it has not the capacity to use the expertise effectively because of another’s inability to communicate this fact. ‘Trust in integrity’ is important because it relates to the degree to which it is believed that an individual or community behaves decently and honestly. This may be related to ‘trust in value’. Trust in value is also important in our findings. It is related to the degree to which it is believed that an individual or community possesses one’s own values. Perceiving that another party does not share one’s own values leads to a distrust of that group (Sitkin & Roth, 1993), which can undermine the PBL activity and interfere with the effectiveness of the CoP. The research indicates that there is a direct relationship between the functioning of CoPs and the effectiveness of PBL in support of KM in a variety of organisations.

CONCLUSION Knowledge sharing is necessary for promoting team skills among students. Knowledge, particularly tacit knowledge, can only be transferred through social activities (Nonaka & Takeuchi, 1995). Trust can be developed across remote project teams by the creation of a social context through initially swapping information among team members (Jarvenpaa & Leidner, 1998). Knowledge is not a physical commodity, but is an ongoing social accomplishment, constituted and

reconstituted in everyday practice (Orlikowski, 2002, p. 252). Similarly, trust is also a dynamic process. As knowledge increases through the practice of sharing and giving, so trust is nurtured (Styhre, 2002, p. 230). Knowledge grows rather than diminishes with use and trust also appears to grow with use. There is an obvious relationship we found among our learners between trust and knowledge sharing. It is important to have initial trust when students first come together. It is our belief that scaffolding should be promoted to support effective trust building. Knowledge sharing is a social process involving teams of people working together. The trust that individuals develop within their teams helps them to predict others’ behaviour. The initial ‘getting to know you’ session encompasses sharing much cultural information. During this session, individuals exchange information about shared values, assumptions, opinions and beliefs. There is also a sharing of personal information such as hobbies, interests, family life, work life, personal expectations of teams, equipment, resources, etc. We believe that the most pertinent determinants for trust are open communication, inclusion in decision making, sharing of critical information, as well as feelings and perceptions. These determinants of trust would increase the likelihood of trust building among students. Institutional trust also plays a critical role in promoting knowledge sharing. Institutional law or university procedures can be used to develop institutional-based trust. This includes the use of continual assessment and penalties for not contributing.

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Chapter 14

Culturally-Bound Innovation in Romanian Teaching and Research Hospitals Mihaela Cornelia Dan Academy of Economic Studies, Romania Simona Vasilache Academy of Economic Studies, Romania Alina Mihaela Dima Academy of Economic Studies, Romania

ABSTRACT This chapter discusses innovation in the Romanian healthcare sector, from the point of view of organizational learning, which is influenced by the components of organizational culture. Starting from the premise that hospital organizational culture differs from other types of organizations, we investigated the perceptions of a mixed sample of doctors and nurses from an internal medicine clinic of a large teaching and research hospital. The Dimensions of the Learning Organization Questionnaire and items selected from a questionnaire developed by the authors were used in order to study how the two groups perceived organizational culture and, subsequently, innovation, as both a component and a result of it. The results of the study show differences in perception between physicians and nurses, consistent with the ones presented in literature, and account for which facets of hospital organizational culture affect learning easiness versus which factors are negatively correlated with it.

HOSPITAL ORGANIZATIONAL CULTURE In Anatomy of a Hospital, Ashley (1987) makes an intriguing figurative diagnosis of the hospital organizational life. So it is this hospital culture, DOI: 10.4018/978-1-60566-701-0.ch014

supported by many figures, but still intriguing in its essence, and posing threats to whoever would attempt to approach it in an orderly managerial manner. To speak about innovation in a system which is financially burdened, on the one hand, and socially compelled to excellence, on the other, is not a comfortable position. Still, healthcare innovation (Djellal & Gallouj, 2005), under the pres-

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Culturally-Bound Innovation in Romanian Teaching and Research Hospitals

sure of changing lifestyles (Kivisaari, Saranummi & Väyrynen, 2004), increasing healthcare costs, and diversification of customers demands (Boland, 1996), is an issue to be considered. The relationship between innovation and adoption was first examined in 1982 in a study by Tornatzky and Klein. The two researchers selected 75 articles on innovation characteristics. Several variables, as prediction, adoption and implementation, design, measurement, number of characteristics, number of innovations, locus of innovation, empirical findings and statistical tests were monitored in these articles, in the form of a meta-analysis. The correlation between the presence of a certain characteristic of innovation and its adoption and implementation was computed, and the results show that it is important for an innovation to be compatible in order to be adopted, to be bring a relative advantage, to be communicable, observable, to reach social approval, the first two being the most relevant, while complexity of an innovation hinders its adoption. Taking just the first characteristic positively related to adoption, compatibility, which means, according to Rogers and Shoemaker (1971, p. 15), ‘the degree to which an innovation is perceived as being consistent with the existing values, past experiences, and needs of the receivers’, the connection between innovation and organizational culture is neatly revealed. McLean (2005) provides a comprehensive review on this relationship, quoting, for instance, Amabile et al. (1996 P. 1155): ‘The social environment can influence both the level and frequency of creative behavior’. Adoption is both technologically bound, and culturally bound. The interaction between the two is examined by Frambach, Herk and Agarwal (2003) in a research on the telecom industry. In a classical article from 1981, Kimberly and Evanisco assess the influence of a triad of factors, individual, organizational and contextual, on innovation adoption in hospitals. Culture appears as a factor in all these three perspectives, as individuals, organizations and contexts are not culturally free.

Sarros et al. (2008) use the Organizational Culture Profile developed by O’Reilly et al. (1991) and revised by Sarros et al. (2005), comprising seven factors, supportiveness, innovation, competitiveness, performance orientation, stability, emphasis on reward, social responsibility, in order to assess the readiness for change in organizations. Again, the relationship between organizational culture and innovation is ascertained. A study by Sta. Maria (2003) brings into discussion also organizational learning, relying on Watkins’ and Marsick’s (1993, 1996) dimensions of the learning organization. Analyzing the diffusion of innovation in healthcare organizations, Greenhalgh et al. (2005) take into consideration the inner context of innovation, the environment which filters the innovation, from the moment of its emergence to the moment of its adoption. Particularizing previous studies (Damanpour, 1991, 1992, 1996) to the healthcare system, they distilled several factors, as size, structural complexity, leadership and decision-making, climate and receptive context, supporting knowledge manipulation, which are relevant to innovation creation and adoption. Two ideas are to be discussed, from their conclusion. First, they advocate that, since there are individuals with a certain combination of personal characteristics (higher education, higher social status), who are predisposed to more easily adopting innovation, organizations which are rich in this type of individuals will, in their turn, be more innovative. Following the thesis according to which the agglomeration of intelligent individuals does not make organizational intelligence, but rather, if we admit the Braess paradox, it leads to ‘collective stupidity’. This evidence makes the thesis regarding the linear summation of innovation in organizations debatable. Second, they present large size and complexity as being favorable to innovation, because of the possibility to create specialized niches which are well protected in a large and efficient organization, and can concentrate on specific problem-solving. This may be so, for the moment of innovation emergence,

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but creates bottlenecks especially insofar as diffusion is concerned. There should be operated a distinction between innovation as a component of organizational culture, and innovation as a result of organizational culture. We suggest using the term of pro-forma innovation for the component of organizational culture which is inactivated, in its normal state, and which, at a particular moment, is transformed into innovation as such, the result of an innovationoriented organizational culture. Culture, as Schein defined it, is the outcome of a learning process, in which the organization becomes aware of how to solve the problems related to its survival. The next step to be taken is the culture of performance, where external adaptation and internal integration should serve the purpose of obtaining a competitive advantage. The particularities of the hospital organizational culture have to be analyzed, in connection with the willingness of the hospitals to promote and adopt innovation. According to Tidd, Bessant, and Pavitt (2001), organizational culture plays a significant role in promoting or inhibiting innovation. We take here the approach of Klingle et al. (1995), who developed a scale of the hospital culture. Building on the idea that the approach to medical services providing has to be a collaborative one, involving team work from the part of physicians and nurses, on the one hand, and medical staff and patients, on the other hand, Klingle et al. discuss the challenges this collaboration poses to the organizational culture of the health providing institutions. They review the theses of Hodes and Van Crombrugghe (1990), Campell-Heider and Pollock (1987), stating that physicians and nurses are not guided by the same set of norms and behaviors, and this happens, usually, because physicians tend to regard nurses as their subordinates, while nurses tend to view themselves as partners, whose role in the care process is essential and expanding. The demand that Ashley (1987) was formulating, ‘take me to your manager’, seems to steer, in

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hospitals, an insolvable dilemma. And the idea of taming hierarchies for the sake of collaboration is, even at a twenty years distance, easier said than done. The loose power relationships (Patterson, 2001), which identify managers with enables, rather than with leaders, affect the way hierarchy is understood. What a faculty dean said, that managing academics is like ‘trying to herd cats’, because their critical spirit will hardly allow that, is even more adequate for describing the work relationships in hospitals. Still, these people with various educational and professional backgrounds and with diverging representations of their roles in the organization have to work together. As conflicts affect the quality of care, and undermine the mission of the clinic, the diversity of the studies on hospital culture is not at all surprising. The old culture, of ‘everything to everybody’, a collegial community based on disinterested care, which performed a sine qua non social role, is replaced by a culture of performance, in which hospitals should professionalize, and admit the limits of care. In a recent article on e-health, Callen et al. (2008) reassess the idea of the differences in perception of doctors and nurses, in the specific matter of IT management. The doctors and, respectively, the nurses groups, are discussed as subcultures, following Scott et al. (2003), Braithwaite (2006), and the problem of external contributions to culture (for instance, doctors being more likely to pay attention to the values and beliefs of their professional organizations, national or international, than to the norms of the hospital) is put forward. The Organizational Culture Inventory (Cooke and Lafferty, 1989) was used, in order to assess the way different subgroups perceive the culture of their hospital, as being either constructive (further split in humanistic, affiliative, achievement and self-actualizing components), passive/ defensive (with approval, conventional, dependent and avoidance components) and aggressive/ defensive (oppositional, power, competitive, perfectionist). The model employs also two axes, task vs. people orientation, and satisfaction needs vs.

Culturally-Bound Innovation in Romanian Teaching and Research Hospitals

safety needs. Their results have shown that doctors perceive the hospital culture as being rather aggressive-defensive, while nurses perceive it as being constructive. This may stem from the difference between the more affiliative style of nurses, and the more perfectionist style of doctors. Making them work together becomes an issue of cross-cultural management, between sub-cultures which are equally stable and powerful. Considering the model advanced by Quinn and Rohrbaugh (1983), based on two axes, flexibility vs. control and internal vs. external, and delimiting four quadrants, human relations model (flexible and internal), open systems model (flexible and external), internal process model (controlled and internal), and rational goal model (controlled and external), hospitals should be, ideally, placed in the first and second category, having some characteristics of the human relations, and some characteristics of the open systems. Still, the general perception on hospitals, from the point of view of the staff and, at the same time, from the point of view of the patients, is that of highly controlled institutions. Here lies the cultural schism, between the way culture is projected, and its desirable characteristics. These characteristics influence the capacity and the willingness to innovate. Martinssons and Chong (1999) have proven that even the most efficient technology can be sabotaged, if people in the organization perceive it as threatening their relational nexus. Cooper (1994) has also referred to a pressure on innovation, from the culture’s side. If the innovation is not compatible, it will be either forced to adapt, or eliminated. We will further examine the way hospital culture is perceived in a clinic of internal medicine, in a Romanian teaching and research hospital.

METHODOLOGY We identify innovation here with the easiness to learn, which is the long-term counterpart of

curiosity or of an inquisitive mind. We select, as a working definition, the definition proposed by Levitt and March, ‘the process of encoding interfaces from history into routines that guide behavior’, which relates organizational learning to organizational culture and to the dynamics of disruption and routine which is characteristic to innovation processes. In Table 1 we list a number of the available definitions of organizational learning. We used, in our approach, two different instruments: the questionnaire developed by Watkins and Marsick (1993) on the dimensions of the learning organization, as a benchmark of our research, and a questionnaire on organizational culture and innovation developed by ourselves. Our research purpose is to show the correlations between organizational culture characteristics and organizational learning, and the effects of these correlations on the level of innovation. The sample included 30 respondents, 20 physicians and 10 nurses, working in the same clinic, which was selected as a representative single holistic case study for innovation in hospitals (according to Yin, Bateman and Moore, 1983). The sample is a total one, including all the personnel working in the considered healthcare unit. Our questionnaire included 18 variables, consistent with the variables used by Watkins and Marsick, customized for the healthcare case study, as follows: res (similar personal results in any other clinic), dis (periodic discussions of interesting cases), yng (students and residents actively involved in learning), mem (archiving case solutions for further use), prs (each treatment is part of a process), inf (patients are informed on choices), out (physicians/ nurses not observing the values of the clinic are out), imp (the performance of the clinic is systematically improved), new (new therapies are experimented), ext (the clinic integrates the experience of physicians/ nurses having worked in other clinics), per (the clinic studies the performance of similar clinics), for (the clinic integrates foreign experts in teams), fbk (the clinic integrates patients’ feedback), inv (the

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Culturally-Bound Innovation in Romanian Teaching and Research Hospitals

Table 1. Definitions of organizational learning Argyris and Schön (1978) Morgan and Ramirez (1983) Fiol and Lyles (1985) Levitt and March (1988) Senge (1990) March (1991); Simon (1991) Huber (1991) Dodgson (1993) Kim (1993); Morgan (1997)

Single-loop learning: change of behaviours and actions as an effect of the gap between intentions and facts; Double-loop learning: change of performance improvement measures after reviewing the assumptions leading to wrong decisions; Deutero learning: ability of learning to learn. The process through which the members of an organization use learning to solve a common problem they are facing The process of improvement of actions through better knowledge and understanding The process of encoding interfaces from history into routines that guide behaviour Continuous testing of experience and its transformation into knowledge available to the whole organization and relevant to its mission Organizations learn through individuals who learn OL implies acquisition, dissemination, interpretation and storage of new knowledge Structured and systematic method applied by an organization to motivate its employees to learn Acquisition, dissemination, interpretation and storage of new knowledge

Argote (1999)

Acquisition, sharing and storage of new knowledge

Jones (2000)

The process through which managers try to increase the organization’s members’ capabilities, in order to better understand and manage the organization and its environment

Marchand et al. (2000) Murray and Donegan (2003)

Gradual process by which staff learn through experience and co-operate with each other Lengthy process leading to changes in individual and organizational behaviour

clinic monitors training and technology investments), prf (it is known why the patients prefer this clinic), rep (good results can be repeated in further cases), tmw (it is known what makes the teams work), nrd (the clinic’s performances are not random). The degree of agreement considering the presence of these factors in the organization was recorded on a 1 to 7 Likert scale. The Cronbach’s Alpha factor for the considered variables is.753, which accounts for the reliability of the analysis. The results obtained, by means of general linear modeling and factorial analysis, are presented below in Tables 2, Table 3, Table 4, Table 5 and Table 6. We have also assessed the organizational learning processes in the considered clinic, by means of the Watkins and Marsick questionnaire, with its eight components: creating a supporting culture, gathering internal experience, accessing external learning, communication systems, mechanisms for drawing conclusions, developing

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an organizational memory, integrating learning into strategy and policy, applying the learning. These were assessed on a 0 to 4 Likert scale.

RESULTS The results1 for the Dimensions of the Learning Organization Questionnaire, in the case of physicians, are presented in Table 2. As it can be seen, the values for all the eight dimensions are well below the maximum value of four, indicating that doctors seem to perceive their organizational culture as being only somewhat supportive and prone to developing a memory of the organization, while both internal experience and external knowledge are highly disregarded. The radar diagram corresponding to these answers for physicians is presented in Figure 1. In the case of the nurses, the results of the survey are presented in Table 4:

Culturally-Bound Innovation in Romanian Teaching and Research Hospitals

Table 2. Dimensions of the Learning Organization, as perceived by physicians in the sample Creating a Supportive Culture

Gathering Internal Experience

Question

Question

Accessing External Learning

Communication Systems

Mechanisms for drawing conclusions

Developing an organizational memory

Question

Question

Question

Question

Integrating Learning into strategy and policy

Applying the Learning

Question

Question

1

2

2

1

3

3

4

1

5

1

6

3

7

2

8

2

16

2

15

1

14

1

13

2

12

2

11

1

10

3

9

1

17

2

18

2

19

2

20

3

21

3

22

3

23

2

24

2

32

2

31

2

30

2

29

2

28

3

27

3

26

2

25

3

33

2

34

2

35

3

36

3

37

2

38

2

39

2

40

2

Total

Total

12

Total

Av

10 2

Total Av

1.6

8

Total Av

2.2

11

Total Av

2.2

11

Total Av

2.2

11

Av

2.4

Av

2.2

11

Av

Total

10 2

SD

0

SD

0.548

SD

0.837

SD

0.837

SD

0.837

SD

0.894

SD

0.447

SD

0.707

Table 5. Factors of organizational learning in the considered clinic Item

Table 3.

Loadings

Factor 1: Knowledge sharing (α = 0.813)

Scores Not true

0

Rarely true

1

Sometimes true

2

Often true

3

Very true

4

Students and residents are actively involved in learning

.833

Patients are informed on choices

.822

Physicians/ nurses not observing the values of the clinic are out

.863

Factor 2: Process orientation (α = 0.796) Each treatment is part of a process

.722

Solutions are archived for further use

.759

Table 4. Dimensions of the learning organization, as perceived by the nurses in the sample Creating a Supportive Culture

Gathering Internal Experience

Question

Total

Question

Accessing External Learning Question

Communication Systems

Mechanisms for drawing conclusions

Question

Developing an organizational memory

Question

Integrating Learning into strategy and policy

Question

Question

Applying the Learning Question

1

2

2

1

3

3

4

1

5

2

6

3

7

2

8

2

16

2

15

1

14

1

13

2

12

2

11

1

10

3

9

3

17

3

18

3

19

2

20

3

21

3

22

3

23

2

24

3

32

3

31

2

30

2

29

2

28

1

27

3

26

1

25

3

33

3

34

2

35

2

36

1

37

2

38

2

39

3

40

2

13

Total

9

Total

10

Total

9

Total

10

Total

12

Total

11

Total

13

Av

2.6

Av

1.8

Av

2

Av

1.8

Av

2

Av

2.4

Av

2.2

Av

2.6

SD

0.548

SD

0.837

SD

0.707

SD

0.837

SD

0.707

SD

0.894

SD

0.837

SD

0.548

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Culturally-Bound Innovation in Romanian Teaching and Research Hospitals

Table 6. GLM for learning ease Constant

Model 1

Model 2

Model 3

Model 4

6.219

5.172

4.125

3.601

YNG

0.221

0.012

0.102

0.021

PRS

-0.032

- 0.148

- 0.070

- 0.042

INF

0.109

- 0.036

- 0.050

0.037

OUT

0.213

NEW

0.093

0.061

0.025

-0.257

- 0.230

- 0.091

MEM

0.124

TMW

0.223

R2 adjusted Change in R

1.3% 2

2.4%

6.1%

9%

1.1%

3.7%

2.9%

Figure 1. Organizational learning profile (Physicians)

It can be noticed that the overall scores are comparable, but they are differently allocated. Consistent with previous research, as mentioned in our review of literature, nurses tend to perceive a more supportive culture than doctors, while they signal poorer communication and less internal and external learning acquisition. The figural representation of these scores is shown in Figure

236

2. The second questionnaire was administered to the entire sample, doctors and nurses together. The results of the factorial analysis, taking into account the 18 variables included in the analysis, are presented in Table 4: It can be seen that seven variables of the 18 initially considered account for the three factors of organizational learning in the clinic. The dimen-

Culturally-Bound Innovation in Romanian Teaching and Research Hospitals

Figure 2. Organizational learning profile (Nurses)

sions of organizational learning are: knowledge sharing, process orientation and incremental innovation, obtained by means of the two above. In other words, although the clinic continuously puts in practice new paths of action, the knowledge of what had worked previously is carefully archived, in an old-new continuum. It is the specific of the organizational culture, and of the knowledge sharing patterns in this particular organization. The focus, as resulting from the analysis, is more on organizational and contextual variables, than on the individual ones, as individuals not aligning to the principles of the clinic are eliminated. We applied general linear models to the concept of learning easiness, which is essential to innovation, in the hospital culture, in order to trace its dependency on each of the factors. The results of modeling, in various degrees of complexity, are presented in Table 4: The change in R2 indicates that the last model, including all the considered variables in the three factors, is the most representative for learning easiness. The process approach to tasks, and the knowledge absorption from exterior sources, like

studying the performance of similar clinics, and integrating foreign experts in teams are seen as negatively correlated with learning easiness. The adherence to process is known to ‘tame’ innovation (Dougherty and Heller, 1994). This indicates that innovation in hospitals does not suit the model of the open system, but rather that of the functional niches, where every sub-culture concentrates on a particular nucleus of innovation. The dissemination, prior to adoption (i.e. as a proposal to be discussed, and post-adoption, as a new principle to be put in practice) is thus hindered. Innovation is an internal process, with little or no absorption of external knowledge, based on competition, rather than on cooperation, between various sub-cultures.

CONCLUSION The study, at the level of an internal medicine clinic of a large teaching and research hospital, underlined the particularities of the organizational culture in this type of organizations, and the assumptions of the two main categories of personnel regarding these characteristics. The two sub-

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Culturally-Bound Innovation in Romanian Teaching and Research Hospitals

cultures, which are expected to cooperate closely, hold different points of view in terms of axes of culture, the physicians being more ‘perfectionist’ or oriented towards personal achievement, while the nurses are more prone to affiliation. From here stems the perception that they contribute more to the way culture is edified, which entitles them to a more central position than physicians believe they deserve. It is debatable which is, in fact, the category, or the sub-culture, which gives to the organizational culture its characteristics. Nevertheless, differences between subcultures, especially when they are related to blocking innovation, because of conflicting attitudes towards it, have to be recognized and somehow quantified. It was our purpose here, to relate innovation adoption to learning easiness, and to decompose learning easiness into simpler factors, which can be compared with Watkins’s and Marsick’s scale. Some other items, present in literature, as freedom, risk-taking, confidence in the internal and in the external environment have to be taken into account in an expanded version of the questionnaire, to be used for further research.

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Scott, T., Mannion, R., Davies, H., & Marshall, M. (2003). Healthcare performance and organizational culture. Oxford: Radcliff Medical Press. Tidd, J., Bessant, J., & Pavitt, K. (2001). Managing innovation integrating technological market and organizational change (2nd ed.). Chichester, UK: Wiley. Tornatzky, L. G., & Klein, R. J. (1982). Innovation characteristics and innovation adoptionimplementation: A meta-analysis of findings. IEEE Transactions on Engineering Management, 29, 28–45.

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Watkins, K. E., & Marsick, V. J. (1993). Sculpting the learning organization: Lessons in the art and science of systematic change. San Francisco, CA: Jossey-Bass. Watkins, K. E., & Marsick, V. J. (1996). Dimensions of the learning organization questionnaire. Warwick, RI: Partners for the Learning Organization. Yin, R., Bateman, P., & Moore, G. (1983). Case studies and organizational innovation. Washington, DC: Cosmos Corporation. 1 The results were obtained using the automated version by Marc Steinlin, Helvetas, 2002

Section 3

Creativity and Collaboration in Organizations

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Chapter 15

Exploiting KM in Support of Innovation and Change Peter A.C. Smith The Leadership Alliance Inc., Canada Elayne Coakes University of Westminster, UK

ABSTRACT This chapter emphasizes the importance of formally promoting close social interaction and open knowledge sharing to achieve superior innovation capability. It does so by discussing the advantages of developing Communities of Innovation and citing a case study that exemplifies these concepts. This chapter addresses the challenges and opportunities faced by businesses in today’s complex and often unpredictable business environments. For success, an organization must be able to combine and recombine their resources in novel ways, eliminating or reconfiguring resources that are no longer relevant, and acquiring new resources. An organization’s capability to change by manipulating resources continuously and rapidly—to innovate—is a competitive advantage that is not readily imitated by competitors. Innovation is critical to an organization’s viability since it enables the development and introduction of new products and services and thus enables an organization to maintain, or improve, its current business position. The chapter reviews the numerous theories of change and change management in the literature based on practice and precept. However, research shows that competitive advantage is increasingly located by authorities in an organization’s intellectual resources including the skill base, business systems and intellectual property of its employees: its Human Capital. Organizational innovation depends on the individual and collective know-how of employees, and innovation is characterised by an iterative process of people working together, sharing insights, and building on the creative ideas of one another. The chapter emphasizes that an organization’s intellectual resources have significant potential to realize innovation and change capabilities, but that the impact of these capabilities largely depends on the means of an organization to foster close community social interaction and open knowledge sharing, and to leverage its informal leadership as a precursor to and part of any related Knowledge Management (KM) initiative. DOI: 10.4018/978-1-60566-701-0.ch015

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Exploiting KM in Support of Innovation and Change

INTRODUCTION In trying to most effectively implement KM and foster innovation and change it is tempting for an organization to begin by simply introducing a system-wide technological solution. When the predicted performance improvements are not achieved further technological upgrades will likely be undertaken. However, although the centralization and codification of knowledge via storage/ retrieval systems and the like are often useful, a big impact on KM and related innovative performance should not be anticipated. This is because significant breakthroughs and competitive advantage typically come from the social exchange, exploitation, and augmentation of current tacit knowledge, rather than codified explicit knowledge based on past contexts. Without an understanding of the “as is” state of the organization’s inter-personal and collective relationships and their implications, plus an appreciation of how the organization’s culture influences these relationships, technological approaches are almost certainly doomed to failure. Indeed, a purely technological solution may make matters worse by creating a “credibility black hole” for future interventions. Sharing explicit and particularly tacit knowledge, and developing an open culture are typically challenging problems for organizations. In this chapter the authors describe a practical approach for resolving this concern. To this end they discuss the development of a positive organizational social fabric; the impact of “people factors” on relationships and knowledge sharing; the important role played by “Opinion Leaders” in ensuring that KM initiatives are undertaken only when social conditions are appropriate; practical means to identify these highly influential individuals and their networks; and an explanation of how identifying Innovation Champions will facilitate the formation of Communities of Innovation (CoInv) that will more effectively and speedily leverage an organization’s intellectual resources, and significantly enhance its innovative capability.

THE IMPORTANCE OF SOCIAL FABRIC The extent to which public and private conversations can take place across an organization, including its customers and stakeholders, will be critical to the widespread sharing and generation of knowledge. Successful knowledge management and innovation will depend on an organization having the means to readily share tacit and explicit knowledge. To this end, the current knowledge economy in both public and private sectors is optimally founded on having ready and effective communications across collaborative partnership-networks of all kinds. It is not just “What you know” or even “Who you know” but rather “Who you know and trust” that leads to a viable and effective social fabric. When examined closely, an organization’s social fabric is not homogeneous, but rather consists of innumerable unique social networks based on members’ interpersonal relationships. Its collaborative properties are exposed when the organization’s Social Capital (SC) is appraised. SC is “The set of resources, tangible or virtual, that accrue to a corporate player through the player’s social relationships, facilitating the attainment of goals” (Gabbay & Leenders, 1999; pp. 3). Trust, openmindedness, and lack of prejudice enhance SC, whereas distrust, fixed mindsets, and deep-seated independence foster low SC. Most importantly, the SC of individuals aggregates into the SC of the organization – its social fabric. The formation of SC clearly depends on having positive individual attitudes and emotions with respect to forming and sustaining interpersonal relationships. SC is best developed when individuals work together, particularly in teams (Bakker et al, 2006), or on a special project (Snowden, 2005). Reasons why organizations often do not achieve high SC and a collaborative and trusting social fabric are complex and illogical since tacit feeling-laden “people-factors” are involved. Unfortunately sub-optimal KM and innovative

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performance are in many cases directly attributable to these ambivalent or negative non-rational “people-factors”. These people-factors clearly will include task related issues, but at the semiconscious level they include complex feelings and values, and at the unconscious level include deep-seated anxieties, survival instincts, impulses, and power needs. Such factors often deter formation of appropriate relationships and will suppress knowledge creation (Stacey, 2001). Since most organizations operate under a facade of rationality (Smith & Sharma, 2002) these people-factors typically remain un-acknowledged or un-discussable, and enhancement of SC, knowledge sharing and innovation is effectively blocked. People-factors are often perceived as negative and are linked to what are deemed inappropriate emotional aspects of life rather than to the goalorientation that drives most organizations. Indeed emotional maturity is strongly associated with the control of feelings in organizations, and the word “emotional” is used in a belittling sense as a deviation from intelligence (Putnam & Mumby, 1993, p. 36). These same authors (Putnam & Mumby, 1993; pp. 37) note that organizations exert “overt and covert control over emotional displays” and “emotional labour” is expended in this effort (ibid; pp. 37) that for example will produce relationships based on compliance rather than the interest and commitment essential to successful change, KM and innovation. In 1973 Egan wrote “Emotional repression in organizations is undoubtedly still a far greater problem than emotional overindulgence” (p. 61) - thirty-five years later this statement is as true as ever. It is important to satisfy these needs since they directly correlate with the quality of individual performance (Fortune, 1997). Maslow (1943) postulated that individuals have an innate requirement to satisfy a hierarchy of needs, including self-actualization (Mahesh, 1993; p. 35). Self-actualisation is critical to development of the cultural traits and social fabric that successful KM and innovation require.

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How people in an organization meet one another is also very important. Often at formal meetings, related to say KM or innovation, the last thing people want is to reveal their real underlying concerns. On the surface, all appears well, and discussion proceeds in a calm and dignified manner; however, under the surface, a more turbulent encounter is often taking place that will profoundly affect any subsequent actions. One way to picture what is happening with regard to how an organization and its employees process these people-factors is as icebergs floating together in a sea. When icebergs meet, the submerged parts of the icebergs (people’s unawareness), which is much greater than the visible tips of the icebergs (people’s awareness), meet first. Change is like bereavement, and in order to move on, individuals must first build a new “reality” bearing in mind that knowledge sharing for innovation may well threaten an individual’s uniqueness and sense of self worth. For example, an iceberg tip might be articulated as “How do we develop a KM system to promote innovation for our organization?” whereas the underlying unarticulated problem that will need resolution might more realistically be defined as “How do I and the people in my team deal with feelings related to power loss and vulnerability etc?” Gaunt (1991) discusses the group conscious and unconscious awareness at various levels of an ‘iceberg’, and points out that the content is often defeated by the unarticulated process, which is largely about building trust. Such icebergs related to change, KM and innovation cannot be fused into a cohesive whole by examining and responding only to their tips. Practical initiatives set out by Smith and McLaughlin (2003) to “get the people factors right” as part of a convincing workforce development effort include: •

Community-wide collaborative development of a Vision for the KM system since this provides excellent understanding and

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•

•

•

•

motivation for relationship building based on a sharing of the individual yearnings of all employees; An appreciation of the physiological needs of individual employees based on Maslow’s theory (Maslow, 1943) that will enhance the willingness to form relationships via the need for belongingness, esteem, and striving to be the best a person can be; Improvement in the way people meet (and form relationships) by helping them become sensitized to the semiconscious and unconscious impulses that operate as individuals and groups struggle to come together. Concerns such as these may be explored through the discipline of group dynamics (most notably psychoanalysis, field and systems theories, and Gestalt); Application of the concept of a Personal Knowledge Management System (PKMS). This notion involves Action Learning based workshops, plus post-workshop Communities of Practice (Saint-Onge & Wallace, 2003), that populate an individual’s PKMS with appropriate cognitive, affective and resource related factors; Promotion of voluntary Communities of Practice (Saint-Onge & Wallace, 2003). These offer a powerful framework to promote formation of appropriate relationships based on conversations and activities of interdependent people in complex responsive processes (Stacey, 2001).

The authors have extended the work of Smith and McLaughlin (2003) with new research that is reviewed in the following sections. Although we agree that KM systems for innovation must be based on high quality collaborative relationships, we assert that such systems may be made more effective by first ensuring (through rigorous analysis) that they are based on a sound socialnetwork structure, and that there is a thorough

understanding of the influence of “Opinion Leaders” in the organization. Further that “Innovation Champions” may be identified together with their trust-tagged networks, and that these individuals if given appropriate KM and general organizational support will naturally form communities – we call these Communities of Innovation - that will have a significant impact on innovation. These topics are discussed in the next sections.

ASSURING SOUND SOCIALNETWORK STRUCTURE The perception that networks are a dominant organizing principle for how organizations “really work” is well established; for example, Cross and Parker (2004) present many examples from their practice, and case studies are available (TLA, 2006). Social networks in organizations are omnipresent and are central to knowledge sharing and influence, but often only the formal hierarchical network is acknowledged. There are innumerable social networks across an organization, but the following often have a significant impact on employees’ opinions: work, friendship, subject matter expert, management, leadership, innovation, client/customer, stakeholder, CoP, and CoI (Community of Interest). These are all examples of important intangible assets and organizational structures that could contribute directly or indirectly to value creation through innovation. These trust networks support the development of this capability by building and sustaining the SC necessary for innovation to develop and flourish. A special analytical technique called Social Network Analysis (SNA) (for details of SNA see for example Wasserman & Faust, 1997) is required to visualise the complexities of how people communicate in social networks. When mapped “real” communications channels are often seen to be distributed unevenly, since dense clusters tend to form around established relationships. The strong ties formed in

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these clusters have many benefits, but it is also critical to have “weaker” links between clusters to ensure the quick flow across the community of innovative ideas, and the timely awareness of new opportunities and challenges. For this reason, identification of weak ties, and knowledge of how to form and leverage them, is an important aspect of any KM implementation or optimization effort. An analytical technique such as SNA is particularly necessary for pinpointing these weaker links, since such ties are often informal, having little obvious relationship to the official organisational-communications design. Because of the importance of social network structure to the success of KM and innovation, an organization should develop an appreciation of, and the means to carry out SNA, plus the expertise to interpret the emergent social and communication patterns (Cross & Prusak, 2002). In this way the organization’s communications networks may be visualized and compared to optimal patterns e.g. “small world” networks (Buchanan, M., 2002), and contextual remedial interventions undertaken as necessary.

THE IMPORTANCE OF OPINION LEADERS Whatever the cultural climate in an organization, some individuals within it accumulate considerable SC, and achieve high levels of prestige and/ or influence with their peers. They form “core groups” and their names come up in anecdotes and as referrals time and again, sometimes because they have authority, but most often because they have attained legitimacy (Kliener, 2003). Such individuals are termed “Opinion Leaders”, and they are highly trusted as advisors by other individuals for a variety of reasons e.g. personal attributes, expertise, knowledge, longevity, local deployment, power etc. They assume heroic characteristics within an organization by matching existing “trust norms” fortified through emergent

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stories and myths. By providing an assessment of “local fit” opinion leaders are often seen as removing or confirming risk with respect to change and innovation. How they achieve this, and how they may be identified, are set out below. Given the scepticism and negative emotions that a personally threatening activity, such as the introduction of change or KM for innovation typically induces in an organization’s personnel (Harvey & Butcher, 1998; Smith & McLaughlin, 2003), one must anticipate that the attitudes and advice of opinion leaders will critically influence success or failure. The role of opinion leaders in implementation of KM for innovation is based on a framework for innovation diffusion developed and advanced by Rogers (1995) over a period of more than twenty years. According to Rogers, an innovation is an idea, object, or practice that is seen as new by an individual or group, and diffusion is the process by which an innovation is communicated over time among the members of their social system, including the process of understanding that follows reception of information (Warner, 2003). Rogers (1995) proposed that the innovation diffusion process takes place in five stages; however, for our purposes here, another and more important aspect of the process is “Innovativeness”. This is the extent to which an individual is relatively quicker than others in adopting or rejecting an innovation. Based on Rogers (1995) five categories of innovativeness are proposed: 1. Innovators who are gate keepers in the flow of new ideas into a social system; 2. Opinion Leaders who decrease uncertainty about a new idea by adopting or rejecting it and by conveying a subjective evaluation to near-colleagues; 3. Pragmatists who follow in adopting or rejecting an innovation and who through their position between the opinion leaders and the fence sitters are important links for further evaluation-diffusion and action;

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4. ‘Fence Sitters’ who according to Kautz & Larsen (2000) often have scarce resources which means that almost all of the uncertainty about a new idea has to be removed before they adopt; 5. Laggards who are extremely cautions and may never adopt any innovation. With regard to adoption or rejection of an innovation, these five categories are displayed versus time in Figure 1. In this figure, innovationrelated knowledge is traveling from left to right across the various subgroups of the community. Each sub-group of the overall community shares knowledge with the sub-group that follows it, and each in their turn serves to reduce the risk of adopting or rejecting the knowledge; this rhythm has implications for access and usage of a KM system for innovation. In a sense, opinion leaders function as the initial diffusion catalysts or inhibitors. About 14% of community members may be expected to be opinion leaders, and about 84% of

members will directly or indirectly rely on their advice. It is important to note that about 50% of the members will be very resistant to adoption of innovation in any event. When opinion leaders are identified, their input regarding design and implementation of all aspects of the organization’s KM agenda, including innovation, may be ascertained and focused e.g. advisory groups, communities of practice etc. If the opinion leaders are negative or apathetic to the proposed KM implementation or innovation, it is critically important that the organization’s decision makers be aware of the situation. Where the opinion leaders view the changes positively, “buy in” and take up may be realistically anticipated, and speed of adoption will be catalyzed as noted above. It is also important to certify that an organization has sufficient opinion leaders e.g. too many innovators and too few opinion leaders is the foundation for a stagnant organization even though the company may seem to be doing all the right innovative things.

Figure 1. Diffusion and Adoption/Rejection of Innovation (after Rogers 1995)

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From the above discussion, it is clear that an organization having at least an up-to-date list of its opinion leaders will be well placed to begin to sort out how to effectively evaluate and progress its KM options. A practical methodology to identify an organization’s opinion leaders is described in the next section.

IDENTIFYING OPINION LEADERS Network Visualization and Analysis (NVA) is the methodology used to identify an organization’s opinion leaders. Whereas SNA is concerned to elucidate network performance (information flow, segregation, vulnerability to disruption etc.) with little emphasis on understanding the characteristics of the individuals that form the network nodes, NVA is intended to elucidate node characteristics (personal characteristics, who trusts whom etc) but provides less information regarding network performance. In NVA, an Archetype is developed that describes the features of the individuals to be identified. Data regarding “who influences whom” are then collected from the target organizational population, or the whole organization. In the past this was a very time consuming manual task involving interviews and/or lengthy questionnaires. Today software exists to streamline and automate this function. A typical data gathering process begins with an archetype-based query sent by email to all individuals in the target community e.g. “Who would you go to for help in weighing up a new organizational initiative that affects your work?” Each person selects from an online list of names that are recognizable as co-workers. New names e.g. external contacts, may be added online to the list. Based on data that each individual voluntarily provides in response to the query, a list of individuals who are influential is developed, ordered by degree of influence. Based on data from a number of questions reflecting different aspects of the archetype, a picture emerges of the various

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opinion leaders, their locations and networks. Personal attributes may be listed for each individual to further enrich the picture. A practical application is described below based on an NVA project undertaken at a major retail organisation with branches in a number of different cities. One of the primary objectives was to identify the most influential individuals from the company’s most senior management levels across all locations and departments with regard to “leadership influence” with the objective of forming one or more steering groups to facilitate development and introduction of a new Leadership Development Programme across the company. An email-delivered question relating to the above objective were posed to all members (about 100 individuals) of the most senior management levels across all the company’s locations and departments. Members of this target group responded by picking names from a list displayed on the Internet. This list contained names for all the approximately 100 individuals targeted for the study. Respondents were free to write in names of individuals not identified in the original target community; questions were not posed to these “write-ins”. The question posed was “In your role as a leader in our company, whom do you seek out for ‘brainstorming’ around dealing with complex issues in business, interpersonal or cross-functional situations? Think about people in your area and other people within the business.” Prior to emailing out questions, the company’s HR Director informed the target community of the reasoning behind the project and set out the process. A period of two weeks was allowed for all those wishing to respond to the question to do so. The final response by the group to the question was around 75%. After completion, NVA results were reported to the study sponsors for further dialog and finally for action. The NVA provided the names, locations and management levels for a signifiant nimber of people sought out most often by others in a leadership influence context. It was noteworthy that names were identified that

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surprised the study sponsors. This is fairly typical of such studies, and demonstrates the informal nature of much network activity, and in particular the unsystematic characteristics of trust-tagging. Influential steering groups and knowledge sharing communities for the development and support of the new leadership programme were formed from individuals drawn from the leadership list. NVA also provided a list of names of (a) those who seek out the most influential individuals, and (b) those who these influential individuals themselves seek out. This significantly contributed to understanding the informal communications links and influence patterns across the various organisational characteristics included in the study. This information was used to facilitate setting up the steering groups and knowledge sharing communities on already existing trust-tagged networks. Communication and knowledge sharing was further facilitated within these get-togethers using action learning (Smith, 2005c). It is conceded that if KM implementation is founded largely on the counsel of opinion leaders, all members of an organization will not have a direct voice in its introduction; however, the views of employees in general will certainly be represented in a more focused and visible fashion than is currently the norm, or indeed is practical given the constraints of time, budgets etc.

THE ROLE OF KNOWLEDGE COMMUNITIES In previous sections the importance of fostering collaborative trusting social interactions has been emphasized, and this self-organising human interaction, with its ability for emergent creativity, is at the centre of the desired knowledge creating and sharing processes. Closely networked communities e.g. Communities of Practice (CoP), are a one of the most powerful supporting and promoting organizational forms for this social interaction. In a CoP, the generation of new ideas

that activates innovation is fostered and facilitated by the diversity and breadth of experience to hand in the community e.g. experts who have a great deal of contact with other experts in the fields; links to users; and links to ‘outsiders’. Creativity frequently appears at the boundaries of disciplines and specialties, and communities such as CoP will work hand in hand collaboratively with other communities within and between organizations inter-and intra-organizationally. These close-knit communities are the place where innovative new practices, new services and new products will be developed, and they may be serviced appropriately by KM. As was noted earlier, Human Capital contains the intellectual capability to create and innovate through the mixing of skills with knowledge and this innovation occurs within the context of organizational culture and its shared values, beliefs, expectations and attitudes. Indeed Lesser and Storck (2001) argue that we must think of a closeknit community such as a CoP as an engine for the development of social capital. They argue that the SC resident in communities of practice leads to behavioral changes, which in turn positively influence business performance. Social capital, in particular, they argue decreases the learning curve, increases responsiveness to customer experiences, reduces rework and prevents reinvention, and also increases innovation. Communities of Practice that are specifically dedicated to furthering innovation are termed by the authors Communities of Innovation (CoInv), and members of a CoInv, being themselves ideas champions, naturally support creative notions; provide the structure to permit those interested in innovation to become involved; and by their nature as members of a form of CoP, are given autonomy in their innovation activities and are afforded the necessary resources to this end e.g. KM. As part of their natural formation CoInv provide the necessary ‘safe places’ for innovative ideas to be mutually explored and sheltered. In addition intrinsic motivation is a character trait of

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those involved in such a community, and people are most creative when they are motivated by interest, satisfaction and challenge. In forming and operating CoInv it is critical to leverage the influence and expertise of “innovation champions” - individuals who have the social, political and/or interpersonal knowledge to influence the acceptance of innovative change; these individuals are described in the next section.

INNOVATION CHAMPIONS Innovation champions are special people, with particular personality types and psychological profiles (Coakes and Smith, 2006). Champions have a naturally developed range of networks in which they participate and may be characterised as renaissance people (Howell, 2005) with a large variety of interests and a diversity of activities. They tend to have had a long tenure in the organisation (Howell and Higgins, 1990) with experience in many divisions and locations, and an in-depth knowledge of the industry. In order to succeed in championing innovations in organizations they need both procedural and resource support, and social and cognitive support. Howell (2005) has identified six crucial things that innovation champions require from the workplace: to work within an innovative environment; to work with other innovators; to be challenged and to learn; to be (socially) connected within and without the organisation; to be recognised for their work; and to work for management that supports their activities. The influence of innovation champions comes through social contacts, multiplied through the communities in which they participate, and through the genuine esteem in which they are held. As previously stated, innovation champions already have extensive social networks in place and the challenge to the organisation and those charged with managing innovation is to transform these networks into CoInv. Once a CoInv is formed

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all the other champion requirements, as identified by Howell, naturally fall into place. CoInv will provide stimulus and constant learning opportunities as they will be built from other champions from all parts of the organisation, irrespective of discipline, functional unit, and organisational role. On this basis we argue that developing closeknit CoInv around such champions makes very practical sense for organizations. The presence of champions operating within these communities will be inspiring and stimulating. As is the case for opinion leader, innovation champions often exert their influence informally, and the issue for organizations is again how to discover these innovation champions and their social network so that stimulation, development and population of CoInv may be undertaken. The process of identification is similar to that for opinion leaders – an archetype is developed that describes the characteristics of the individuals to be identified and Network Visualization and Analysis is undertaken as described previously. Once innovation champions have been located, these individuals will by their nature be motivated to form CoInv. The organization must encourage and support them in this effort e.g. via traditional community building and technological (KM) methods. The know-how and influence of these individuals may then be leveraged by sustaining and harvesting the CoInv in the same way as a CoP. Such an approach will effectively and speedily enhance significantly an organization’s competitive edge.

CONCLUSION This chapter has emphasized that an organization’s intellectual resources have significant potential to realize superior innovation and change capabilities, but that the impact of these capabilities depends significantly on the means at an organization’s disposal to promote close community social interaction and open knowledge sharing, and to

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leverage its informal leadership as a precursor to, and as part of, any related Knowledge Management (KM) initiative.

REFERENCES Bakker, M., Leenders, T. A. J. R., Gabbay, S. M., Kratzer, J., & Van Engelen, J. M. L. (2006). Is trust really social capital? Knowledge sharing in product development projects. The Learning Organization, 13(6), 594–605. doi:10.1108/09696470610705479

Howell, J. M. (2005). The right stuff: Identifying and developing effective champions of innovation. The Academy of Management Executive, 19(2), 108–119. Howell, J. M., & Higgins, C. A. (1990). Champions of technological innovation. Administrative Science Quarterly, 35, 315–341. doi:10.2307/2393393

Buchanan, M. (2002). Nexus. New York: Norton.

Kautz, K., & Larsen, E. A. (2000). Disseminating quality management and software process improvement innovations. Information Technology & People, 13(1), 11–26. doi:10.1108/09593840010312726

Coakes, E., & Smith, P. A. C. (2006). Using communities of practice for sustainable change management. Paper presented at Knowledge Management Aston Conference, Birmingham, UK.

Kleiner, A. (2003). Core groups: A theory of power and influence for ‘learning’ organizations. Journal of Organizational Change Management, 16(6), 666–683. doi:10.1108/09534810310502595

Cross, R., & Parker, A. (2004). The hidden power of social networks. Boston: Harvard Business School Press.

Lesser, E. L., & Storck, J. (no date). Communities of practice and organizational performance. Knowledge Management, 40(4). Retrieved 2004, from http://www.research.ibm.com/journal/ sj/404/lesser.html

Cross, R., & Prusak, L. (2002). The people that make organizations stop – Or go. Harvard Business Review, 80(6). Egan, G. (1973). Face To Face. Monterey, CA: Brooks/Cole. Fortune. (1997, October 13). Gabbay, S. M., & Leenders, R. T. A. J. (1999). The structure of advantage and disadvantage. In Leenders, R. T. A. J., & Gabbay, S. M. (Eds.), Corporate social capital and liability. Boston: Kluwer. Gaunt, R. (1991). Personal and group development for managers: An integrated approach through action learning. Harlow, UK: Longmans. Harvey, P., & Butcher, D. (1998). Those who make a difference: Developing businesses through developing individuals. Industrial and commercial training, 30(1), 12-15.

Mahesh, V. (1993). Thresholds of motivation. New Delhi: Tata McGraw-Hill. Maslow, A. (1943). Theory of human motivation. Psychological Review, 50. Putnam, L. L., & Mumby, D. K. (1993). Organizations, emotion and the myth of rationality. In Fineman, S. (Ed.), Emotion in organization (pp. 36–57). London: Sage. Rogers, E. M. (1995). Diffusion of innovation (4th ed.). New York: Free Press. Saint-Onge, H., & Wallace, D. (2003). Leveraging communities of practice for strategic advantage. New York: Butterworth-Heinemann.

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Smith, P. A. C. (2005a). Knowledge sharing and strategic capital: The importance and identification of opinion leaders [Special issue: Knowledge Sharing]. The Learning Organization, 12(6), 563–574. doi:10.1108/09696470510626766 Smith, P. A. C. (2005b). Organizational change elements of establishing, facilitating, and supporting cops. In Coakes, E., & Clarke, C. (Eds.), Encyclopedia of communities of practice in information and knowledge management (pp. 400–406). London: Idea Group Reference. Smith, P. A. C. (2005c). Collective learning within CoPs. In Coakes, E., & Clarke, C. (Eds.), Encyclopedia of communities of practice in information and knowledge management (pp. 30–31). Hershey, PA: Idea Group Reference. Smith, P. A. C., & McLaughlin, M. (2003). Succeeding with knowledge management: Getting the people-factors right. Paper presented at the 6th World Congress on Intellectual Capital & Innovation, January 15-17, McMaster University, Hamilton, Canada.

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Smith, P. A. C., & Sharma, M. (2002). Rationalizing the promotion of non-rational behaviors in organizations. The Learning Organization, 9(5). doi:10.1108/09696470210442132 Snowden, D. (2005). From atomism to networks in social systems. The Learning Organization, 12(6), 552–562. doi:10.1108/09696470510626757 Stacey, R. D. (2001). Complex responsive processes in organizations. London: Routledge. TLA. (2006). Case Studies 1 – 5. Retrieved August 1, 2006, from http://www.tlainc.com Warner, J. (2003). Modeling the diffusion of specialised knowledge. Aslib Proceedings, 55(1-2), 75–83. doi:10.1108/00012530310462733 Wasserman, S., & Faust, K. (1997). Social network analysis: Methods and applications. Cambridge: Cambridge University Press.

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Chapter 16

Knowledge Management Profile: An Innovative Approach to Map Knowledge Management Practice Zoltán Gaál University of Pannonia, Hungary Lajos Szabó University of Pannonia, Hungary Nóra Obermayer-Kovács University of Pannonia, Hungary Zoltán Kovács University of Pannonia, Hungary Anikó Csepregi University of Pannonia, Hungary

ABSTRACT For knowledge-intensive organizations, it is important to carry out an objective assessment of their current position in the area of knowledge management activities and processes. Uncertainty presents a barrier to the introduction of suitable activities for improving knowledge management. We believe that the results of the research will be significant to practice and will provide substantial support for leaders and managers. Moreover the right knowledge management activities can help push thinking beyond the everyday in a way that spurs innovative creativity. To ensure success and long-term existence of any organizations effective application of organizational knowledge and knowledge management practice is of critical importance. Besides simply assessing the benefits inherent in knowledge management, the organizations must learn to recognize and manage the different areas of their knowledge management practice. Our innovative solution, the “Knowledge Management Profile” is devoted to the formulation of a new knowledge management maturity model, which is believed to be of vital importance in the quest of the successful knowledge management practice. DOI: 10.4018/978-1-60566-701-0.ch016

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Knowledge Management Profile

INTRODUCTION “In an economy where the only certainty is uncertainty, the one sure source of lasting competitive advantage is knowledge.”(Nonaka, 1991, P.96). The knowledge economy influences the innovation process and the approach to innovation. The traditional idea that innovation is based upon research and interaction among organizations is replaced by the current social network theory of innovation, where knowledge plays a critical role in fostering innovation. In the knowledge economy, innovation has become central to achievement in the business world. Organisations have begun to re-evaluate their products, services and corporate culture in the attempt to maintain their competitiveness in the global markets of today. The organizations need to develop, collect, share and implement organizational knowledge efficiently and effectively.The more forward-thinking companies have recognised that only through such root and branch reform can they hope to survive in the face of increasing competition. At the same time, organisations need new initiatives to develop the methodologies and tools to support the management of innovation in business. Higher education establishments, business schools and consulting companies are developing appropriate methodologies and tools. Innovation exists in many forms. In addition to traditional technological innovation, there is innovation through new business models, new ways of organising work. Managing and exploiting to best effect all these different kinds of innovation represents a major challenge today (EC 2004). In March 2000, European Union heads of state and government agreed on an ambitious goal: making the EU “the most competitive and dynamic knowledge-based economy in the world, capable of sustainable economic growth with more and better jobs and greater social cohesion”. They designed a digitalized economy and society where the living standard and the working conditions are

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improving, job opportunities are expanding and productivity is continually increasing. One of the essential prerequisites for the attainment of those objectives is to disseminate info communication technologies (ICT) to the greatest possible extent. In Lisbon, instead of emphasizing the macroeconomic stability of the European Union, the leaders announced structural reforms, with particular consideration to the field of expanding the digital society, which in turn should promote the foundation of the knowledge society (Blanke et al. 2002). Knowledge and innovation play an essential role in the knowledge society. The emergence of the knowledge based economy, the globalization and the competition have impacts on organizational growth, sustainability and survival. For most of the organizations, innovation and managing knowledge are no longer extraordinary conceptions, but rather necessities and a means of sustaining economic development and competitiveness (Uden et al. 2007). Our main objective was to develop a practically applicable and, at the same time, theoretically and methodologically founded approach that contributes to the exploration and realisation of knowledge management-related organizational activities. Our innovative solution is devoted to the formulation of a new knowledge management maturity model, which is believed to be of vital importance in the quest of the successful knowledge management practice. We attempted to model the factors governing knowledge management practice in organizations.

LITERATURE REVIEW Maturity modelling is a general approach, which represents the development of an entity in progress of time through levels towards a usually idealistic final phase. This entity can be human being or organisational function, etc. There are four main properties of the maturity models (Klimko 2001):

Knowledge Management Profile

•

The development of an entity is simplified and described with a given number of maturity levels; The levels are characterised by certain requirements that the entity has to achieve on that level; The levels are sequentially ordered, from the initial up to the ending level; During development, the entity is stepping forward from one level to the next one and no levels can be left out.

•

• •

Maturity models are the result of the application of the life-cycle approach. Each entity develops through the levels over time until it reaches perfection – up to the highest level. In the knowledge management literature there are some knowledge management maturity models elaborated by researchers and practitioners. Existing knowledge management maturity models can be categorized into two groups, depending on whether they are based on Software Engineering Institute’s (SEI) Capability Maturity Model (CMM) or not. In the CMM, five levels of maturity are defined, with each level described by a unique set of characteristics. Apart from level 1, several different key process areas are identified at every maturity level. Each key process area indicates

that the organization should focus on in order to improve its process (Paulk et al. 1993). There are four well-known CMM-based KMMM: Siemens’ KMMM, Infosys’ KMMM, Paulzen and Perc’s (2002) Knowledge Process Quality Model (KPQM), and Kulkarni and Freeze’s (2004) Knowledge Management Capability Assessment Model (KMCA). All models are based on CMM and have (except KMCA) identified five levels of KM maturity. There are many non-CMM-based KMMMs, like KPMG’s Knowledge Journey (KPMG 2000), Klimko’s KMMM (Klimko 2001), TATA Consultancy Services’ 5iKM3 KMMM (Mohanty and Chand 2004), and WisdomSource’s K3M (WisdomSource 2004). Two types of maturity models are known: the staged and the continuous. In the staged maturity model, such as Capability Maturity Model (CMM) (Paulk et al. 1993), the development of a single entity is described with a limited number of maturity levels (usually four to six levels). Continuous maturity models use the concept of ’process area’, where maturity is interpreted in the context of process areas. An organisation can develop itself simultaneously in different process areas. The maturity model can be a tool for comparison between organizations. If the description of the levels contains the characterisation of the

Table 1. Maturity levels of CMM (Paulk et al. 1993) Maturity Level

Characteristics Software process is characterized as ad hoc, or even chaotic. Few processes are defined and success is due to individual efforts.

1

Initial

2

Repeatable

3

Defined

Software process for both management and engineering activities is documented, standardized and integrated into a standard software process for the organization. All projects use an approved, tailored version of the organization’s standard software process for developing and maintaining software.

4

Managed

Detailed measures of the software process and product quality are collected. Both the software process and products are quantitatively understood and controlled.

5

Optimizing

Basic project management processes are established to track cost, schedule and functionality. The necessary process discipline is in place to repeat earlier successes in projects with similar applications.

Continuous process improvement is enabled by quantitative feedback from the process and from piloting innovative ideas and technologies.

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Table 2. Maturity levels of CMM-based KMMM (Paulk et al. 1993) Level

CMM

CMM-based KM Maturity Models Siemens’ KMMM

0

Infosys’ KMMM

KPQM

KMCA

Not Applicable

Difficult / Not Possible

1

Initial

Initial

Default

Initial

Possible

2

Repeatable

Repeatable

Reactive

Aware

Encouraged

3

Defined

Defined

Aware

Established

Enabled / Practiced Managed Continuously Improving

4

Managed

Managed

Convinced

Quantitatively Managed

5

Optimizing

Optimizing

Sharing

Optimizing

processes to be achieved, the entities could be compared (Klimko 2001).

CONSCIOUS KNOWLEDGE MANAGEMENT IN A KNOWLEDGE ECONOMY - RESEARCH ‘Knowledge Management in Hungary 2005/2006’ Empirical Survey In 2004, Department of Management at the University of Pannonia joined forces with KPMGBME Academy in order to investigate the current state of knowledge management in Hungarian profit and non-profit sectors. Therefore, a detailed survey – ’Knowledge Management in Hungary

2005/2006’ - was conducted, which examined the successfulness of knowledge management practice of organizations. Based on the literature and on previous surveys (KPMG 2003, KPMG Hungary 2000), we created a survey instrument consisting of eight demographic and twenty-six questions related to knowledge management, divided into four main areas: general information, knowledge management, the success of the knowledge management programs and at last knowledge management technology and investment. We used a large-scale Internet survey. The Internet approach was faster and more cost-effective that a mail-out survey and it helped reducing non-response errors. In order to reach the respondents, an invitation email with the URL of the online questionnaire was sent to the organizations. The participation of

Table 3. Maturity Levels of non CMM-Based KMMM (Paulk et al. 1993) Level

Knowledge Journey

5iKM3

Klimko’s KMMM

K3M

1

Knowledge chaotic

Initial

Initial

Standardized Infrastructure for Knowledge Sharing

2

Knowledge Aware

Intent

Knowledge Discoverer

Top-Down Quality-Assured Information Flow

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3

Knowledge Focused

Initiative

Knowledge Creator

Top-Down Retention Measurement

4

Knowledge Managed

Intelligent

Knowledge Manager

Organizational Learning

5

Knowledge Centric

Innovative

Knowledge Renewer

Organizational Knowledge base / Intellectual Property Maintenance

6

Process-Driven Knowledge Sharing

7

Continual Process Improvement

8

Self-Actualized Organization

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this study was voluntary. Reminder emails were also sent a few weeks after the first invitation email. In the course of the survey, answers from 130 organizations were included in the database, which was used as the basis of our research activity (KPMG-BME Academy 2006). No previous Hungarian survey connected with knowledge management was based on such comparably large sample. The results are unique and new regarding both the number of participants and the wide scope of respondents. The organizations involved in the survey are all based in Hungary; they include privately owned Hungarian companies, enterprises under joint foreign and Hungarian ownership, subsidiaries of global international companies, and other organizations in the fields of education, health care and public administration. The respondents are mainly upper level managers, owners and HR, strategic or IT managers. Some participants are middle level managers in the fields of economics, finances, marketing, supply, logistics or consulting. Among the organizations involved in the survey, all sectors of industry are represented with different portions. The majority of the respondents work in information technology, industrial production, business services, or retail trade. The representation of public administration is also significant in the sample (Óvári et al. 2007). The completed questionnaires received in the course of the empirical survey were evaluated with the help of Professional Quest 3.5 and Microsoft Office Excel 2003 software.

Statements from the Empirical Survey The ‘Knowledge Management in Hungary 2005/2006’ survey deals with several topics concerning knowledge management. We have selected the key findings, which are outlined in this section (Gaál et al. 2008). In general, knowledge management practice in Hungarian organizations is characterized by recognition of significance of the efficient management of knowledge, and the availability of the utilizable knowledge and

the required infrastructure. The results show growing awareness of knowledge management, its value to business and the benefits resulting from a systematic and holistic approach to the effective use of intangibles. In the application of knowledge, there is huge potential, which is still mostly unexploited. It indicates that the current level of knowledge management implementation is rather a challenge for the future than the reality at present. Respondents were asked whether their organization had a knowledge management strategy. The answers show that organizations in Hungary are just starting to implement knowledge management strategies. Overall, only 37% of respondent organizations have developed a comprehensive strategy in the form of a written document; however, 77% are indicating knowledge as a strategic asset. It can be seen that there is huge gap between the reality and the desire. 22% of the participants have knowledge management program and 30% are currently setting up or considering one. Of 130 surveyed organizations, only 3% of organizations that considered the need for such a program and then decided against it. Nearly one third of the respondents were not aware of any, denominated knowledge management programs being conducted in the organization. Those having such a program most frequently mentioned the need for a program for the improvement of knowledge sharing and access (31%), building up a knowledge repository (30%) and the nurturing a knowledge-sharing culture (17%). Knowledge management is seen as a key accelerator for “realising synergies between units”, “improving quality”, and “increasing added value for customers” are shown on Figure 1. The survey stated that the major prerequisite of long-term knowledge management is deemed to be “the embedding of knowledge management in daily work processes” (61%) and “the motivation of the work force to use knowledge management” (61%). The most popular in knowledge management initiatives are document databank, information centre, and centre of excellence as

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Figure 1. Key objectives

are shown on Figure 2. “Establishing formal knowledge management networks” (47%), “defining knowledge centres” (33%) and “developing communities of practice” (29%) were indicated as the most favoured knowledge management projects. A half of the respondents affirmed that leaders attach high importance to knowledge management, they were considered to be the main drivers of knowledge management initiatives. Technology is an essential tool for supporting knowledge management. The level of technology implementation is high, however, special applications are still only used by a low proportion of organization, such as Decision Support System (22%), Document Management System (27%). The most popular IT applications were Internet (implemented by 92% of the respondents), Intranet (69%) database systems (76%). From the perspective of knowledge management, all the technologies mentioned in the survey were assessed as more effective than ineffective. Technologies, which are widely known and applied, got higher scores than those less recognized. The most significant problems are an inadequate understanding of the benefits of knowledge management scoring 8.7 and a lack of time to share knowledge 8.5 on a scale from 1 to 10 (where 1 is “not at all significant” and 10 “extremely significant”). Other issues recognized by respondents were difficulty in capturing tacit knowledge (8.3); lack of resources (7.4) and information overload (7.2). Three characteristic stages can be

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identified for the period of the last fifteen years. In the first stage, the organized and structured documentation of organizational knowledge was decisive; the second stage was focused on the promotion of knowledge sharing and utilization between the employees within the organization, while the current third stage has the aim of establishing cooperation between networks of professional communities and competence centres. The results of the survey, lead us to conclude that the majority of domestic organizations are currently in the second stage (KPMG-BME Academy 2006). The results are unique and new regarding both the number of participants (130 organizations) and the wide scope of respondents (organizations with turnover figures below EUR 400 thousand and above EUR 4 million were also represented).

KNOWLEDGE MANAGEMENT PROFILE MATURITY MODEL The main objective of our research was to develop a practically applicable and, at the same time, theoretically and methodologically founded approach that contributes to the exploration and realisation of knowledge management practice. We sought answer to the following question: What are the determinant factors in the knowledge management practice of an organization? In order to describe organizations’ knowledge management practice, a measurement scale, a maturity model, had to

Knowledge Management Profile

Figure 2. Knowledge management initiatives

be developed. The aim of our examination was to reveal knowledge management peculiarities as well as the correlation of its elements. In the first part of the examination, we defined thirtytwo knowledge management elements from our empirical survey. Knowledge management elements represent knowledge management-related organizational activities. Using SPSS software, we conducted descriptive statistics, including correlation and factor analyses. Factor analysis groups the variables into a smaller number of components that are easier to

analyze. This type of analysis is especially useful in developing a new instrument. We conducted factor analysis with Varimax (orthogonal) rotation, which technique rotated the data to give us clear patterns of which items loaded on which factor; in other words, it rotated the data to give us a more interpretable solution. We examined the rotated component matrix, the sree plot (see Figure 3) and extracted seven factors; they are (1) consciousness; (2) storage; (3) sharing; (4) technology; (5) information; (6) community and (7) infrastructure. The seven factors extracted

Figure 3. Scree plot

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Knowledge Management Profile

Table 4. Knowledge management factors and elements Factors

Elements

1. Consciousness

Strategy; Building-up a strategy; Existence of a program; Training, Awareness

2. Storage

Program for measurement of knowledge capital; Program for building up a knowledge repository; Document databank; Knowledge database; Centre of excellence; Building-up a knowledge database

3. Sharing

Program for nurturing knowledge sharing culture; Program for improvement of knowledge sharing and access; Competence centre; Sharing “best practices”

4. Technology

Extranet; Measuring intellectual capital; Artificial intelligence; Document management system; Decision support system; Performance Management system

5. Information

Information centre; Formal channels of information; Appoint knowledge officers / Knowledge centres; Data warehousing

6. Community

Communities of practice; Customer communities; Supplier communities; Build and develop “communities of practice”

7. Infrastructure

Internet; Intranet; Benchmarking; Groupware

represent 57,038% of the total variance of the original variables, which is acceptable for a factor analysis (Gaál et al. 2008). By indicating the factor values in a net chart, we identified the ‘Knowledge Management Profile’, which is perceived as a continuous maturity model. The consciousness factor refers to the entirety of the fundamental factors that facilitate the creation of knowledge management practice. The storage factor comprises those factors that serve the storing and recording of intellectual capital accrued in the organization. The sharing factor comprises the programs and initiatives related to knowledge transfer. The technology factor contains special information technology applications and solutions. The information factor covers those factors that are related to managing information to be found within the organization. The community factor involves knowledge management initiatives, which concentrate on the creation of special ’communities’. The infrastructure factor includes those factors that provide basic information technology background to knowledge management. The ’Knowledge Management Profile’ is a continuous maturity model. It names areas to be identified with the help of the knowledge man-

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agement factors, within which areas the given organization can develop its knowledge management practice by introducing and applying the individual knowledge management elements. The ‘Knowledge Management Profile’ model enables a comparison with other organizations or groups of organizations. It is a framework that does not require strict ordering to be followed when elaborating and implementing the knowledge management elements connected with the individual areas. It permits different modes of development, so an organisation can develop itself simultaneously in different areas (Gaál et al. 2008).

Practical Aspects Throughout our research, it has been a major aim of our research that the results should be a practical value. The empirical survey, the result of which formed the basis of our investigations, gives an overview of knowledge management practice in Hungarian organizations. It indicates what stage they have reached in the development and application of knowledge management and what the level of consciousness is regarding the management of the knowledge hoarded in the organization. In terms of its practical implications, this study provides a ‘Knowledge Management Profile’ for

Knowledge Management Profile

Figure 4. Knowledge Management Profile

the organizations, which can be used as a tool to map the practice of knowledge management using the seven determinant factors. The factors indicate those areas of knowledge management practice in organizations that should be established and developed, while the knowledge management elements refer to actual activities to be performed. To ensure success and long-term existence of any organizations effective application of organizational knowledge and knowledge management practice is of critical importance. Besides simply assessing the benefits inherent in knowledge management, national organizations must learn to recognise and manage the different areas of their knowledge management practice. The ‘Knowledge Management Profile’ is a continuous model. It is a framework, that does not require strict ordering to be followed when elaborating and implementing the knowledge management elements connected with the individual areas. It permits different modes of development, so an organisation can develop itself simultaneously in different areas (Gaál et al. 2008).

Future Work The issues raised here in our paper warrant further research. In the near future, we plan to repeat

our empirical knowledge management survey with a larger sample. The aim of the research would be to explore the knowledge management practice of Hungarian organizations as accurately as possible, and to identify directions of change and development. Moreover, a more developed version of the ‘Knowledge Management Profile’ could incorporate other external factors, which have an impact on knowledge management, such as organizational culture, structure, or leadership. The research could be expanded to include the organizations of the states, which joined the European Union in 2004, since it would be important to make comparison between knowledge management practices in the individual states.

CONCLUSION Benchmarking and modeling are always needed to assess and evaluate the state of acceptance and maturity of any business initiative, which has the opportunity to impact the business process and delivery. In our competitive world, knowledge management has become one of the most brilliant capabilities by a forward-thinking organization can have, and this is now one of the most important business decisions, which influences business de-

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Knowledge Management Profile

livery. As investment in knowledge management initiatives has increased, a demand for complex principles to guide implementation efforts has appeared. As a practical tool, maturity modelling was proposed with the primary objective of enabling organizations to map their knowledge management practice (Gaál et al. 2008). In the knowledge economy, establishing bridges between knowledge and the marketplace and putting in place the right environment for innovation is the key to building competitiveness. The knowledge economy also represents new opportunities and requires some design actions to support and take advantage of this economy (EC 2004). In this paper, an innovative model for knowledge management maturity was formulated, which could serve as a basis for comprising different stages of an organization development or companies, in the same sector of industry. If levels involve set to define the goals to be achieved, the organizations could be ranked and compared. Following this line of reasoning, this article emphasizes that our model can be a useful tool for assessing knowledge management development and indicating possible improvements in organizations and it serves as indication of areas needing more resources and guidance in improving knowledge management.

REFERENCES Blanke, J., Cornelius, P. K., Mettler, A., Mundschenk, S., Paua, F., & von Hagen, J. (2002). The Lisbon Review 2002-2003. An Assessment of Policies and Reforms in Europe. World Economic Forum, Geneva. Retrieved from http:// www.weforum.org/pdf/Gcr/Lisbon Review/ LisbonReview_2002.pdf EC (European Commission). (2004). Innovation management and the knowledge-driven economy. ECSC-EC-EAEC Brussels-Luxembourg. Retrived from http://www.nkth.gov.hu/hivatal/elemzesek -hatteranyagok/innovation-management 262

Gaál, Z., Szabó, L., Kovács, Z., ObermayerKovács, N., & Csepregi, A. (2008). Knowledge management profile: Maturity model. In Proceedings of 9th European Conference on Knowledge Management (ECKM 2008), Southampton Solent University, Southampton, UK. Gaál, Z., Szabó, L., & Óvári, N. (2007). Knowledge management and its cultural perspective. In Proceedings of Knowledge Management in Organization (pp. 10–14). Lecce, Italy: New Trends in Knowledge Management, University of Salento. Klimko, G. (2001). Knowledge management and maturity models: Building common understanding. In Proceedings of the 2nd European Conference on Knowledge Management, Bled, Slovenia (pp. 269-278). KPMG. (2000). Knowledge management in Hungary– Research. KPMG Consulting Report, Budapest. KPMG. (2003). Insights from KPMG’s European Knowledge Management Survey 2002/2003. KPMG Consulting Report. Amsterdam. KPMG-BME. (2006). Academy: Knowledge management in Hungary 2005/2006. KPMG BME Academy – University of Pannonia Report. Budapest: KBA Kft. Kulkarni, U., & Freeze, R. (2004). Development and validation of a knowledge management capability assessment model. In Proceedings of the Twenty-Fifth International Conference on Information Systems, Washington, DC (pp. 657-670). Mohanty, S. K., & Chand, M. (2004). 5iKM3 knowledge management maturity model for assessing and harnessing the organizational ability to manage knowledge. Mumbai: TATA Consultancy Services. Retrieved from http:// www.tcs.com/NAndI/default1.aspx? Cat_ Id=154&DocType=324&docid=419

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Nonaka, I. (1991). The knowledge-creating company. Harvard Business Review. NovemberDecember. Óvári, N., Gaál, Z., & Szabó, L. (2007). Cultural impacts on organizational knowledge sharing. In DAAAM International Scientific Book 2007. Vienna: DAAAM International Publishing. doi:10.2507/daaam.scibook.2007.15 Paulk, M. C., Weber, C. V., Curtis, B., & Chrissis, M. B. (1995). The capability maturity model: Guidelines for improving the software process. Reading, MA: Addison-Wesley.

Paulzen, O., & Perc, P. (2002, December). A maturity model for quality improvement in knowledge management: Enabling organisations and society through information systems. In Proceedings of the 13th Australasian Conference on Information Systems, Melbourne, Australia (pp 243-253). Uden, L., Kekale, T., & Naaranoja, M. (2007). Knowledge management and innovation. In Proceedings of the 3rd International Conference on Knowledge Management in Organization, In proceedings of Knowledge Management in Organization, New Trends in Knowledge Management, University of Salento, Lecce. WisdomSource. (2004). K3M knowledge management maturity model. Wisdomsource News, 2(1). Retrieved from http://www.wisdomsource.com/ co ntentassets/K3M%20Overview.pdf

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Chapter 17

Recognizing Innovation through Social Network Analysis: The Case of the Virtual eBMS Project Grippa Francesca University of Salento, Italy Elia Gianluca University of Salento, Italy

ABSTRACT Advances in communication technologies have enabled organizations to develop and operate decentralized organizational structures by supporting coordination among workers in different locations. Such developments have lessened formality in control structures and replaced formal channels of communication with less formal social networks. The chapter describes the development and application of a ‘Social Network Scorecard’(SNS) managerial tool to monitor social interchanges and relationships within and across organizations in order to assess the effectiveness of knowledge networks. In this chapter, a project team made up of individuals from academia and industry collaboratively implemented an integrated technological platform for KM, e-Learning, e-Business, and project management disciplines in a higher education environment. This VeBMS platform, consisting of a collaborative working environment within the University of Salento, Italy, was used as a ‘test bed’ to evaluate the validity of the scorecard in practice. The chapter describes how the SNS tool can help in monitoring the evolution of an organizational community, recognizing creative roles and initiatives, and tracing the connections between such initiatives and innovative outcomes. Looking at trends at individual, team, inter-organizational, and organizational levels, researchers identified the most innovative phases within the team’s life cycle using network indicators like density and degree centrality. The SNS provided feedback on the effectiveness of the team and helped discover the phases in which the team acted in a manner conducive to innovation. The Virtual eBMS project team followed the typical structure of an innovative knowledge network where learning networks and innovation networks co-exist with a more sparse interest network. DOI: 10.4018/978-1-60566-701-0.ch017

Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Recognizing Innovation through Social Network Analysis

Introduction Looking at tacit knowledge as the key competitive factor, the discipline of Knowledge Management is going towards a new phase focused not just on knowledge codification (building databases, installing technologies, collecting and disseminating information). In what has been recognized as the “new wave of Knowledge Management” (Cross et al., 2006), managerial efforts are put in exploiting tacit knowledge built in social capital, searching for techniques to foster organizational learning and knowledge creation. Network-based competition requires new types of “business scorecard” for managers to understand the reliance and limitations of resource exchange in social networks. The utilization of network metrics to monitor the evolution of an organizational or inter-organizational network provides advantages for managers in terms of business intelligence and ability to recognize the emergence of innovation. Although managers recognize the importance of fostering collaborative networks inside and across organization’s boundaries, there is still a lack of methods and tools to assess and support links among people, and nurture the most promising relationships that might lead to innovation. In this contribution we propose a Social Network Scorecard applied to an innovative project aimed to design and implement a technological platform integrating Knowledge Management, e-Learning, e-Business and Project Management. The platform - called “Virtual eBMS” - is a collaborative working environment enabling knowledge sharing and learning processes within a higher education institution (eBMS). The e-Business Management Section (eBMS) is a department of Scuola Superiore ISUFI, an advanced education institute of University of Salento, in Italy. In this research we have applied a methodology, that we call Social Network Scorecard, to monitor and discover the hidden phases and individual roles

that are associated to the emergence of innovation. The innovation we are referring to is represented by the components of a technological platform whose success (described later) is based on the ability of a small team to create a high level of connection within and across university boundaries. The main research question leading our study is: How to use a social network scorecard to recognize the emergence of innovation in a project team? To answer this question, we illustrate the case of a team that successfully worked to design an innovative platform, the Virtual eBMS. To understand the hidden dynamics behind this successful collaboration pattern, we used the Network Scorecard as a methodological guideline to monitor the evolution of the individual and group dynamics. The team in charge of the Virtual eBMS project was composed by members of an international business school and a multinational consulting company (hereafter “partner company”) specialized in providing ICT-based solutions. We applied this scorecard to identify the innovation roles and the members’ contribution degree, matching actors’ formal roles within the team and the informal roles emerging from the analysis. The main goal was to identify “in fieri” the network properties able to give suggestions on how to better support the team in the efforts towards the creation of an innovative platform.

Literature Review Within the aim of this chapter, we focus our literature review on three interdependent domains related to innovation management field: the knowledge management systems (KMS), the intellectual capital management (ICM) and the Social Network Analysis (SNA). These three research fields are briefly presented and introduced by highlighting the concept of “relation” as a key success factor for creating value.

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Recognizing Innovation through Social Network Analysis

Knowledge Management Systems The literature on Knowledge Management Systems (KMS) is very rich of multi-disciplinary contributions spacing from strategic perspectives to organizational dimensions, technological issues, human resource development and process-based approaches. A complete and holistic definition of KMS can be the one given by Ronald Maier (2002): “A KMS is an ICT system … that combines and integrates functions for the contextualized handling of explicit and tacit knowledge … A KMS supports networks of knowledge workers in the creation, construction, identification, … distribution, maintenance, searching, … application of knowledge, the aim of which is to support the dynamics of the organizational learning and organization effectiveness…”. From a business perspective, an effective usage of a KMS allows to gain competitive advantage in different areas of the company through better understanding of markets and customers, through more effective design of products and services, through supporting management and change plans (Heisig et al., 2001). In education, KM applications ensure to reach benefits at different processes’ levels: research, curriculum development, students and alumni services, administrative services (Jillinda, Kidwell et al, 2000). In the last few years, KMS attracted the interest of many organizations in order to create new collaborative ICT-enabled environments for sharing and applying knowledge in workplace contexts, both at intra- and inter- organizational level. The strategic importance of these systems derives mainly from the integration of two real observations: the fundamental role of knowledge in innovation processes for competitive dynamics, and the rapid evolution and diffusion of ICT. The integration of these two facts allows the digitization process of knowledge flows, with the consequent rapid exchanges and acquisition of experiences and know-how within and outside the organization. This generates new challenges

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in KMS domain focused on the integration of such systems with the rest of the enterprise digital infrastructure, with a specific focus on Learning Management Systems and Human Resource Management Systems. Moreover, the new waves of web 2.0 and web 3.0 open new perspectives for user-generated contents and experiences, and new tools for interaction and collaboration. Finally, the emerging paradigms of “cloud computing” and “internet of services” promise to revolutionize the models and the approaches to manage knowledge flows and to monitor social relationships among the knowledge workers. This last point highlights a very important topic in KMS concerning the strategies, techniques and tools to monitor effectively the social relationships among people working on the same project, in the same or different organizations, with or without the participation of external experts or other (virtual) communities. Indeed, the knowledge “embedded” in social relationships represents a valuable resource that could be used for exploiting current assets, enhancing the level of usage of specific services or knowledge sources, creating new adhoc “think tanks” focused on specific domains, or for exploring new fields to create future value.

Intellectual Capital Management Since the focus of this research is to propose a business scorecard to help organizations reach their goals, in this section we examine the main contribution given in literature in terms of methods and tools to monitor and assess innovation and knowledge creation (Wenger and Snyder, 2000, Ahuja and Carley, 1999). Knowledge as important asset for innovation is conceptualized in literature using the term “intellectual capital” to refer to the knowledge of a social collectivity, such as an organization, a business community, or a cluster of firms. It is often explained using the following classification: Human Capital (know-how, capabilities, skills); Structural Capital (organizational capabilities, patents, routines,

Recognizing Innovation through Social Network Analysis

systems, databases); Social Capital (connections and network of relationships inside and outside the organization). Although many researchers agree that knowledge is the key strategic asset for creating value in modern organizations, an effective system and set of tools to evaluate and manage effectively and systematically the knowledge flows do not yet exist in a well-structured ad consolidate form, considering both the explicit and the implicit dimension. Over the last ten years, several intellectual capital measurement methodologies have been developed: Balanced Scorecard (Kaplan and Norton, 1992), Skandia Navigator (Edvinsson and Malone, 1997), Intangible Assets Monitor (Sveiby, 2000), Inclusive Value Methodology, IC Rating of Intellectual Capital Sweden, and VAICTM (Value Added Intellectual Coefficient). Most of the traditional methods to measure intellectual capital have been criticized for their static nature. They have the limitations to be made “ex-post”, since they provide a static view of the quality of intellectual capital. They are tools able to offer a photograph of the knowledge assets with focus on human capital (e.g. training per employee, R&D expense/administrative expense, satisfied employee index), and structural capital (patents, licences) without adding any dynamic, temporal perspective. While measuring human capital and structural capital may be less challenging, measuring social capital is more problematic, but still strategically important. Burt (2000) suggests how the social capital concept is “a metaphor about advantage”: the better the social connections between people, the higher the collective and individual returns for them. Given the need to build a system for managing knowledge assets in a dynamic way, we propose an analytical approach based on Social Network Analysis (SNA) defined as “the disciplined inquiry into the patterning of relations among social actors, as well as the patterning of relationships among actors at different levels of analysis” (Breiger, 2004, p. 506). Next paragraph will introduce the benefits

of applying SNA to discover innovation roles and recognize the emergence of innovative phases in the organizations’ dynamics.

Social Network Analysis Given the increased prevalence of group work in organizations today, it is of great value for managers to evaluate the relationship between structural properties of work groups and team performance indicators. To reach this objective, Social Network Analysis can be used to dynamically assess the value created by social interactions. SNA is a set of methods and tools used to identify, visualize, and analyze informal networks within and across organizations. The focus is on the relationships among social actors (individuals, teams, organizations), and on the patterns and implications of these relationships. As is shown by recent studies among several communities of practice across a number of industries (Cross et al., 2006) SNA can be used as a warning system to measure social capital by connecting knowledge seekers and knowledge providers. It might help support and optimize knowledge flows, and discover groups of people collaborating actively to introduce innovations. It allows one to visualize the hidden informal relationships that can facilitate or impede communities’ effectiveness. Cross, Borgatti and Parker (2002) suggest a list of benefits deriving from the adoption of Social Network Analysis: • • • • • • •

Improving a network’s ability to sense and respond to opportunities; Supporting strategic partnerships; Assessing strategy execution; Integrating networks across core processes; Improving innovation; Finding and supporting communities of practice; Ensuring integration post-merger or large scale change.

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Other scholars suggest that the analysis of hidden relationships can be used to recognize the emergence of Collaborative Innovation Networks (COINs), a type of informal networks that form from the interaction of self-motivated individuals who share the same vision (Gloor, 2006). In these networks, people join not for immediate monetary reward, but because they share a common vision, and want to be part of a community that enables innovation. COINs share many similarities with the concept of “virtual networks”, defined as “permeable structures without physical borders of separation from the environment, comprising a multiplicity of autonomous, adaptive, interdependent, and self-organizing actors that rely on the Internet infrastructure to integrate and exchange value” (Romano et al., 2000). The category defined COINs is described by Gloor (2006) as a type of “Collaborative Knowledge Networks” (CKN), a set of relationships among actors that provide a technical and social infrastructure for collaboration and allowing an effective sharing, extension of the tacit and explicit knowledge within and across organizations. The CKN ecosystem is made up of different types of virtual communities: Collaborative Innovation Networks (COINs), Collaborative Learning Networks (CLNs) and Collaborative Interest Networks (CINs). “Innovations originating from COINs are disseminated by a ripple effect transferring knowledge from COIN to CLN, and from CLN to CIN. Discussion of the innovations within the CIN leads to new innovation and the creation of new COINs, resulting in a perpetual Double Helix of CKN” (Gloor, 2006). The network scorecard that we propose in this research is based on the assumption that to recognize the presence of innovative groups or roles, it is possible to use network indicators like the ones we present in Table 1. Our scorecard is based on the main findings of research conducted by Burt (2000), Gloor (2006) and Cummings and Cross (2003), describing the organizational benefits of network indicators to

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recognize innovation roles and group dynamics. As Gloor stated (2006): “The COIN core team has comparatively high density, but low group betweenness centrality (GBC). A CLN or CIN has lower density, but higher GBC, as external members are only connected to core team members, but not among themselves”. Metrics like network density and GBC describe the network as a whole and can be used to track macro-level dynamics. Metrics like degree centrality and betweenness centrality can be used at meso-level to understand the evolution of a community or an organization within an extended network of stakeholders. Applied to the single organization’s level, the same metrics can identify relative positions with reference to partners or competitors. At individual level, the most used metrics for measuring “point centrality” are actor’s degree centrality, betweenness centrality and closeness centrality. This derives from the assumption that the power of individual actors is not an individual attribute, but arises from their relations with others. Centralization and density are important complementary measures, since the concept of density describes the general level of cohesion in a graph, while centralization describes the extent to which this cohesion is organized around particular focal points. In terms of the benefits of these metrics for organizational studies, the use of a metric like individual betweenness centrality allows to identify brokers and boundary spanners. These actors are located on the shortest path between many others in the network and are ideal people to work with when trying to diffuse strategic information (best practice, organizational redesign).

A Social Network Scorecard As Iyer, Lee and Venkatraman (2006) state: “We urge managers to view their company’s network scorecard as the blueprint for navigation in complex dynamic settings that call for intense degree of

Recognizing Innovation through Social Network Analysis

competition and cooperation. It is a scorecard that allows for multiple managers involved in different types of relationships to know how their actions and interactions are constrained or facilitated by other existing and potential ties in the network” (P.18). In this perspective, the application of a social network scorecard would help to recognize when and how virtual networks emerge, following the evolution of key social network indicators. Figure 1 presents our proposed network scorecard that integrates six network metrics into a three-step methodology of analysis (based on sociogram, dynamic view and intensity of interaction). By looking at the most central actors or discovering

the phases in which the intra-group communication is more intense, this scorecard may provide managerial recommendations at individual, group and organizational/inter-organizational level. This scorecard, if compared to some of the network scorecards proposed in literature (Iyer et al., 2006), is peculiar in that a multiple perspective on the network structure is proposed. As a matter of fact, managerial insights can derive from either the static representation of the network (sociogram), or the dynamic evolution of network indicators or the visualization of the intra and inter-group interactions (intensity).

Table 1. Social network metrics and their benefits Level of Analysis

Network Metrics

Description

Main Benefits

Actor Betweenness Centrality

It is the number of times an actor connects pairs of other actors, who otherwise would not be able to reach one another. It measures the extent to which a particular point lies “between” the various other points in the graph (Wasserman and Faust, 1994; Borgatti and Everett, 2006).

• To identify gatekeepers and boundary spanners • To quickly diffuse strategic information such as best practices or organizational changes.

Actor Degree Centrality

It is the total number of other points to which a point is adjacent. It is also defined as the total number of a point’s neighborhood. A point is central if it has a high degree, defined in the interval (0.00-1.00) (Borgatti and Everett, 2006).

• To recognize individual prominence • To help overly burdened community members

Contribution Index

CI is a number that ranges from “-1” to “+1”. A +1 value indicates that somebody only sends messages and does not receive any messages. A –1 value indicates that somebody only receives messages and never sends any messages. A 0 value indicates that somebody sends and receives the same number of messages (Gloor, 2006).

• To evaluate actor’s interactivity level • To recognize coordinators, ambassadors

Group Betweenness Centrality

The proportion of geodesics (shortest path between actors) connecting pairs of non-groups actors that pass through the group. GBC of the entire group is 1 for a perfect star structure, where one central person, the star, dominates the communication. GBC is 0 in a totally democratic structure where everybody displays an identical communication pattern. (Borgatti and Everett, 2006)

• To identify innovative phases within a community life cycle • To avoid centralized, star structures that might impede free knowledge flows

Group Degree Centrality

The number of actors outside the group that are connected to members of the group (Borgatti and Everett, 2006).

• To recognize similarity in the communication patterns among different group members

Density

The total number of relational ties (connections) divided by the total number of possible relational ties (Wasserman and Faust, 1994).

• To evaluate the network’s compactness and the presence of sub-groups

Individual

Group

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For instance, the use of metrics like betweenness centrality helps to identify the information broker that are able to span the organization’s boundaries, helping strategic knowledge to flow from the marketing department to the market and vice versa. As Cross et al. said: “Network analysis can also help reveal loosely connected or isolated members that often represent underutilized resources of a community as their skills, expertise, and unique perspectives are not leveraged effectively” (Cross et al., p.39). In our methodology, a complete network analysis requires three operational steps to be followed: 1. Visualize the sociograms, to get insights from the maps and identify the key actors. By coloring the nodes in the sociograms, it is easy to recognize fragmentation points or peripheral members that might affect an organization’s ability to promote innovation and knowledge transfer. Sociograms are graphs of the inter-relationships within a Figure 1. Social Network Scorecard

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group (i.e., the basic network of friendship patterns and sub-group organization); 2. Visualize the evolution of the network, where the temporal evolution of the team is followed and the trends in groups’ and actors’ centrality is observed; 3. Visualize the intensity of interaction, where the communication patterns are studied sorting by amount of interaction. To support the visual insights with more quantitative evidences, a further step is required: the analysis of individual, team and organizational data referred to indicators like actors’ degree and between-ness centrality and contribution index. The decision to choose six out of all the possible metrics derives from the assumption that network scorecard cannot work for all firms and industry. It is critical to build one that reflects how a particular organization seeks to access knowledge from external and internal stakeholders. For example, Iyer et al. (2006) created a scorecard based on

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five network metrics (average degree of nodes, average degree of partners, path length, clustering coefficient and network density) to measure the topological structure of the software ecosystems. The proposed scorecard is contextualized to the analysis of intra and inter-organizational ties.

The Virtual eBMS Case Virtual eBMS is a project launched in 2004. Its major goal was the creation of a collaborative working environment supporting every time and everywhere learning processes and knowledge flows among learners, mentors, professors, researchers and private/public stakeholders of the School. The project was promoted with the strategic perspective to enlarge the community of stakeholders interested in the School’s education and research activities, and to consolidate the collaboration among them. After the conclusion of the project, new relationships and collaborations emerged between the School and national/ international stakeholders. The virtual environment provided them with the tools and services required to collaborate as a distributed team. The partner for the project was chosen after an open competition at European scale. The entire project lasted two years from the conceptualization phase to the realization of the technological platform, including the effective delivery and usage of services by the end users. The Virtual eBMS system is composed of four complementary and integrated sub-components: a knowledge management suite, a project management tool, a web learning platform and an e-business suite. The knowledge and learning databases of the platform are strongly integrated and they contain several typologies of knowledge resources, learning modules, multimedia assets, case studies, web links and exercises. Today, it constitutes one of the main technological and methodological assets of the School; it enables and supports knowledge sharing and collabora-

tive learning processes that contribute to develop and consolidate the School Intellectual Capital.

Service Architecture of the Virtual eBMS The service architecture of the Virtual eBMS system is composed of four main complementary and integrated sub-components: • • • •

Knowledge Management (KM) suite; Project Management (PM) tool; Web Learning (WL) platform; e-Business (eB) suite.

A transverse layer to the previous mentioned components completes the overall system by ensuring authentication, authorization, system management and monitoring services.

Knowledge Management Suite The main services available into the Knowledge Management Suite are: •

• • •

•

Document Management, to manage the entire life cycle of the documents, including workflow issues; Content Management, to manage news, bookmarks and bulletins; e-Library, to manage e-books, CD-ROMs, DVDs, web seminars; Search and Retrieval, to extract knowledge embedded into documents, news, books, thanks to metadata-based search, text-based search, as well as taxonomiesoriented search and interactive knowledge map navigation; Community (and Collaboration), to support the creation and growth of informal communities for knowledge exchanges, ideas generation. In this context, chat rooms, forums, document exchange areas

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Figure 2. The Virtual eBMS Architecture

•

•

and virtual communication support collaborative activities within the communities; Web Mining, to gather and index new and updated knowledge from external knowledge sources; Recommendation Engine, to suggest knowledge resources according to user profile.

Project Management Tool The Project Management Tool is characterized by two elements that enhance its value: •

•

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the integration with the Document Management service, in order to feed and use the knowledge base through the knowledge generated into the projects; the integration with the Web Learning platform that gives the opportunity to structure project’s activities starting from the competencies of knowledge workers.

The main services offered by the Project Management tool are: •

• • •

Phase and Activity Management, to organize the project and to assign the responsibility to each knowledge worker. Workflow Definition, to organize the life cycle of the deliverables. Reporting, to monitor the status of each phase and activity. Recommendation Engine, to suggest “justin-time” to knowledge workers specific learning patterns according to the competence gap to fulfil.

Web Learning Platform The Web Learning platform allows the organization and delivery of competency development programs. Beyond the traditional way to offer web learning programs (based on SCORM objects catalogue), this sub-component offers learning patterns based on the Problem-Based Learning

Recognizing Innovation through Social Network Analysis

Figure 3. Services and Contents of the platform

strategy. Thanks to this innovative element, in 2006 the Project won the international award Brandon Hall Research - Learning Technology category. The main services offered by the Web Learning platform are: •

•

•

•

Skill Gap Analysis and Competence Profile Management, to map and manage the competence profiles of knowledge workers. Structured Learning Program Management, to offer structured learning programs based on the subscription to a predefined catalogue of on-line learning modules. Unstructured Learning Program Management, to offer learners the opportunity to self-define their own learning patterns through the navigation of the learning base. Tracking and Reporting, to monitor the learning results obtained by the learner.

•

Recommendation Engine, to suggest additional learning modules according to the specific profile of competencies and interests.

Different data sources and typologies of contents feed the whole system, as in Figure 3. The Web Learning platform is also supported by a Multimedia Laboratory composed of a fixed multimedia capture system, a mobile multimedia capture system, an automatic multimedia capture system, a storage multimedia area, post-processing equipments, streaming technologies and advanced editing infrastructure.

e-Business Suite The e-Business suite allows to experiment the benefits of the adoption of ICT into business processes. During the classroom sessions, undergraduate,

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Master’s and PhD students can use the platform to experiment concretely the implications of “eBusiness”, by practicing on the platform. The main services available into the e-Business suite are: Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), Supply Chain Management (SCM), Business Intelligence (BI). Moreover, this suite has been also customized to tourism and agro-industry sectors, to experiment models and processes characterizing respectively a Destination Management System and a Digital Marketplace.

The first two phases (April 2004-July 2004) are monitored in this research and fall into the observation period. The main working groups formally involved into the design and implementation of the Virtual eBMS system were as follows: •

•

Project Duration and Organization The overall duration of the Virtual eBMS project has been just less than 2 years (April 2004-June 2006), including the phase of training end users. The main activities can be classified in six phases: 1. Kick-Off, to define the strategic vision and objectives of the project; 2. Users and Services Analysis, to analyse user profiles’ requirements and needs; users’ scenarios and services’ prototype; graphical design; 3. System Design (services and contents), to perform a functional analysis of services’ requirements; design of the hardware architecture and the software architecture (middleware and implementation choices); Technical Design (use cases, class diagrams) of the KM, PM, WL and eB services; and the design of the Knowledge & Learning Base; Courseware design development; 4. System Implementation, to implement hardware and middleware architectures and KM, PM, WL, eB services; 5. System Test, through Single-based test and Integration test; 6. User deployment, involving training and monitoring.

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•

•

•

•

•

•

Network and System Support group, focused on technical and functional design of the network infrastructure and hardware architecture; Software Architecture group, focused on software architecture design (operating system, middleware and application levels) and definition of the implementation design pattern; Courseware Design and Development group, focused on learning strategy implementation in the on line modules and in the definition of the methodology to create a multimedia on line modules from static knowledge resources used in face-to-face lectures; Knowledge Base design group, focused on taxonomies generation, definition of parameters to evaluate the effectiveness of a taxonomy, definition of processes to update and to use the knowledge base; Software Implementation group, focused on coding the software application, according to the defined implementation guidelines (Software Design and Development group); Knowledge Management group, focused on defining and designing the knowledge management services, with the end-user perspective; Project Management group, focused on defining and designing the project management services, with the end-user perspective; e-Learning group, focused on defining and designing the e-learning services, with the end-user perspective;

Recognizing Innovation through Social Network Analysis

•

e-Business group, focused on defining and designing the e-Business services, with the end-user perspective.

The above mentioned groups played a central role according to the different phases of the project. Figure 4 illustrates the duration of each phase as well as the involvement of the groups during the whole development of the project. For each sub-component a member responsible for the requirements analysis was appointed. Many cross-groups meetings were organized to share the main ideas inspiring the description of the services and to align the overall design of the integrated system.

Social Network Scorecard applied to A Virtual eBMS project To verify the validity of the Social Network Scorecard – described in section 2 –we opted for the team working in the design of the Virtual eBMS. Data collection was realized by automati-

cally accessing seven e-mailboxes of researchers involved in the project for a period of ten months (i.e. March-December 2004). The availability of tracking and mining digital data is an important advantage for social network scientists, since most of the social network studies are still based on questionnaires that are limited by the response rate and by the subjectivity of the answer. To match group activities with network indicators’ evolution, and avoid any possible misinterpretation, we analyzed official project documents and conducted interviews with community members, including Project Managers, other key informants and the members who agreed to share their e-mailboxes in the data collection phase. A focused interview with the Virtual eBMS Project Manager helped contextualize the analysis.

Visual Analysis by Sociograms The visual analysis of sociograms provided evidences of the hidden organizational structure of the team, that over time appeared to be organized around three clusters/informal groups:

Figure 4. Phases and Time scheduling of the Project

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• • •

Cluster 1. Software Design and Development Team Cluster 2. Hardware and Network Team Cluster 3. Client and Supplier Relationship Management Team

The third cluster was an unexpected informal group, not formally recognized in the official documentation (see Visual Analysis by Intensity). The visual analysis of the communication flow indicates that senior members of the partner consulting company hold a peripheral position in the network. This result is consistent with their tasks in the project, since they were financial accounting managers and managers in charge of future contextualization of the platform to specific industries. This group represents a Community of Interest (CIN), mostly acting as receivers of e-mails from the core team, which liaised with stakeholders interested in the further development of the platform. From March to December 2004 Cluster 1 was the most involved and interactive, while the other two were centralized around few key actors and characterized by a mono-directional flow of Figure 5. Static view of three clusters

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communication, from a central hub towards a periphery of internal and external members. Figure 5 shows the communication via email that the whole project team exchanged at the end of the observation period (10 months). A link represents an email sent and received and each node stands for a single actor. The more two nodes are close to each other, the most frequent is the exchange of emails between them. Red nodes are eBMS members, black nodes are members of the partner company and grey nodes represent other external stakeholders interested in the project. Looking at their communication patterns, we recognized three clusters: 1. Software Design and Development. This central cluster is composed of internal and external members jointly working to define the platform architecture. It was constituted of instructional designers, subject matter experts and knowledge mining experts. 2. Hardware and Network, composed of members of the partner companies connected directly to the central actor (eBMS IT Manager). Other members recognized as

Recognizing Innovation through Social Network Analysis

active communicators are: hardware sales manager, network architecture manager and eBMS technicians. In the first ten months of the Virtual eBMS Project this group interacted to understand how the innovative platform would impact the IT systems of the School. 3. Client and Supplier Relationship Management, a group of external observers composed of all the managers responsible for the initial contact between partnering companies and eBMS. This social network is built around the “External Relations Manager” at eBMS, whose role was to nurture relationships with external stakeholders interested in observing the evolution of the platform. Their external role in the design phase – rather than being operative - was typical of an interest network, or steering committee, in charge of visioning future applications for the platform. The results of the visual analysis support the findings of other studies that explored the impact of different network structures on group productivity. Guetzkow and Simon (1955) showed that communication patterns with centralized structures improved the diffusion of information when team were involved in simple tasks. This is exactly what we observed in the case of the Hardware and Network cluster and the Client and Supplier Relationship Management cluster, whose role in this phase is uniquely to nurture interest in the external stakeholders. The communication behaviour of the Software Design and Development cluster is decentralized, and represents a typical example of group patterns described by Shaw (1964) in his systematic review of research on communication structure of teams. The author showed how decentralized communication flows took less time to finish complex tasks than groups with centralized communication flows. At individual level, we recognized specific informal roles emerging from the social network

maps. An interesting insight rising from the analysis of the Hardware and Network cluster is the potential weakness coming from the high centralization of this cluster around one single actor, the eBMS IT Manager. He seemed to have no substitutes in managing the interactions with the hardware suppliers. Indeed, after removing this actor from the network, all the suppliers were disconnected from the network. This result represented a warning element that created awareness in the school’s direction to reduce the occurrence of this potential “single point of failure”. On the other hand, the positive performance of the platform in terms of software and hardware components showed that a “star structure” like this, instead of creating information bottlenecks, supported an efficient way of distributing workload. The IT manager had the typical role of a “boundary spanner”, that is an “individual within a team who acts as the transducer between the team and the rest of the firm (organization) during the exchange of information that take place during the process of innovation” (Afuah and Tucci, 2003). The most central actor in the Software Design and Development cluster was the Virtual eBMS Project Manager, whose role was to facilitate a fluid and constant information flow within the core team. He was in charge of coordinating an interdisciplinary group, composed of internal and external members, whose tasks were complex and required more information processing compared with the simple, routine tasks characterizing the other two clusters.

Dynamic Analysis The visual dynamic approach allows to find periods of high and low collaboration within a community’s life cycle. By observing the trends in terms of low and high group between-ness centrality (GBC), we identified few periods of potential high productivity and information dissemination. In a social network perspective, the group betweenness centrality of the entire group

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is 1 for a perfect star structure, where one central person dominates the communication. GBC is 0 in a completely democratic structure where every actor has an identical communication pattern. Figure 6 shows the evolution of Group Betweenness Centrality (red line), Group Degree Centrality (blue line), and Density (green line) from March to December 2004. The periods of intense communication and high activity are recognizable by slopes in the evolution of group between-ness centrality and high-density structure of the community (Gloor, 2006). A macro-level analysis suggests that, after the innovation seminar, the group between-ness centrality dropped down to less than 0.60, with a minimum value of 0.20 and an average value of 0.45. While in the initial four months the high centralized structure (average GBC 0.68) was explained by a high number of one-way seminars (training phase), after the Innovation Management Workshop the average GBC was steadily equal to 0.55 (for 6 months). During the Design Phase, several strategic meetings and workshops took

place. To design the main features of the platform, as well as the customization of the solutions proposed by the partner company, in the period June-July internal and external project members were involved in frequent meetings to discuss Design of the Content Management System, OpenCMS and NetOffice, Innovation Seminar, e-Business suite workshop. From the dynamic view we noticed a very intense period of information sharing during a two-day seminar called “Innovation Workshop”. It was organized as a modular brainstorming session to facilitate cross-fertilization of knowledge and experiences. It was targeted to members of eBMS (14 participants) and the partner company (12 participants) including: project managers, subject matter experts, e-learning manager, IT managers, software engineers, instructional designers, technicians. Its mission was to create a shared knowledge about the new approaches to Knowledge Management, e-Learning and Project Management, searching for a common definition of new ways for integrating them in the new

Figure 6. Evolution of group level indicators over 10 months

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technological platform. An operative outcome of this phase was the delivery of the “Design Documents”, a point of reference for the knowledge base classification, and for the e-Learning modules to be loaded in the innovative platform. During the Innovation Workshop there was an increased level of collaboration between members of the Software Design and Development and external partners involved in the project. Whereas the other two clusters are characterized by a centralized structure (see Figure 7), the first cluster had a low centralization and a good level of density and cohesion between eBMS members and the 14 external partners. Some interesting results emerged at a mesolevel analysis. To see the reliance of the network, we removed the three nodes with the highest between-ness centrality, who were also coordinators of cluster 1, 2 and 3 (see Figure 8). The resulting map represents a snapshot of the communication ties in June-July where 15 members of the partner company (black nodes) are still actively interacting with the community’s members. Taking out the hubs of clusters 2 and 3, the external stakeholders resulted totally dis-

connected from the core team. This result suggests that: •

•

•

The Virtual eBMS project manager is the most central actor without being a bottleneck; The whole community has developed internal mechanisms able to make the communication network self-sustainable; The disconnection of cluster 2 and 3 from cluster 1 might represent a “structural hole” when the inclusion of external actors or IT decision makers is required.

Visual Analysis by Intensity (Adjacency Matrix) To look for more insights and confirm the ones emerging from the map and dynamic views, we observed the information flows through the value adjacency matrix created using Netgraph, a matrix generator embedded into the software suite TeCFlow (Gloor, 2006). Each cell in Figure 9 represents an information exchange between actors grouped by organization. The more intense the cell colour, the higher the number of emails

Figure 7. Focus on the period of the “Innovation Workshop” (June-July)

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Figure 8. The three clusters without the most central actors

exchanged between two actors. Quadrants A1 and A2 highlight the e-mails sent and received by members of the same organization, while quadrants B1 and B2 indicate the communication within the two organizations, eBMS and the partner company. We observed a dense collaboration in the quadrant A2, as well as an intense communication from the Virtual eBMS Project Manager towards external and internal members (first row/ actor in quadrants B2 and A2). This confirms the role of the Virtual eBMS project manager acting as “cluster 1 coordinator”, facilitator of information dissemination within the team and boundary spanner towards external community members.

Using Indicators to Support Visual Insights In this paragraph, we re-consider the insights described in the previous sections through the lens of selected indicators. Figure 11 shows the most active members interacting in the ten-month period, divided by cluster. The three clusters were composed by mixed groups in terms of knowledge, skills and competences. In particular, the core team in cluster 1 was composed by knowledge experts in business management, IT management, software developers, instructional designers, IT architect. This represents one of the key factor

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to build an innovative project depending on the participation of a variety of talented people across the whole process. As shown by Rulke and Galaskiewitz (2000), decentralized groups composed mostly of specialists and mixed background – such as in the Virtual eBMS team– outperform centralized groups composed by members with similar background. Watching the movies that visualize over time the communication dynamics of the Virtual eBMS community, we noticed how internal and external members filled different informal roles over 10 months. By recognizing the roles of individuals in virtual communities, we can answer questions like: Who are the experts within the team?, Who are the innovators, collaborators, and communicators? We also noticed a trend in the communication behaviour of the eBMS project manager. Starting in a position of receiver and collector of documents in month 1, he assumed at the end of the period a more active role of coordinator before the launch of the implementation phase. Adopting a classification introduced Gloor (2006), and based on a large number of innovation networks, we attribute the eBMS project manager a network role of collaborator or expediter. This role has been to plan, allocate, assign, schedule and assure completion of the innovation project,

Recognizing Innovation through Social Network Analysis

Figure 9. Matrix view of the communication in the community

Figure 10. Classification of the most central actors sorted by cluster

going beyond the traditional program manager’s tasks. This emerges from his contribution index (high frequency of interaction and average value equal to 0) which means good ability to facilitate knowledge exchange that is at the basis of the innovation process. Figure 9 shows the position of the most active members in the Contribution Index

Plot (red dots are eBMS members, black dots are actors from the partner company). The majority of the community members have a contribution index within the interval [-0.8, +0.2], which is a sign of balanced communication and free flow of information (Gloor, 2006).

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Figure 11. Contribution Index for different project members: x-axis: frequency of communication, y-axis (contribution index by Gloor, 2006)

At the end of ten months, the aggregate value of contribution index indicates the emergence of the following “informal roles” (Figure 11): •

• •

•

Collaborator: only the eBMS Project Manager holds this role and is in charge of organizing and coordinating tasks; Innovator or creator: eBMS IT architect/ software engineer; Gatekeepers and boundary spanners (six actors), among which the Project Manager of the partner company; More than fifteen knowledge experts, who serve as ultimate source of explicit knowledge. Among them most of the instructional designers whose role was to propose the methodology for preparing and delivering e-learning courses.

Discussion of results The results of our analysis suggest evidences that a social network scorecard has the validity to match knowledge management initiatives with

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innovation outcomes. The analysis allowed the identification of the most innovative phases within the community’s life cycle. Within the phase of high connectivity and intense collaboration, we recognized in the Virtual eBMS project team the typical topography of an “innovative collaborative knowledge network” (Gloor, 2006), where learning networks and innovation networks (core and extended teams) co-exist with a more peripheral interest network. The analysis allowed the identification of the most innovative or productive phases within the team’s life cycle. Within the phases of high connectivity and intense collaboration, we recognized in the Virtual eBMS project team the typical topography of an “innovative collaborative knowledge network” where learning networks and innovation networks (core and extended teams) co-exist with a more peripheral interest network (Gloor, 2006). During the two-year project, many critical situations happened, but the awareness to design and implement an innovative platform that could revolutionize the global education industry motivated all the groups, the two main organizations involved in the project and the stakeholders

Recognizing Innovation through Social Network Analysis

interested in the contextualization of the platform. This strong belief has been demonstrated by the list of results briefly described below:

an international corporate master’s program realized by an international industrial group and some executive programs.

All the deliverables were positively accepted during the several auditing phases created by external international experts; The innovative elements characterizing the methodological assets and the technological solution have been published in international proceedings conferences and scientific journals; The network of relationships developed during the project have been transformed into stable partnerships of the School oriented to promote and realize new projects; Two prestigious awards have been obtained; an international one, received in 2006 in Denver, Colorado (USA), by the “Brandon Hall Research” in the category “Learning Technology”, for the creation of an innovative web learning platform which embeds the problem-based learning approach and a national one, received in Italy, for the “excellent” evaluation recognized to the knowledge management component, obtained within the National Operative Program for Research (PON Ricerca) 2000-2006. The realized system now constitutes the basic technological and scientific platform for a multi-year research program specialized in innovative management of tourism industry, agro-industry supply chain and collaborative product design in aerospace sector; The growth of the internal team involved in this project, that has been institutionalized into a new Laboratory. The overall platform supports the research and educational programs of the School, also those realized in partnership with external stakeholders. Indeed, a subset of services has been customized to support

Even the evidences of possible weaknesses of the network turned to be effective structural properties, like the star structure shown by the IT support cluster. What we found is consistent with the results found by Shaw (1964) according to whom “communication nets” with centralized structures improve the diffusion of information in simple tasks while decentralized structures delay the diffusion of information. The same analysis illustrated how groups with decentralized communication nets take less time to finish complex tasks than groups with centralized communication nets.

•

•

•

•

•

•

•

Conclusion Assessing and measuring the success of a community or project team can be a real challenge. Scholars have proposed several ways to assess its performance: evaluate the tangible outcome of the community in financial terms; ask external experts to rate the performance of the community; find other objective measures of community success such as the speed of innovation diffusion, or the time and degree of acceptance of the innovation proposed by the community. It is even more challenging to identify what went wrong during the innovation process, if managers do not have a sort of scorecard suggesting who is holding roles of informal leadership, who is acting as “gatekeeper”, who represents the knowledge repository. Once a community has developed a tangible product and its value is measured, it is important to recognize which factors helped reach those objectives. By using the Social Network Scorecard as methodological guide, we observed that in the Virtual eBMS project team innovation roles have naturally emerged beyond management’s expectations (e.g. coordinator, knowledge expert, boundary spanner). The Scorecard acted as a sort

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of toolkit to discover the hidden relations. We can summarize in the following dimensions the main success factors of the innovative team working to design the Virtual eBMS: • • •

• • •

De-centralized communication structure; Frequent exchange of information within the operative team; Community structure based on three clusters: core/operative team, support/technical team, interest team; Interdisciplinary community’s composition; Balanced presence of different informal roles (gatekeepers, SMEs, coordinators); Organization of face-to-face workshops to speed up the innovation process.

The Scorecard and the related concepts of SNA have been useful to assess the strategy execution during the first ten month of the project, stressing individual roles and phases associated with very collaborative patterns. It also helped to strengthen the relationship between the two organizations, eBMS and the partner company, providing feedback to their strategic partnerships that is still continuing. The findings of this study are limited to structures formed by email communication. It is possible that communication network formed by media other than email is substantially different, even though we assume that email communication is a good approximation of social ties in the case of virtual communities. It remains to be seen whether other media or even a more structured electronic environment will render similar results. Another limitation of this study is that we did not have access to the content of the email exchanges; thus we could not add more meaning to the interactions occurred. We believe that this limitation – typical of the Social Network Analysis approach - can be overcome with a strong contextualization of the analysis and – if possible – with a Content Analysis applied to digital records revealing the meaning/quality of the communication.

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REFERENCES Afuah, A., & Tucci, C. L. (2003). Internet business models and strategies: Text and cases. New York: Mac Graw Hill. Ahuja, M. K., & Carley, K. M. (1999). Network Sstructure in virtual organizations. Organization Science, 10(6), 741–757. doi:10.1287/ orsc.10.6.741 Breiger, R. L. (2004). The analysis of social networks. In Hardy, M., & Bryman, A. (Eds.), Handbook of Data Analysis (pp. 505–526). London: Sage Publications. Burt, R. S. (2000). The network structure of social capital. In Sutton, R. I., & Staw, B. M. (Eds.), Research in Organizational Behavior (pp. 345–423). Greenwich, CT: JAI Press. Cross, R., Borgatti, S. P., & Parker, A. (2002). Making invisible work visible: Using social network analysis to support strategic collaboration. California Management Review, 44(2), 25–46. Cross, R., Laseter, T., Parker, A., & Velasquez, G. (2006). Using social network analysis to improve communities of practice. California Management Review, 49(1), 32–60. Cummings, J. N., & Cross, R. (2003). Structural properties of work groups and their consequences for performance. Social Networks, 25, 197–210. doi:10.1016/S0378-8733(02)00049-7 Edvinsson, L., & Malone, M. S. (1997). Intellectual capital: The proven way to establish your company’s real value by measuring its hidden values. London: Piatkus. Gloor, P. A. (2006). Swarm creativity: Competitive advantage through collaborative innovation networks. New York: Oxford University Press. Guetzkow, H., & Simon, H. (1955). The impact of certain communication nets upon organization and performance in task-oriented groups. Management Science, 1, 233–250. doi:10.1287/mnsc.1.3-4.233

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Heisig, P., Mertins, K., & Vorbeck, J. (2001). Knowledge management: Best practices in Europe. City: Springer. Huber, G. P. (2001). Transfer of knowledge in knowledge management systems: Unexplored issues and suggestions. European Journal of Information Systems, 10(2), 72–79. doi:10.1057/ palgrave.ejis.3000399 Iyer, B., Lee, C. H., & Venkatraman, N. (2006). Managing in a small world ecosystem: Some lessons from the software sector. California Management Review, 48(3), 28–47. Jillinda, J., Kidwell, K., Vander Linde, M., & Johnson, S. L. (2000). Applying corporate knowledge management practices in higher education. EDUCAUSE Quarterly, 4, 28–33. Kaplan, R. S., & Norton, D. P. (1992). The balanced scorecard measures that drive performance. Harvard Business Review, 70(1), 71–79. Romano, A., Passiante, G., & Elia, V. (2000). Modelling growth clusters in the new Web Economy. Proceedings 45th International Conference on Small Business (ICSB) World Conference, Brisbane, Australia.

Rulke, D. L., & Galaskiewicz, J. (2000). Distribution of knowledge, group network structure, and group performance. Management Science, 46(5), 612–625. doi:10.1287/mnsc.46.5.612.12052 Shaw, M. (1964). Communication networks. In Berkowitz, L. (Ed.), Advances in Experimental Social Psychology (pp. 111–147). New York: Academic Press. doi:10.1016/S0065-2601(08)600507 Storey, J., & Barnett, E. (2000). Knowledge management initiatives: Learning from failure. Journal of Knowledge Management, 4(2), 145–156. doi:10.1108/13673270010372279 Sveiby, K. E. (2000). Measuring intangibles and intellectual capital. In Morey, D., Maybury, M., & Thuraisingham, B. (Eds.), Knowledge management: Classic and contemporary works. Cambridge, MA: The MIT Press. Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. Cambridge: Cambridge University Press. Wenger, E. C., & Snyder, W. M. (2000). Communities of practice: The organizational frontier. Harvard Business Review, 78(1), 139–145.

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Chapter 18

Complexity and Clarity:

The Knowledge Strategy Dilemma – Some Help from MaKE Peter Sharp Regents Business School London, UK Alan Eardley Staffordshire University, UK Hanifa Shah Staffordshire University, UK

ABSTRACT Organisations face a great problem. How can they create a knowledge management (KM) strategy that takes account of the complexity of knowledge issues in their organisation and be able to clearly communicate it? This issue, called here the Knowledge Strategy Dilemma, is the main theme of this chapter and is vital for KM success in practice. The authors argue that literature reveals that the Dilemma is one that can be tackled. They also argue that whilst the literature reveals approaches that help address different parts of the Dilemma, the best approach to address it in a coherent way is a KM method called MaKE. MaKE is presented and two of its principles—Traceability and Transparency— are explained. Also visual tools that help implement these principles in practice are critically discussed along with feedback from industry. The principles, when applied, are helpful in tackling the Dilemma with some success. Also, the authors argue that different forms of communication (including face-to-face meetings with visual aids) should be used to address the Dilemma. The question that remains is: are organisations willing to devote the time to do these things in practice?

INTRODUCTION Organisations face a great problem. How can they create a knowledge management (KM) strategy that takes account of the complexity of knowledge DOI: 10.4018/978-1-60566-701-0.ch018

issues in their organisation and also be able to clearly communicate it? This issue, called here the Knowledge Strategy Dilemma (the Dilemma), is the main theme of this chapter. The problem is discussed in the context of literature in the field and research that relates to the KM method called MaKE (Sharp, 2006a).The authors argue

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Complexity and Clarity

that while the Dilemma is an ongoing one and may seem insurmountable on first appearance, there are some principles that, when applied, help address the Dilemma. This chapter presents and critically discusses two KM concepts from MaKE that have implemented these principles in practice. Feedback on these concepts is critically analysed before a conclusion is presented.

KNOWLEDGE STRATEGY DILEMMA: COMPLEXITY AND COMMUNICATION There is no doubt that the area of KM and its application to organisations is littered with complexity. The concept of ‘knowledge’ is enough to generate a plethora of different definitions (Sharp, 2006b) and an enormous range of different concepts and approaches (Sharp, 2003). There are a wide range of different viewpoints on the subject which has led some to argue that the field of KM is a bankrupt in the very disciplines (e.g. Information Systems) in which it was originally applied (Galliers & Newell, 2001). Others have argued that the area of KM related to how people think and learn, is so intangible that it is beyond definition. Polanyi (1967) wrote a lot about the subject of personal knowledge at the human-oriented end of the spectrum, but in doing so, provided no definition of knowledge for his readers. This may have been because he believed that by defining it, he may oversimplify the concept and support an unsophisticated approach. The issues relating to tacit knowledge, its meaning, and the whole notion of what can be made explicit, raises dilemmas in KM about implementation and there have been calls for a reconsideration of KM methods in organisations (Vasconcelos, 2008). Although the issue of defining knowledge is seen as a stumbling block by some, the majority of people working in the field have moved on from that issue by either not defining it or discussing it in the context of different concepts and practices

they write about. In doing so, authors have used many different approaches and have created a wide variety of KM concepts (e.g. Wiig, 1993; Machlup, 1980; Bontis, 2000; Malhotra, 2000; Stewart, 2002; Tobin & Snyman, 2008). The KM frameworks that exist range considerably. Some focus on a particular aspect of organisations while others take a more comprehensive view of knowledge in organisations (Holsapple & Joshi, 1999). Some frameworks emphasise the process of knowledge creation and innovation (e.g. Nonaka & Takeuchi, 1999) whereas others focus on knowledge as a resource to be evaluated (e.g. Sveiby, 1997; Edvinsson & Malone, 1997). Some authors argue that complexity itself should be embodied in KM frameworks and that management is best done by creating organisational environments in which these complex processes take place (Snowden, 2002). Others adopt a very different argument. They suggest that by creating unified generic frameworks that synthesise the thinking in the domain, organisations can categorise, process and evaluate their knowledge and intangible assets in a more structured way (e.g. Holsapple & Joshi, 2001a and b). One could argue that the range and diversity of frameworks can cause confusion in the practical application of KM in organisations (Sharp, 2006b). If two or more different approaches are used in a company then employees may question what KM means. This may lead to a lack of trust in KM projects especially if different approaches are used over a period of time (Patrick et al., 2000). If this happens, the ability to implement a KM initiative successfully is severely undermined as employees may cease to engage in it (Kelly, 2007). Also, there is the challenge of sharing potentially complex issues relating to knowledge and learning with employees whose assumptions and levels of understanding may vary enormously (Senge et al., 1995; Checkland & Scholes, 2000). This may mean that a considerable amount of advice and help from a KM expert may be required to guide, train and select suitable KM approaches

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to adopt within the organisation. Also, employees may tackle issues of knowledge from different epistemological perspectives (Sharp, 2003) or be strongly influenced by the different work contexts, and cultural, political and linguistic factors (Kuznetsov & Yakavenka, 2005; Ardichvili et al., 2006; Checkland & Scholes, 2000). This can lead some employees to choose their own version of the knowledge concept for their own ends and others to question the value of carrying out such initiatives at all. Although this may be the case in some organisations, the authors argue that these arguments present a rather negative picture about the ability to conduct KM in organisations with any success. The authors argue that those who suggest KM is a passing fad or that it is a waste of time, are presenting a picture that is one-sided and potentially harmful to organisations. Although it is healthy to bear them in mind so that so that KM initiatives in organisations are scoped realistically, this should not prevent practitioners from drawing on KM work of the last twenty years. The arguments about the value of intangible assets in society (Leonard Barton 1995; Stewart, 1997; Sveiby 1997; Teece 1998) and the examples and evidence (e.g. Nonaka and Takeuchi, 1995; Petrash, 1996; Stewart 2002; Kazi and Wolf 2006) should not be ignored. This is because organisations that do not carry out KM initiatives risk missing out on the competitive advantage they would otherwise gain (Stewart 2002). Alternatively they may MaKE catastrophic mistakes of those who ignored the significance of the knowledge of their employees in the past (Sveiby, 1997; Larson and Myers 1999). Also, arguably KM initiatives are particularly important when intangible assets comprise such a significant proportion of business value and that this is increasingly recognised by organisations themselves (Machlup, 1980; Seely Brown 2001; Stewart 2002). To add to this, initiatives have been taken that suggest that in very real and practical ways organisations have been helped in KM and that the goal of addressing the Dilemma is a valuable one for organisations. Arguably, the

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Dilemma has been addressed already either in part or as a whole. To help illustrate this, the Dilemma can be broken down in to a number of challenges. These challenges can be summarised in a series of questions that an organisation may pose. If all of the challenges can be addressed practically (both individually and in combination) then the Dilemma no longer provides such an obstacle in KM practice. The questions are: 1. How can an organisation define knowledge in a way that is suitable to KM practice and bring consensus rather than confusion and distrust? 2. How can the diverse complex range of KM concepts be used to create a KM strategy for the organisation involving employees as participants? 3. How can the organisation bridge different contexts and cultures when it forms the KM strategy? 4. How can the organisation clearly communicate its KM strategy without oversimplifying it? Question 1 may be addressed in a number of ways. Some authors argue that their particular definition of knowledge should be used as a starting point irrespective of other views (e.g. Davenport and Prusak 1996). Although this is convenient, this is not a consensual approach and may lead to distrust within the organisation. Another approach is the concept MaKE First Steps (Sharp 2006b) which is a component of MaKE (see Section 4 below). This process embraces the range of definitions of knowledge. Instead of seeing the range as a problem, it uses it for its advantage by involving employees in teamwork to collaboratively define knowledge for their own organisational context (Sharp 2006b). By doing this, it brings consensus among participants and builds trust (Sharp 2008) and can help address concerns employees may have about hypocritical management practice (Sveiby 2007). Both

Complexity and Clarity

of the approaches to the first challenge help, in part, to address the second part of the Dilemma. A definition of knowledge can form a common base of understanding among employees. It can also be a starting point for managing knowledge and creating a KM strategy within the organisation (Sharp 2006a and b) (see Section 4). However, decisions must at this stage be made about whether to adopt one wholesale approach (Sharp 2006a) or to use a range of different KM tools from this point. In either case, there is a need for careful understanding and use of KM concepts from this point. The third challenge is discussed in KM and general management literature extensively. As with the other challenges, suggesting cast iron solutions is dangerous. However, approaches like the Soft Systems Methodology (Checkland and Scholes 2000) which acknowledge the existence of different cultures, contexts and stakeholder interests, can be used to articulate problems. Such approaches can be used to start building bridges in the organisation between employees. A very different approach which acknowledges different contexts in the organisation and can help develop its strategy is the Balanced Scorecard (Kaplan and Norton 1996). However, instead of producing rich pictures and versions of different viewpoints, it produces metrics that are selected and prioritised by participants from different parts of the organisation. However, neither of these approaches takes an explicitly KM approach. However, MaKE (see section 4) is an option which does and includes characteristics of both of the above concepts (Sharp 2003). Finally, there is the fourth challenge. Literature from a wide variety of domains relating to communication of concepts clearly suggests that the use of different techniques in combination (e.g. literature based, intranet, pictures, audio and face-to-face), particularly the use of selected/ devised pictures and face-to-face communication, helps clarify transmission of messages (e.g. Miles and Huberman, 1984; Senge et al. 1995; Swan et al. 1999; Checkland and Scholes 2000,

Sharp et al. 2003). However, the issue of how to do this without compromising the complexity is rarely discussed. In fact, some have alluded to the problem of trying to summarise organisational situations and in the process, losing the richness and complexity (Remenyi et al. 2002). Arguably the Balanced Scorecard addresses this to some extent. However, this does so without an explicit KM lens and it is a primarily ‘top down’ approach (Sharp 2003). Within the context of KM, the principles of Traceability and Transparency are suggested as a way forward in this area within the more structured approach of MaKE (see section 5). Pointers as to how different aspects of the Dilemma may be addressed in a KM initiative in an organisation are discussed above. However, arguably to address them in combination there is a need to adopt a wholesale approach that brings together a KM strategy in a coherent way. MaKE does this. It uses the knowledge definition issue as a starting point (Sharp 2003). The remainder of this chapter gives a summary of a context in which MaKE was applied and the research method (action research) used. Then, the concept of MaKE itself is summarised to provide a basis for explaining the principles of Traceability and Transparency. Two KM tools (Knowledge Targets Pyramid and Knowledge Blocks) designed to implement these principles are explained followed by critical analysis of feedback on the implementation of these innovations.

ACTION RESEARCH AND CONTEXT OF APPLICATION MaKE was developed, applied and validated by an adapted form of action research. The company in which this took place is a major UK Fast Moving Consumer Goods (FMCG) manufacturer and distributor and has several brands within the 20 top-selling grocery brands in the UK and holds major UK franchises. A piece of KM software was implemented in the company and a need to

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explore a human-based KM approach was identified. MaKE was developed, applied and tested in a series of five workshops and 13 questionnairebased interviews. At each workshop there were between five and seven participants and at the interviews one employee and the author were present. The questionnaires used for each component embodied key criteria to test, validate and develop the different components. The criteria for the components varied depending on the design of each component, what had been agreed in advance of implementation, and practical limitations to the research like the number of times and the environment in which each component would be tested. The feedback that was acquired came in four different forms: formal, informal direct, indirect, and the author’s reflections in and on practice. The formal category refers to written or tape-recorded

Figure 1. Overview of MaKE

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feedback in questionnaires and questionnaire interviews. The informal direct feedback refers to communication of participants directly with the author of MaKE (Sharp 2003) about the design. The indirect category consists of feedback from other employees in the organisation not present when the component was applied.

THE MaKE METHODOLOGY Figure 1 illustrates MaKE that was developed from an earlier prototype called SolSkeme (Sharp 2003). MaKE has three major components applied in sequence. It begins with MaKE First Steps. The outcome of this component is a definition of knowledge for the context to which the rest of MaKE is applied. This definition is used as a

Complexity and Clarity

starting point of MaKE Direct and MaKE Executive. MaKE Direct is conducted with employees to elicit ‘Knowledge Requirements’ and the means by which the Knowledge Requirements may be achieved (Sharp, 2002). In theory MaKE Direct can be conducted any number of times and this is represented by the ‘MaKE Direct n’ ellipse in Figure 1. The Knowledge Requirements are ranked in a ranking table and pooled within the MaKE Executive component and Knowledge Targets and their associated Knowledge Blocks are generated and represented (see the following section). Once this is completed MaKE Measures is implemented to marry up appropriate measures with knowledge manipulation activities in the Knowledge Blocks. A Linking Overview can also be generated (Sharp and Hanlon 2004). In Figure 1 there is a dotted line from MaKE Measures to MaKE First Steps because MaKE is designed to be used repeatedly at periodic intervals. This can be done to see whether Knowledge Targets have been achieved and knowledge manipulation activities have been effectively carried out. The latter can be gauged by reference to measures and indicators in the Knowledge Blocks. When MaKE is applied in a new cycle new Knowledge Targets may be articulated as previous Knowledge Targets are achieved and the organisations’ circumstances change. MaKE is a process that produces a number of outcomes. MaKE uses visual tools to assist in the implementation of the process and illustration of the outcomes. The visual tools provide frameworks for collating, categorising and structuring information. Some of the visual tools have more than one of these uses but generally they are designed to help communicate the KM strategy to employees and implement the principles of Traceability and Transparency.

TRANSPARENCY AND TRACEABILITY PRINCIPLES The Transparency principle is: wherever possible an organisation should record and MaKE explicit the information and reasoning used to develop its KM strategy. The Traceability principle is: wherever possible an organisation should devise a KM strategy that logically traces back to its origins (i.e. the final summarised version can be traced back to the starting points of the process). There is logic behind the adoption of these principles in organisations. When employees can trace the basis upon which the KM strategy rests they can be satisfied that the organisation has formed it in a justifiable way. They can also understand their role in the formation of the KM strategy. Therefore, through the development and communication of the strategy consensus is formed, trust is built up and barriers to communication and involvement of employees are broken down. Figure 2 illustrates an overview of the implementation of these principles in MaKE. Level A represents the feedback obtained from participants in the implementation of MaKE Direct with employees. It forms the basis for developing the KM strategy that is honed as the Levels B to G are worked through. The final outputs which summarise the KM strategy (Knowledge Targets Pyramid and Knowledge Blocks) produced can be traced back to what is recorded from the starting points of the process. Therefore it embodies the Traceability and Transparency process in its design.

KNOWLEDGE TARGETS PYRAMID AND KNOWLEDGE BLOCKS The Knowledge Targets Pyramid and Knowledge Blocks are visual tools used to help illustrate and

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Figure 2. Traceability and transparency of MaKE

communicate the KM strategy that is developed. Figure 3 illustrates the design of the Knowledge Targets Pyramid. It shows how the Knowledge Targets (the things that the organisation should aim to do in relation to their KM strategy) have been determined in the last few stages of the MaKE process. The Knowledge Targets are prioritised and given a weighting. The principles of Traceability and Transparency are embodied in this visual tool because it illustrates the basis from which the targets were generated. It is a summary of KM strategy targets and is designed to fit on one sheet of paper. The details of how to achieve the Knowledge Targets and measures to monitor how successful the change processes are in achieving them, are summarised in Knowledge Blocks. An example of a Knowledge Block is shown in Figure 4 (see overleaf). It provides an insight into

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a significant level of management detail that may be created and recorded in the block to achieve the Knowledge Target and monitor progress. Again, this KM tool reinforces the implementation of the Transparency and Traceability principles and brings a very practical edge to the implementation of MaKE.

DISCUSSION AND ANALYSIS OF FEEDBACK Feedback was obtained on the MaKE Executive component from a high level strategic thinker (Employee A) and participants of workshops described in Section 3. Half a day was taken in the implementation of the MaKE Executive component with Employee A. This included the use of visual tools. There were only two questions

Complexity and Clarity

Figure 3. Knowledge targets pyramid (example profile)

in the questionnaire which relate broadly to the topic in question in this chapter. However, more importantly there was a discussion around these questions that did relate specifically to the issues. This feedback on the process was recorded on tape and transcribed immediately after the meeting. The Likert-Boolean responses from Employee A are summarised in Table 1. The transcription was longer than for any of the other questionnaire interviews. There are several reasons for this. Employee A did not endorse both the key criteria and the author of MaKE (the Interviewer) asked more follow-up questions

and deliberately explored outstanding issues more deeply. The Interviewer thought it was reasonable to ask Employee A questions about changes he had recommended from the first cycle of action research that he now thought were not sufficient to endorse the outstanding key criteria in this cycle. Also, it was an opportunity to look back on the whole piece of work with the person who, apart from the Interviewer, had more practical experience of MaKE in its entirety than anyone else. Employee A wanted to talk about it in depth and pursue outstanding issues further, and this

Table 1. Summary of Feedback on MaKE Executive at the End of the Action Research Question

Likert Scale

* originally named SolSkeme

Undecided

Strongly Disagree

Disagree

1. [MaKE]* Executive is useful in helping to determine information systems development strategy 2. MaKE Executive is easy for me to understand.

Agree

Strongly Agree 1

1

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Table 2. Example Knowledge Block (Please note: the sub-categories and details in the Knowledge Resources and Environmental Influences categories have been omitted from this example) Knowledge Target: IMPROVE AWARENESS OF POTENTIALLY USEFUL INFORMATION / KNOWLEDGE 1. Finding relevant databases [example] 2. Finding relevant work people have done 3. How people’s knowledge can help others do their jobs 4. Data that brand managers have that could be used to help channel marketing [example] Category

Sub category

Resource, Activity or Influence

Measure/Indicator

Knowledge Resources Manipulation Activities

Acquiring Selection Generation Create more proactive “MaKE aware” culture by: - a publicist taking [example] research to others in person on a periodic basis [every day? every week?] as well as by e-mail (5 - 1) - introducing an induction programme that introduces personnel more fully to: - the roles of others in the company - the uses and understanding of databases and has an induction element that kicks in after 3 months has elapsed so new employees can ask more pointed questions relevant to their work (5 - 2)

YN YN

Other

• Brand managers and [other personnel] to conduct regular meetings (once every 8 weeks) to transfer knowledge on latest findings [example] (5 - 3) • Conduct a presentation dependant on data sources and what questions brands use it to answer (5 - 4) • Update presentation and or send messages at six monthly interviews to inform on relevant changes to sources and questions referred to above (5 - 5)

YN YN YN

Leadership

Support awareness initiatives including: • publicist going to relevant people in person in particular taking [example] work to other relevant departments on a regular basis (5 - 6) • devise better induction programme to train and introduce sources e.g. how to use databases to best effect (see above) • increase investment in database training relevant to individuals’ job role (see above) • support relevant computing systems [example] • introduce effective new search tool [example] (5 - 7) • brand and [other personnel] to arrange and ensure the regular 8 week meetings are held (5 - 8) • encourage / tell people in departments to regularly communicate with individuals in other half of the brand / sales function and lead by example

YN YN YN (Increase S9: databases) YN YN YN -

Co-ordination

Increase engagement of employees in knowledge awareness initiatives through • introduction and use of KM method (5 - 9) • greater number of social networking events across the company (5 - 10)

YN YN (increase number and diversity)

Internalisation

Externalisation

Management Activities / Influences

Control Measurement Other

continued on following page

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Table 2. continued Environmental Influences Is it too difficult or costly?

Please tick

If so, please specify the resource(s) activities, or influences

to obtain the knowledge resources; to carry out the manipulation activities; to carry out the management activities; because of environmental influences Key and Notes (x - y) References that can be used in related visual tools and reports YN Yes / No indicator [notes are provided in square brackets]

was the last one-to-one feedback meeting scheduled in the project. Employee A answered question 1 positively, possibly because he had had time to reflect on the strategic ramifications of the work and see the links with the KM information system in the organisation, something he said in Cycle 1 he would need to do. It may be that Employee A also changed his mind because of the introduction of Knowledge Blocks. Either way, this feedback, along with informal direct feedback, suggests that the new concepts (which included the Knowledge Targets Pyramid and Knowledge Blocks) take time for participants to digest. Most of the focus of formal direct feedback in the interview came in response to question 2 (see Table 1). Employee A’s immediate response was that he disagreed, but that he thought it was better than it was before. After further discussion, Employee A said he thought it was clear when the author was there helping him implement the component. This comment is an endorsement of the design of MaKE which is supposed to be implemented with face to face discussion an intrinsic part of the process (Sharp 2003). Employee A’s initial response may have reflected a misunderstanding about the design. However, the feedback provided by Employee A between his initial and final response was very useful. In this discussion Employee A identified two aspects of MaKE that could be addressed to MaKE

it clearer. One was that the whole framework could be explained better at the start. Employee A thought this might be done by some form of visual aid to show the linkage between the different levels. The other aspect concerned ways to simplify the whole process and shorten the time it takes to achieve the same results. Employee A and the Interviewer generated some suggestions about this. Employee A said that the detail articulated in the Knowledge Blocks is something of great value that was not generated by other business methods the company used. However, he suggested that it might be possible to achieve this without taking so much time pursuing detail with directly with other participants [at the MaKE Direct level]. Time was not considered a key criterion for MaKE at the start of the implementation of the action research. However, by the end of the action research, it was one of the only issues levelled against the design by the company in the context of the usefulness criteria. There was no informal direct or indirect feedback about the process of MaKE Executive element at this meeting. However, the outputs of MaKE, including the Knowledge Targets Pyramid and the Knowledge Blocks were presented and discussed at Workshop 5. This was the last workshop in the project in which employees and participants could talk freely about the usefulness of the outputs. The informal direct feedback on the outputs was very helpful. The Knowledge Targets

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Pyramid and the Knowledge Block were viewed as useful. The greater detail in the Knowledge Blocks was considered more helpful than other visual tools previously used. In this context, the presentation of the Knowledge Targets Pyramid was discussed further. Employee A and Employee C (IT Development Programme Manager) commented on the difference between the high level view of the Pyramid and the detailed view of the Knowledge Blocks, and it was suggested that a presentational tool that links the two together would be helpful. There were different views about how to do this effectively. As a result, the author created the Linking Overview to join together the other two presentational tools (Sharp 2003). The feedback is limited in various ways. The number of people who were involved was small and the questionnaire only addresses broad issues relevant to the Dilemma. However, and the in depth interview and workshop discussions provide some valuable feedback and reflection. Nevertheless, the feedback endorses the value of the principles of Transparency and Traceability in KM practice which, when used, help to generate KM knowledge strategy with employee buy-in and teamwork. Also, when these principles are applied they help to generate KM strategy that can be summarised while not losing grip of the complexity of it. Also, the feedback endorses the argument that visual tools in combination with other forms of communication (particularly face-to-face meetings), help communicate KM strategy clearly.

CONCLUSION This chapter articulates a problem referred to as the Knowledge Strategy Dilemma and discusses literature that suggests that such dilemmas provide almost insurmountable problems to those who wish to conduct KM in practice. The authors argue that whilst such literature should not be completely ignored, it should not stop KM initia-

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tives going ahead in organisations. This is because much work that has been done in the field that has helped organisations improve the way they work and the value of KM and intangible assets is too significant to ignore. Also, when the Dilemma is broken down there are practical ways to address it which have been established. However, whilst there are approaches that help to address it, the authors argue that MaKE is the best approach to address it as a whole, in a coherent way. This is because there are few other approaches that do so in a comprehensive way with an explicit KM thrust and there are none that use the definition of knowledge as a starting point like MaKE does (Sharp 2006b). Also, this research suggests that the two principles of Transparency and Traceability embodied in MaKE are important distinguishing features of it, valued by organisations, which help address the Dilemma. The authors also argue that a combination of face-to-face meetings combined with visual tools provides important assistance when a KM strategy is developed and communicated. However, this all takes time and leads us to question whether organisations are willing to devote the time it takes to do such KM work.

REFERENCES Ardichvili, A., Maurer, M., Li, W., Wentling, T., & Stuedemann, R. (2006). Cultural influences on knowledge sharing through online communities of practice. Journal of Knowledge Management, 10(1), 94–107. doi:10.1108/13673270610650139 Bontis, N. (2000). Managing organisational knowledge by diagnosing intellectual capital: Framing and advancing the state of the field. In Malhotra, Y. (Ed.), Knowledge Management and Business Innovation (pp. 298–315). London: Idea Group Publishing. Checkland, P., & Scholes, J. (2000). Soft systems methodology in action (30-Year Retrospective). Chichester, UK: John Wiley & Sons Ltd.

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Davenport, T. H., & Prusak, L. (1998). Working knowledge: How organisations manage what they know. Boston: Harvard Business School Press. Edvinsson, L., & Malone, M. S. (1997). Intellectual capital: The proven way to establish your company’s real value by measuring its hidden brainpower. London: HarperBusiness. Galliers, R. D., & Newell, S. (2001). Back to the future: From knowledge management to data management. In Proceedings of The Ninth European Conference on Information Systems, Bled, Slovenia, 27th-29th June, 609-615.

Larsen, M. A., & Myers, M. D. (1999). When success turns into failure: A package-driven business process re-engineering project in the financial services industry. The Journal of Strategic Information Systems, 8, 395–417. doi:10.1016/ S0963-8687(00)00025-1 Leonard-Barton, D. (1995). Wellsprings of knowledge: Building and sustaining the sources of innovation. Boston: Harvard Business School Press. Machlup, F. (1980). Knowledge: Its creation, distribution, and economic significance. Princeton, NJ: Princeton University Press.

Holsapple, C. W., & Joshi, K. D. (1999). Description and analysis of existing knowledge management frameworks. HICSS-32. Proceedings of the 32nd Hawaii International Conference, Systems Sciences.

Malhotra, Y. (2000). Knowledge management and new organizational forms: A framework for business model innovation. In Malhotra, Y. (Ed.), Knowledge Management and Virtual Organizations (pp. 2–19). London: Idea Group Publishing.

Holsapple, C. W., & Joshi, K. D. (2001a). Knowledge management: A three-fold framework. The Information Society, 18(1), 47–64. doi:10.1080/01972240252818225

Miles, M. B., & Huberman, A. M. (1994). Qualitative Data Analysis. London: Sage Publications.

Holsapple, C. W., & Joshi, K. D. (2001b). Organizational knowledge resources. Decision Support Systems, 31(1), 39–54. doi:10.1016/ S0167-9236(00)00118-4 Kaplan, R. S., & Norton, D. P. (1996). Translating strategy into action: The balanced scorecard. Boston: Harvard Business School Press. Kazi, A. S., & Wolf, P. (2006). Real-life knowledge management: Lessons from the field. City: Knowledge Board and VTT. Kelly, C. (2007). Managing the relationship between knowledge and power in organisations. Aslib Proceedings: New Information Perspectives, 59(2), 125–138. Kuznetsov, A., & Yakavenka, H. (2005). Barriers to the absorption of management knowledge in Belarus. Journal of Managerial Psychology, 20(7), 566–577. doi:10.1108/02683940510623380

Nonaka, I., & Takeuchi, H. (1995). The knowledgecreating company. Oxford: Oxford University Press. Nonaka, I., & Takeuchi, H. (1999). The knowledge-creating company. In Mabey, C., Salaman, G., & Storey, J. (Eds.), Strategic human resource management (pp. 310–324). London: Sage Publications. Patrick, K., Rourke, G., & Phillips, N. (2000). Issues of knowledge management. Vine, 30(4), 44–51. doi:10.1108/eb040775 Petrash, G. (1996). Dow’s Journey to a Knowledge Value Culture. European Management Journal, 14(4), 365–373. doi:10.1016/02632373(96)00023-0 Polanyi, M. (1998). Personal knowledge: Towards a post-critical philosophy (first published in 1967). London: Routledge.

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Remenyi, D., Money, A., Price, D., & Bannister, F. (2002). The creation of knowledge through case study research. In Proceedings of Third European Conference on Knowledge Management (3rd ECKM), Trinity College, Dublin, Ireland, 24th-25th September 2002, MCIL, Reading, UK (pp. 575-585).

Sharp, P. J., & Hanlon, S. (2004). MaKE Measures: A synthesis of a KM process with intangible asset measures. in: Proceedings of European Conference of Knowledge Management (5th ECKM), Conservatoire Nationale des Arts et Metiers (CNAM), Paris, France, 30th September-1st October 2004, ACL, Reading, UK.

Seely Brown, J. (2001). Title. Retrieved October 9, 2001, from http://www.brint.com/km/whatis.htm

Snowden, D. (2002). Complex acts of knowing: Paradox and descriptive self-awareness. Journal of Knowledge Management, 6(2), 1–13. doi:10.1108/13673270210424639

Senge, P., Roberts, C., Ross, R. B., Smith, B. J., & Kleiner, A. (1995). The fifth discipline fieldbook: Strategies and tools for building a learning organization. London: Nicholas Brearley Publishing. Sharp, P. J. (2002a). SolSkeme: A scheme for managing knowledge in organisations. In Proceedings of Third European Conference on Knowledge Management (3rd ECKM), Trinity College, Dublin, Ireland, 24th-25th September 2002, MCIL, Reading, UK (pp. 609-617). Sharp, P. J. (2003). MaKE: A knowledge management method. Unpublished doctoral thesis. Staffordshire University, UK. Sharp, P. J. (2006a). MaKE: A knowledge nanagement method. Journal of Knowledge Management, 10(6), 100–109. doi:10.1108/13673270610709242 Sharp, P. J. (2006b). MaKE first steps: A collaborative approach to defining knowledge in organisations. Electronic Journal of Knowledge Management, 4(2), 189–196. Sharp, P. J. (2008). MaKE first steps: How a definition of knowledge can help your organisation. Electronic Journal of Knowledge Management, 5(4). Sharp, P. J., Eardley, W. A., & Shah, H. (2003). Visual tools within MaKE: A knowledge management method. Electronic Journal of Knowledge Management, 1(2), 177–186.

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Stewart, T. A. (1997). Intellectual capital: The new wealth of nations. London: Nicholas Brearley Publishing. Stewart, T. A. (2002). The wealth of knowledge: Intellectual capital and the twenty-first century organization. London: Nicholas Brearley Publishing. Sveiby, K. E. (1997). The new organizational wealth: Managing and measuring knowledgebased assets. San Francisco, CA: Berrett-Koehler. Sveiby, K. E. (2007). Disabling the context for knowledge work: The role of managers’ behaviours. Management Decision, 45(10), 1636–1655. doi:10.1108/00251740710838004 Swan, J., Newell, S., Scarborough, H., & Hislop, D. (1999). Knowledge management and innovation: Networks and networking. Journal of Knowledge Management, 3(4), 262–275. doi:10.1108/13673279910304014 Teece, D. J. (1998). Capturing value from knowledge assets: The new economy, markets for know how, and intangible assets. California Management Review, 4(3), 55–79. Tobin, K. J., & Snyman, R. (2008). Once upon a time in Africa: A case study of storytelling for knowledge sharing. Aslib Proceedings: New Information Perspectives, 60(2), 130–142.

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Vasconcelos, A. C. (2008). Dilemmas in knowledge management. Library Management, 29(4/5), 422–443. doi:10.1108/01435120810869165 Wiig, K. (1993). Thinking about thinking: How people and organizations create, represent, and use knowledge (Knowledge management foundations, Vols. I-III). Arlington, TX: Schema Press.

ENDNOTE

*

The acronym ‘MaKE’ stands for “Manage Knowledge Effectively” (Sharp, 2002c). The authors wish to acknowledge that this is not to be confused with an acronym similar to, but different from this one, which is described in Winfield et al. (1996).

Winfield, M. J., Basden, A., & Cresswell, I. (1996). Knowledge elicitation using a multi-modal approach. World Futures, 47, 93–101. doi:10.1080 /02604027.1996.9972589

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Chapter 19

Knowledge Management and Innovation Lorna Uden Staffordshire University, UK Marja Naaranoja Vaasa University of Applied Sciences, Finland

ABSTRACT In today’s society, innovation and knowledge management are no longer luxury items. Instead, they are necessities and a means of economic development and competitiveness. Knowledge and innovation are inseparable. Knowledge management competencies and capacities are essential to any organisation that aspires to be innovative. Innovation and knowledge management are closely related. This paper discusses the importance of knowledge management in innovation for organisations. It describes how innovations can be achieved through the role of knowledge management using a case study involving the renovation and building of a school project in Finland. The case study shows how knowledge creation and sharing were used to help innovation using vision building.

INTRODUCTION Innovation is a process through which the nation creates and transforms new knowledge into useful products, services and processes for national and global markets – leading to both value creation for stakeholders and higher standards of living. The difference between invention and innovation is that invention is a new product, whereas innovation is a new value (Szmytkowski, 2005). To turn invention into innovation requires different types DOI: 10.4018/978-1-60566-701-0.ch019

of knowledge, capabilities, skills and resources. Innovation is a continuous process - often an effect of small incremental/marginal changes in the product or process. The EU marked 2007 as a year of focus for innovation within its member countries. Many governments in the EU are putting significant investment into education and business to stimulate innovation. Innovation is the mainstay of an organization. The speed of innovation has been made possible by rapidly evolving technology, shorter product life cycles and high increase in new product development. For organizations to remain competi-

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tive, innovation is essential. Owing to changing customer needs, extensive competitive pressure and rapid technological change, innovation has become increasingly complex (Cavusgil et al., 2003). du Plessis (2007) attributed the complexity to the increased growth in the amount of knowledge available to organisations. Innovation depends intensively on the availability of knowledge. The complexity created by the richness of knowledge has to be identified and managed to ensure successful innovation (Adams & Lamont, 2003; Pyka, 2002). Knowledge management has important implications for innovation; therefore it is imperative that we understand the role of KM in innovation. This is especially true for construction industry. Construction is portrayed as not being an innovative industry and often it has been labelled as ‘extremely conservative’ (Rosenberg, 1982). It is also ‘low tech’ (Reichstein, Salter & Gann, 2005) and ‘an industry of the old type’ (Landes, 1969). There are several factors that have been put forward as the reasons for the lack of dynamism and innovation. Barrett et al. (2007) attributed three strands to the causes. Firstly, the temporary project-based nature of the industry is seen as constraining innovation (Gann & Salter, 2000). Secondly the structure of the industry with its preponderance of small firms employing less than five people gives rise to an associated limited capacity to innovate (Sexton & Barrett, 2003). Lastly the adversarial nature of the industry with associated short-termism and opportunism does not encourage long term solutions. Another factor in construction is the way innovation is measured and modelled, because of it being a service centred industry. According to Winch (2003), standard measures for construction, take a very narrow view of the industry, and ignore value adding activity in design and use of buildings. The construction industry needs to work collaboratively and pool knowledge in order to capture innovation, says Director of Constructing Excellence, Peter Cunningham. There is a need for innovation in the construction industry. Changes in global markets,

increased customer expectations, and government pressure have all led to innovation becoming a key focus for the construction sector. This chapter briefly reviews innovation and the role of KM in innovation. It begins with a brief review of innovation followed by knowledge management and its role in innovation. This is followed by a case study describing the role KM plays in innovation of a school through knowledge sharing in construction. The chapter concludes with suggestions for further research.

INNOVATION There are many definitions given to innovation. Drucker (1975) defines innovation as the process of equipping in new, improved capabilities or increased utility. Others define innovation as the process of introducing new ideas to the firm which result in increased performance. According to Rogers (1998), innovation is concerned with the process of commercialising or extracting value from ideas. An innovation is any new or substantially improved good, service or process that has been commercialised. For example, innovation can be the introduction of changes in management, work organization, the working conditions, skills of workforce or marketing systems (Rogers, 1998; DIST, 1996). To be innovative requires organizations to engage their customers in continuous dialogue, to co-develop solutions through a knowledge exchange of needs and ideas. Chan and others (2004) define innovation as the introduction of a new combination of the essential factors of production into production systems. It involves new product, new technology, new markets and new combinations. According to Cardinal and others (2001), the innovative process encompasses technical, physical and knowledge based activities that are central to form product development routines. Innovation is defined by Herkema (2003) as a knowledge process aimed at creating new knowledge geared towards the

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development of commercial and viable solutions. It is a process wherein knowledge is acquired, shared and assimilated with the aim of creating new knowledge, which embodies products and services. Gloet and Terziovski (2004) described innovation as the implementation of discoveries and interrelations and the process by which new outcomes, whether products, systems or processes, come into being. They also distinguish radical and incremental innovation. Incremental innovation is live extensions or modifications of existing products. It is also known as market-pull innovation and does not require significant departure from existing business practices. It is likely to enhance existing internal competencies by providing the opportunity to build on existing know-how. On the contrary, radical innovation is likely to be competence destroying, often making existing skills and knowledge redundant and necessitating different managerial practices. Business may be put at risk because of the difficulty to commercialise. Radical innovations are crucial for long-term success because they involve development and application of new technology, some of which may change existing market structures (du Plessis, 2007). Schumpeter (1934) identified five different types of innovations: • • • • •

New product; New methods of production; The exploration of new market of production; New source of supply; New ways to organise business.

Although there are many definitions given to innovation, most definitions share common issues relating to knowledge, which may be turned into new products, processes and services to improve competitive advantage and meet customers’ changing needs (Metaxiotis & Psarras, 2006). Innovation is not just about product innovation, it is also concerned with product, market and

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production competencies, as well as administrative competencies, as defined by Lowe (1995).

KNOWLEDGE MANAGEMENT Knowledge is increasingly recognized as the most important resource in organizations and a key differentiating factor in business today. It is increasingly being acknowledged that Knowledge Management (KM) can bring about the much needed innovation and improved business performance in the construction industry (Egbu et al., 1999). Knowledge is defined as a dynamic human process of justifying personal belief towards the truth (Nonaka & Takeuchi, 1995). It can also be defined as ‘know-why, know-how and knowwho’, or an intangible economic resource from which future resources will be derived (Rennie, 1999). Knowledge is built from data, which is first processed into information (i.e. relevant associations and patterns). Information becomes knowledge when it enters the system and when it is validated (collectively or individually) as a relevant and useful piece of knowledge to implement in the system (Carrillo et al., 2000). Besides the meaning of knowledge is the identification of the kind of knowledge that is to be managed. There are various kinds of classification of knowledge: formal (explicit) and tacit (expertise) knowledge; foreground and background knowledge; knowledge of business environment or knowledge for control activities (Carrillo et al., 2000). Knowledge management is referred to as the process creating, codifying and disseminating knowledge for a wide range of knowledge intensive tasks. (Harris et al., 1998). These tasks can be decision support, computer assisted learning, research (e.g. hypothesis testing) or research support. There are various methodologies that support the systematic introduction of KM solutions into an organization. One of the most popular methodologies is Common KAD (Schreiber et al., 1999). According to Brelade and Harman (2001),

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Knowledge management (KM) is obtaining and using resources to create an environment in which individuals have an access to information and in which individuals obtain, share and use this information to raise the level of their knowledge. In addition to this, individuals are encouraged and enabled to obtain new information for the organization. Knowledge is the key resource that must be managed if improvement efforts are to succeed and businesses are to remain competitive in the global markets (Drucker, 1993; Davenport & Prusak, 1998). Better management of knowledge within the firm will lead to improved innovation and competitive advantage. Sustainability requires special content and processes for Knowledge Management. The main purpose of knowledge management is to enhance exploitation (i.e. where existing knowledge is captured, transferred and deployed in other similar situations) or exploration (i.e., where knowledge is created) (Levinthal & March, 1993). Exploitation is to reduce problems of reinventing the wheel by using existing knowledge more effectively. Although it is important for innovation, it is exploration through knowledge sharing that allows the development of new ideas and solutions. According to Gloet and Terziovski (2004), knowledge management is the formalisation of and access to experience, knowledge and expertise that creates new capabilities, enables superior performance, encourages innovation and enhances customer value. Knowledge management is about supporting innovation, the generation of new ideas and the exploitation of the organization’s thinking power (Parlby & Taylor, 2000). It also includes the capture of insight and experience to make them available and usable when, where and by whom they are required. These authors argue that knowledge management allows easy access to expertise and know-how, whether formally recorded or in someone’s mind. In addition it allows collaboration, knowledge sharing, continual learning and improvement.

du Plessis (2007) defines knowledge management as a planned, structured approach to manage the creation, sharing, harvesting and leverage of knowledge as an organizational asset, to enhance a company’s ability, speed and effectiveness in delivering products or services for the benefit of clients. She also suggested that knowledge management typically takes place on three levels: individual, team and organization. Knowledge management is also a holistic solution incorporating a variety of perspectives such as people, processes, culture and technology. It is not merely focused on innovation, but it creates an environment conducive for innovation to take place. According to Liebowitz (2003), knowledge management means creating value from an organization’s intangible assets and how to best leverage knowledge internally and externally.

The Advantages of Knowledge Management Some organizations believe that by focusing exclusively on people, technologies, or techniques, they can manage knowledge. However, that exclusive focus on people, technologies, or techniques does not enable an organization to sustain its competitive advantages. It is, rather, the interaction between technology, techniques, and people that allow an organization to manage its knowledge effectively (Bhatt, 2001). The improved knowledge management system offers several benefits to the construction department for our project, because knowledge management: • • • • • •

can reduce risk; can increase effectiveness of the organization; provides added value to the organization; allows better decisions to be made; can learn from successful projects; encourage transfer of knowledge between employees;

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• • •

reduces unnecessary processes and streamlines operations; increases employee retention rates; offers better productivity.

KNOWLEDGE MANAGEMENT FOR INNOVATION According to du Plessis (2007), there are three main drivers of the application of knowledge management in innovation. Firstly, the role of knowledge management in innovation is to create, build and maintain competitive advantage through utilisation of knowledge and through collaboration practices. To overcome the complexity involved in sustaining innovation and competitive advantage, close collaboration is needed across organizational boundaries (Cavusgil et al., 2003). Secondly, the role of knowledge management in innovation is that knowledge is a resource used to reduce complexity in the innovation process, and managing knowledge as a resource is critical in innovation. Cavusgil (2003) argues that firms that create and use knowledge rapidly and effectively are able to innovate faster and more successfully than those that do not. Thirdly, the benefit of knowledge management applied to the innovation process is the integration of knowledge both internal and external to the organization by making it more available and accessible. Knowledge integration means that knowledge can be exchanged, shared, evolved, refined and made available at the point of need. Knowledge integration via knowledge management platforms, tools and processes must facilitate reflection and dialogue to allow personal and organizational learning and innovation. Chen and others (2004) argue that without effective information and knowledge management that drives knowledge integration, which in turn underpins innovation, organizations could be under-utilising knowledge as an innovative resource. Thus, knowledge management plays a crucial role in the development of sustainable competitive advantage through innovation. There are several 304

important functions that knowledge and knowledge management play in innovation (du Plessis, 2007) including: 1. Knowledge management enables the sharing and codification of tacit knowledge. Cavusgil and others (2003) suggested that tacit knowledge is critical for organizational innovation capability. Collaboration between organizations helps in the sharing of tacit knowledge, which in turn impacts on innovation capability. Where a lot of tacit knowledge is used for innovation, collaboration between cross-functional teams is necessary; 2. Knowledge management and its role in the innovation process through the use of explicit knowledge. du Plessis (2007) argues that knowledge management can play a significant role in making knowledge explicit for re-combinations into new and innovative ideas. Knowledge management provides the tools, processes and platform to ensure knowledge availability and accessibility, e.g. through structuring of the knowledge base. It also ensures that explicit knowledge that can be used as input to the innovation process is gathered internally and externally; 3. Knowledge management enables collaboration in innovation. Collaboration is defined as the ability of customers, suppliers and employees to form knowledge sharing communities within and across organizational boundaries, that can work together to achieve a shared business objective, resulting in benefits to all community members (du Plessis, 2007). Cavusgil and others (2003) argued that gathering tacit knowledge from collaboration partners can potentially reduce risk and cost in innovation by ensuring a right-first-time approach. This helps to shorten development cycles and ensure effective innovation;

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4. Knowledge management enables the managing of various activities in the knowledge management life cycle. This consists of phases of creation, gathering, sharing and leveraging of knowledge. It ensures the integration of knowledge within the organization through the provision of structure and organizational context that enables knowledge sharing and leveraging. These various types of knowledge management life cycle activities can make direct contributions to the development of innovation. The first example is absorptive capacity, which refers to the organization’s ability to recognise the value of new, external information, assimilate the information and then apply the learned knowledge to its own product and service output. The second example is transformative capacity, that is, the organization’s ability to gather, assimilate, synthesise and redeploy knowledge to meet specific current demands. The third example is provision, distribution and storage of internal knowledge needed to utilise organizational resources effectively. The fourth example is the creation, processing and distribution of knowledge to be assessed by organizational members for strategic decision making. The final example is the examination of the external environment for identification of competitor activities and potential learning opportunities; 5. The creation of a culture conducive for knowledge creation, sharing and collaboration. Scarbrough (2003) believes that knowledge creation, sharing and leveraging build employee skills that are particularly relevant to the innovation process. Knowledge management also contributes to the creation of a culture conducive to innovation through the way that knowledge creation and sharing behaviour is measured and rewarded.

According to du Plessis (2007), the value proposition of knowledge management in the innovation process is as follows: •

• • •

•

•

•

•

•

•

•

Knowledge management assists in creating tools, platforms and processes for tacit knowledge creation, sharing and leverage in the organization, which plays an important role in the innovation process; Knowledge management assists in converting tacit knowledge to explicit knowledge; Knowledge management facilitates collaboration in the innovation process; Knowledge management ensures the availability and accessibility of both tacit and explicit knowledge used in the innovation process, using knowledge organization and retrieval skills and tools such as taxonomies; Knowledge management ensures the flow of knowledge used in the innovation process; Knowledge management provides platforms, tools and processes to ensure integration of an organization’s knowledge base; Knowledge management assists in identifying gaps in the knowledge base and provides processes to fill in the gaps in order to aid innovation; Knowledge management assists in building competencies required in the innovation process; Knowledge management provides organizational context to the body of knowledge in the organization; Knowledge management assists in steady growth of the knowledge base through gathering and capturing of explicit and tacit knowledge; Knowledge management provides a knowledge-driven culture within which innovation can be incubated.

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Knowledge is the primary source of an organization’s innovative potential (Marshall, 1997). Effective knowledge management involves (a) identifying knowledge (b) creating new knowledge (c) building competence (d) effective management of innovation (Enkel et al., 2002). Knowledge creation is the first step to facilitating innovation in the company. There are seven sources of innovation within and outside of an organization (Drucker, 1991). New knowledge is the most important source of innovation. The process of knowledge creation shows that it takes place in five phases. These phases are 1) sharing tacit knowledge, 2) creating a concept, 3) justifying the concept, 4) building a prototype and 5) cross-levelling the knowledge (von Krogh, Ichijo & Nonaka, 2000). A knowledge creating company needs to create an appropriate environment and offer an appropriate space (Nonaka & Konno, 1998), for creating new knowledge (Nonaka & Takeuchi, 1995). According to Krogh and others (2000) knowledge creation can be enabled through the following activities: a) Instilling a knowledge vision, b) managing conversations, c) mobilizing knowledge activists, d) creating the right context, and e) globalizing local knowledge. Better management of knowledge within the firm will lead to improved innovation and competitive advantage. It is important to investigate how to create the desired innovation and which specific requirements and critical factors can support innovation. It is our belief that effective knowledge management can lead enterprises to successful innovation.

THE KNOWLEDGE MANAGEMENT CYCLE Metaxiotis & Psarras (2006) described the knowledge management cycle as follows. This process consists of four key steps, as shown in Figure 1. Step 1. knowledge identification and capture refers to identifying the critical competencies,

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types of knowledge and the right individuals who have the necessary expertise and experience that should be captured. One approach is to conduct a knowledge audit or the use of intranets. Step 2. knowledge creation is the most critical step of the knowledge management cycle as this involves innovation. Knowledge creation is a process that involves tacit and explicit knowledge (Popadiuk & Choo, 2006). Tacit knowledge is closely related to knowledge exploration, while explicit knowledge is more concerned with knowledge exploitation. Based on the model of Nonaka and Takeushi (1995), exploration involves the creation and use of tacit knowledge through the process of socialisation and externalisation. Both types of knowledge creation are taking place during the knowledge creation process. This step consists of four sub-steps: (a) Generating ideas involves getting the innovation process going. Generating innovation involves imagining possibilities, questioning, sensing new problems and opportunities, viewing situations from different perspectives and getting informed through direct experience. It consists of seeking out and proactively sensing new problems and Figure 1. The Knowledge management cycle (from Metaxiotis & Psarras, 2006)

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opportunities. It is important to initiate the problems rather than waiting for problems to be provided. (b) Conceptualising involves divergence. It favours gaining understanding by abstract thinking, then gaining understanding by direct experience. This results in putting new ideas together, discovering insights that help define problems and creating theoretical models to explain things (Basadur & Gelade, 2006). It is important to understand at this step, a theory or explanation must be logically sound and precise. As many ideas as possible should be incorporated into a single conceptual scheme. This step focuses on problem definition and idea finding. There should be brainstorming sessions conducted during problem-solving sessions in this step. It is essential to ask good questions and find ways to define the problem before looking for solutions. Questions such as, ‘Why’? and ‘What else?’ should be repeatedly asked during creative thinking. (c) Optimising also focuses on gaining understanding by abstract thinking. Unlike divergence in conceptualising, thinking should be converged (evaluate and select should be the principle here). The result is to develop practical solutions and plans from abstract ideas. The focus here is on idea evaluation and selection and planning for the implementing process. (d) Implementing completes the innovation process. It favours convergence, but unlike optimising, it favours learning by direct experiences rather than abstract thinking. Implementing involves trying things out rather than mentally testing them. It is important to try as many different approaches as necessary, as well as bringing others on board. This helps in gaining acceptance and implementing.

Step 3.knowledge application is concerned with taking the shared knowledge and internalising it within one’s perspective and worldview. Innovation management is essential if the organization wishes to remain competitive. Besides technical integration, it is also important to manage knowledge such as content management, assigning knowledge roles, etc. Step 4.knowledge sharing culture needs to be created in the organization. One method for knowledge sharing is to use online communities. This helps to establish a community of practice. It is also important to bear in mind that employees with highly specialised knowledge, who bring new ideas and experiences, should be recognised and rewarded to make knowledge sharing a reality in the organization that supports innovation.

Role of Knowledge Management for Innovation in Construction There is a need for innovation in the construction industry. Changes in global markets, increased customer expectations, and government pressure have all led to innovation becoming a key focus for the construction sector. Construction firms are being challenged to be more adept at successful innovation to better meet client needs and to enhance business competitiveness. Conscious management of innovation in construction firms is becoming more and more a necessity. However, the possibilities and ways to successfully put an innovative idea into practice depend on a range of issues. These include issues such as knowledge strength, cooperative behaviour, financial strength and time needs, which were identified as critical variables of the internal environment. (Abbott et al., 2008). Innovation can mean different things to different people, sectors and disciplines. It may signify different things to the social scientist, economist or anthropologist. Innovation in sectors such as construction may differ greatly from innovation in other sectors. However, even within the

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particular sector of construction, the definition is also varied. (Slaughter, 1998). In the construction industry, practitioners’ successful innovation has been defined as, the effective generation and implementation of a new idea, which enhances overall organizational performance. (Barrett & Sexton, 1998). According to these authors, innovation has a number of outcomes, including: • • •

The renewal and enlargement of products and services, and their associated market; New methods of production, supply and distribution; New organizational and work forms and practices.

As can be seen, innovation is subjective and can mean different things to different people. However, there are common features that can be abstracted from the definition. (Abbot et al., 2008). Common in the definition is the perception that innovation does involve something new and does have an outcome. DTI (2003) defined innovation as the ‘successful exploitation of new ideas. In this definition innovation delivers better products and generates more efficient production and processes. For the consumer it means higher quality and better value goods and more efficient services, for the business it means sustained growth and higher profits and for the economy it is the key to higher productivity and prosperity. This definition will be used in this chapter to mean innovation.

How Does Innovation Occur in Construction? There are different types of innovation that can be delivered: product, process or organizational innovations in construction. The scope of innovations can range from radical/disruptive to incremental evolutionary innovations (Trott, 1998). The role of knowledge in the innovation process is crucial in the different range of innovation in construction.

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Knowledge management is becoming increasingly important in the construction industry in order to satisfy the requirements of quality. Firstly the construction project consists of numerous people from different companies with different professional backgrounds such as clients, architects, project managers, designers, site managers, and workers. Secondly, the project organization is unstable over time and often becomes completely changed from phase to phase during the project. Thirdly, most project-related problems, solutions and experiences are usually not documented or stored in a system database and the process of capturing and storing them in usable forms is not easy. Therefore, there is a need to improve the use of Knowledge Management in construction industry and construction projects to overcome projects’ complexity, diversity and non-standard production methods. For incremental innovation, it is important to reuse existing knowledge in many aspects of the process. On the other hand, radical innovation requires new knowledge to be created or applied from different contexts. There are various means can be used to capture new knowledge into the organization such as conversations with customers and suppliers, and community of practice and research etc. External new knowledge is needed to generate innovation (Auernhammer et al., 2001). Besides external knowledge, internal information can also come from internal units within the organization. It is therefore important in organization to promote the sharing and creating of knowledge. According to Woo and others (2004), architecture, engineering and construction (AEC) industries are good at collecting and storing explicit information in enterprise databases, but they are not always successful at tacit knowledge retrieval and sharing. In (AEC) industry, there is a need for innovation and improved business performance. This requires effective deployment and utilization of project knowledge, the need for strategic knowledge management. (Kamara et al., 2002). Knowledge management in construction

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can help deal with the process of creating value from construction operation and organization to company knowledge. To achieve this means that it is important to identify critical success factors in knowledge management for the people who will apply it in the construction enterprises. A variety of factors determine significant success ingredients for knowledge management in construction projects. Shen and Lui (2006) have identified several critical factors that are important for the successful implementation of knowledge management in construction. Ten critical success factors were extracted through a synthesis of empirical studies and opinions from 40 engineers in knowledge management divisions of construction projects. From the analysis of results, these authors identified five success factors as critical. They are: Establishment of a Reward Strategy; Willingness to Share Knowledge; Mechanism to Approve Activities; Friendly System to Exchange and Reuse Knowledge and Top Management Support. An effective way to improve management in construction is to share experiences among engineers and other personals involved in the projects. Subsequent sections of the chapter describes how vision building can be used an effective method to promote the sharing of tacit knowledge in a building project for a school.

CASE STUDY Abbot et al. (2008) argued that the visibility of innovation activity in the construction sector is dependent on the type of innovation. According to these authors, in the Hidden Innovation construction case study, innovation can be classified in three ways as: ‘sector-level’, ‘business-level’ and ‘project-level’ innovation. Sector Level Innovation is very visible and often produces radical or step change in innovation. It takes two principal forms which are led by, regulations and standards or by the dominant construction client. Businesslevel innovation tends to be more obscure than

sector-level, and can produce either radical or incremental innovation. It focuses on general resource and capability development, rather than being project specific. Project-level innovation activity is the most hidden. However, it has the greatest impact on sector performance, and is generally incremental in nature. According to Abbot and others (2008), it is about the co-production of novel design solutions between different parts of the design team (architectural, structural engineer, mechanical engineer, and so on) that builds upon their respective knowledge and experience. They also argued that the day-to-day problem solving on site during the production phase is very much grounded in participants’ tacit knowledge and ‘learning by doing.’ The cumulated impact of incremental innovation over time is significant, both at firm and aggregated sector level. How do we promote project level innovation in construction? It is our belief that knowledge management through knowledge sharing using vision building has a important role in this. This case study describes how the use of visionbuilding can be used as an idea-generation tool in the construction industry for renovating and rebuilding a school in Finland. The case study is about renovating of an old building and construction of a new building for a school. The school has 270 pupils and occupies 3000 square meters. The project started in 2003 and was completed in 2005. The building activities of the school stretched across several sites. The construction project involved many different stakeholders such as architects, surveyors, designers, town council officers, project manager, engineers, builders and school representatives. It was important that the project (construction of a new building and renovation of old buildings) met the needs of the school. This means that all the different personnel involved in the project needed to share the same mental models and mind sets. Recent literature has attached growing importance to the alliances, networks and interdis-

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ciplinary and inter-organizational project teams as a means of expanding such competence (Kinti & Hayward, 2006). This expertise is driven by a process of knowledge sharing within the teams and the development of networked expertise (Swan & Scarbrough 2005). Although there are many studies on knowledge sharing within the firm (Hansen, 1999; Newell et al., 2003), there is currently little work being done about the processes of knowledge sharing and knowledge building at the boundaries of organizations, where teams of skilled experts from different institutional backgrounds collaborate to create new knowledge (Barley & Kunda, 2004). It is our belief that the source of innovation resides not as expertise in a single individual, but in the interaction of experts involved in the process of knowledge co-configuration in boundary zones that lie beyond institutional boundaries (Kinti & Hayward, 2006; Edwards, 2006). Since it is necessary to acquire and exchange tacit knowledge between the various people involved, effective creation of new ideas takes place where socialisation is supported. This means that people involved need to trust each other and have common mental models. An effective approach is to have face to face meetings where different stakeholders can share knowledge. We believe that vision-building can be used as an effective knowledge sharing tool in idea generation. There were three objectives in vision building for the construction team. Firstly to motivate stakeholders to take part in the design process especially school personnel. Secondly, to make ensue that the whole team (architect, other designers, school representatives, political decision makers and authorities of the different departments of the town, project manager and facility department) share the same view of the project and its objectives. Thirdly to use the vision building process as a tool to prioritize the different needs and wishes of the stakeholders. Appendix A describes the vision-building procedure.

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IMPLICATIONS OF KM IN CONSTRUCTION INNOVATION Innovation depends on the availability of knowledge, and the role of knowledge management is critical for the success of innovation. Central to innovation is knowledge creation and sharing. Firstly, knowledge management is used to facilitate collaboration among the different stakeholders involved in the construction project. Secondly, through vision building we are able to integrate both internal and external knowledge, making them available through knowledge management. Thirdly, vision building enables tacit knowledge to be made available through collaboration between cross-functional teams. Fourthly, through the vision building experience, explicit knowledge is made available for recombining into new and innovative ideas. Fifthly, vision building provides us the means to gather and capture tacit and explicit knowledge in the construction industry. Sixthly, the required knowledge and competencies required for the project are developed through the vision building approach. Last, but not least, vision building provides a culture where knowledge sharing is encouraged and promoted. This leads to behavioural change towards creation, sharing and leverage of knowledge. There are several benefits arising from the vision building approach. Firstly it creates an understanding of the importance of having a common vision and mental model of the project to be built. Secondly, the end users of the project were very glad to be involved in the project. Thirdly, the vision building guidelines have now been written into the construction process of the town council.

LESSONS LEARNED There are several lessons that have been learned from the vision-building process. Central to this is the importance of organizational support. Innovation requires the support of top management. The

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key player for this project was the deputy mayor of the town. Without his support, the vision-building process would never have been achieved. We found that the vision-building process allows us to have a common understanding of the vision of the project as well as having common mental models of the buildings to be built. The issue of integrating innovation in the organization is important. Changes in organization, such as a new approach to doing things, take time. It is important to allow the organization time to change. We have to take change step by step. Preparing the vision is not enough. Strategies must be in place to enable the sharing of the vision within the organization. It is the process of togetherness that created the vision. Vision-building enables the participants of the project to realise that their views and objectives of the project are not the only ones. Participants learned that there were reasons for renovating the school building and keeping it at the same location in the city. There were also users’ needs from people working in the facilities and the maintenance departments. In addition there were budget and time restrictions for the completion of the project.

laborators, even competitors, to build and operate innovation systems. Networking can be used to for linkages and connections. Innovation networks are more than just ways of assembling and deploying knowledge in a complex world. According to Bessant and Tidd (2007), networks have emergent properties – that is, the potential for the whole to be greater than the sum of its parts. The benefits include getting access to different and complementary knowledge sets, reducing risks by sharing them, etc. Participating in innovation networks can help organizations bump new ideas and creative combinations – even for mature businesses. Bessant and Tidd (2007) argue that getting together through networks helps to open up new and productive territory. These authors also point out that networking also helps innovation in providing support for shared learning. There are several advantages associated with shared learning (Bessant & Tidd, 2007). These include:

FUTURE TRENDS

•

To innovate requires different types of knowledge, capabilities, skills and resources. No single individual in an organization possesses all the required skills. Innovation is not a solo act, but a multi-player game (Bessant & Tidd, 2007). It depends on working with many different players, typically involving people working together in teams inside an organization. However, to be productive, it is important to form links between organizations. Smart organizations have recognised the importance of linkages and connections. This involves getting close to customers to understand their needs, working with suppliers to deliver innovative solutions, linking with col-

• •

•

In shared learning there is the potential for challenge and structured critical reflection from different perspectives; Different perspectives can bring in new concepts; Shared experimentation can provide support and open new lines of inquiry or exploration; Shared learning provides an environment for surfacing assumptions and exploring mental models outside of the normal experience of individual organizations.

Innovations ought to be informed by, and contribute to, the development and realization of environmentally and socially sustainable business strategies and practices. Research should be conducted to address many of the issues of sustainable innovation. How can Knowledge Management contribute to sustainable value creation in the new economy? What opportunities for advancing sustainability are provided by emerging knowledge

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management technology? What are the critical social systems and cultural issues involved in turning knowledge management into a vital, dynamic, self-renewing learning system in support of sustainability? Further research into these issues is needed if we are to remain competitive in a knowledge society.

CONCLUSION No matter how big or small the organization, if it does not innovate, it will not be able to survive. Organizations need new knowledge. Knowledge creation takes different forms such as new business, improved organizational processes and systems, new products and services. Implementing new products and processes, as well as obtaining and creating new knowledge, is an undeniable requirement for market competition. Knowledge Management is a key driver and enabler for preeminence in the new Sustainable Economy. To implement innovation process and skills that are sustainable requires that organizations continue to find, define and solve problems and implement sustainable solutions. Organizations must cultivate a knowledgesharing culture to be innovative. One approach of sharing and creating new knowledge is that of vision building. Although the vision building process was time consuming and expensive, the benefits outweighed the costs by enabling the different personnel involved in the project to be brought together. We believe that vision building is a powerful tool that can help us to identify the need of end users as well as providing a platform where different people involved in the construction can come together to share knowledge. By sharing knowledge, new ideas and knowledge are created. Through vision building, different stakeholders involved in the project can collaborate through knowledge sharing to arrive at a shared objective for the project. We believe that it is important for all parties that are involved in

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the construction project to have the same vision and mental model of the project to ensure that the aims and objectives of the project are understood and interpreted in the same vision by all. This is important because if this is achieved then the project would be a success – that is, what the project delivers is what the customer expected. Besides this, the vision building scenario enables the sharing of tacit knowledge in construction. The reuse of knowledge minimizes the need to refer explicitly to past projects; reduces the time and cost of solving problems, and improves the quality of solutions during the construction phase of a construction.

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APPENDIX VISION BUILDING IN PRACTICE This section describes our vision-building approach. A half day session was organised by the researchers and town personnel to build shared vision of the construction project. The participants consisted of all staff from the school, some parents of the students, city planning personnel, facilities and maintenance personnel, officers from the museum and designers involved in the project. There were 69 participants involved in the session. The largest group of participants was people working at the school. Representatives from the building department included project manager, architect and supervisors. Some of the participants were asked to write a short introduction about their own viewpoints and goals for project. The deputy mayor of the city then presented twelve different viewpoints to the participants. This helped the different parties to understand that their own needs and wishes are not the only ones and that it would be very likely that the wishes of some of the parties would not be met. The introductions by the deputy mayor also showed that beside the small core team of the project, there are several different parties involved in the project. The participants were randomly divided into small groups of 7-8 people, lead by a local leader, to discuss the kind of school that they would like to have. Each group consisted of representatives from the different parties. There were three different scenarios in which the participants were involved. These were a small village school, a poor school and a specialized school. Each of the groups was allocated to one of these scenarios. The aim of the exercise was so force the groups to consider the different possible features of the three school scenarios. In the case of the small village school scenario, the school is the heart of the village and there are other activities going on besides teaching. The poor school scenario was where there were a large number of students in the school and there was little money to spend on education. For the specialized school scenario, this occurs where schools are becoming competitive. In order to compete, the school needs to become specialised. Participants in each of these groups had to consider what the most important features of their given schools were. Each member of the group had to come up with what he/she believed to be important and write these ideas down on paper. Using the ideas generated, the group voted for the three most important things from each list. By working together and sharing knowledge, each group generated new ideas, based on their individual experiences and knowledge. This enabled individual views to be heard. Each group then presented their generated ideas to the other groups. The ideas presented by each group were then listed and written down on flip charts. Words such as ‘safety’, ‘practical’ and ‘acoustics’ appeared. Each participant cast their votes to choose the three most important features chosen from the list presented by all the groups. Among all the features presented, eight items were chosen as most important for the school building project. A common vision for the project was thus obtained, based on the voting results. Discussion then followed to determine the actual meaning of the words chosen. This was to make sure that every participant had the same understanding and shared the same objectives for the project. During the discussion, some changes were made to have a better understanding of the proposed vision for the project. Several iterations were made following the discussion. In the final version the built outcome should be:

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• • •

Practical and safe; Supporting specialization using multipurpose rooms; Economical and of good quality.

Most of the participants found the vision-building session beneficial and believed there was a need for it. All participants came in their own time. People felt that they had a chance to influence the project. Many believed that this exercise should be carried out for every construction project. The aim of the vision-building exercise has been achieved.

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Chapter 20

Holonic Management:

Innovation Creative Entrepreneurship Akira Kamoshida Tokyo Institute of Technology & Nagoya University of Commerce and Business, Japan

ABSTRACT The aim of an innovative management is to intentionally create a “chaos edge” and to foster and organize the ideas which are born. Chaos edge is a term usually used in complexity studies, but it is also highly applicable to management. In this paper, the management concept used to create innovation is referred to as “Holonic management.” Holonic management requires the following three elements: 1) cultivating the soil from which innovation shoots can grow, 2) introducing an appropriate competition principle, and 3) preparing a strict evaluation and proper support system. Constructing the field of chaos edge in holonic management can activate an internal environment to create ideas, which result in the internal cooperative work possible to generate innovation. The “Heretic management” finds the innovation shoot created by a minor group within a corporation and allows it to grow without fear of failure. This is not just the most effective tool. It is also the method for the realization of knowledge management.

STRATEGIC MANAGEMENT AND A NEW MANAGEMENT PARADIGM Corporate strategy should direct the way we invest managerial resources such as people, capital and materials in order to achieve corporate intention. Historically speaking, strategy has been used within military terminology, exemplified by the ancient Sun Tzu’s ‘The Art of War’ or in more recent history, Carl von Clausewitz’s ‘On War’.

Strategy has been argued for over 2300 years, since Sun Tzu or more recently in the 200 years since von Clausewitz (1780–1831) that it continues to be a permanently ongoing subject of interest. What is the essence of corporate strategy? If it is true, what is required within the corporate world in order to continue its progress by contributing to society, strategy is nothing but a methodology to achieve the requirements. Therefore, it is not until the ‘action’ to conduct the strategy and to obtain the ‘results’ that we realize the true value of

DOI: 10.4018/978-1-60566-701-0.ch020

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Holonic Management

strategy. It is worthless to argue a strategy merely on ‘desk principles’. An ancient precept teaches the importance of ‘unified knowledge and action’ that is the thought offered by Wang Yangming (1472–1529), who was the founder of Yangming school and known as an activist among Confucian scholars. This precept means that ‘knowledge’ and ‘action’ are undividable and ‘action without knowledge’ or ‘knowledge without action’ loses the truth. Once the precept is applied to strategic management, strategy cannot be considered not worthy until it has been conducted and achieved actually, and simultaneously corporate can only manage coherently with its vision and policy, if the corporate has employed individual management tactics. The current world climate is shifting towards a ‘knowledge society’. John Naisbitt in ‘Megatrends’ (1982) forecast the ‘Ten Tidal Waves’ toward the 21st century, in which he said the temporal industrial society would move towards an information society. In such an information society, networks would become more important than social hierarchy, and a smaller organization or individual human capability would be more significant than larger ones.This trend suggests that corporate competitiveness supporting economic growth is achieved by ‘virtual resources’ such as individual knowledge and wisdom rather than real financial capital such as plant or land (Naisbitt, 1982). An ‘innovation’ is realized by human knowledge and wisdom and becomes the organizational tool used to pursue corporate strategy with actual behaviors. During the last ten years of the 20th century, we witnessed the transformation of an industrial society towards a knowledge-based society. For instance, even though the real property of Microsoft was only one twentieth of the size of General Motors, Microsoft’s stock price attained a total three times that of GM. There are many large companies that leverage their real properties to progress more towards the knowledge -based market, and it is probable that these

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results accrue from excellent management such as the ‘perpetual innovation’ that brought about Microsoft’s prosperity (Gates, 1996). In a knowledge-based society, the most important managerial resource is people. In an industrial society, resources; people, capital, materials, and information were valued in equal measure. However, the wisdom of utilizing corporate resources was generated by people. This meant that people became the first prioritized resource in the knowledge-based society. What kind of corporate management is required for the new knowledgebased society? In Naisbitt’s view (1982), the aim of management in a knowledge-based society is to maximize the capabilities of the small organization and the individual person. In other words, management’s target is to utilize the network rather than the hierarchy which was previously a major factor in a conventional management style. We call this new management style ‘innovative management’. Innovative management does not premise the conventional hierarchical type of management model but uses a ‘holonic’ type, whereby a large-scale empowerment of individual employees is secured in order to respect his or her autonomy and to stimulate his or her originality, which differs from the conventional model of employee governing style as shown in Figure 1. While corporate ‘effectiveness’ is pursued by governing employees in the hierarchy management model, the holonic type model maximizes corporate wisdom through promoting the ‘autonomy’ and ‘originality’ of each individual employee in the knowledge based society. Therefore, the fundamental aim of the holonic management is to stimulate the collaboration between corporate people and to improve wisdom by inducing each employee’s invention and unique personal characteristics. How will the corporation manage individual behaviors or thoughts under the circumstances prioritizing a promotion of individual capability? Innovative management does not principally conduct the ‘micro-management’ for each em-

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Figure 1. Holonic Type (innovative) Organization and Holon (Kamoshida, 2004)

ployee such as checking their daily work, rather, it embodies macro-management where corporate aims or vision are transferred to employees as a managerial concept which leads to employees being oriented towards accomplishing their target (Lisaack and Roose 1999). Ilia Prigogine, the Nobel laureate in chemistry, proposed ‘Dissipative Structure Theory’, in which a small fluctuation in a system sometimes causes the ‘Sudden Collapse’ of the whole system. This kind of collapse is focused in the ‘Complex System Research’, and covers various areas such as the sudden extinction of dinosaurs, the emergent destruction of an ancient civilization or an unpredictable drop of stock price are explained by the theory. By applying the theory to ‘Innovative Management’, it is suggested that organization evolution through innovation slightly differs from its collapse risk (Tasaka, 1997).

DEFINITION OF INNOVATIVE MANAGEMENT The ‘holonic’ approach to innovative management was originated from the concept of the ‘holon’ as advocated by Arthur Koestler (1979). Though the

terms of ‘holonic management’, ‘holonic pass’, or ‘holonic system’ have sometimes been found in management theory or in an organizational theory, its origin is a concept of ‘sub-wholes = holon’ as proposed by Koestler, where he noticed that the organism was not only relevant in a biological system but also in a social system and is not an aggregation of elementary parts but is regarded as the hyper ‘parts’ which are displayed selfsustainably in the whole. The ‘holon’ overcomes the dichotomy between the part and the whole. Every holon possesses the dual properties required to compensate each other and the parts simultaneously exist by asserting its individuality, without subsiding situation to the whole. In other words, holons should exist as self-assertive parts which functionally affecting the whole and contain integrative properties that coordinate with every part. The holon concept is translated to the corporate organization theory as follows. While every employee in the Holonic Organization is able to conduct innovative work with autonomy and contains a large scale discretion for employees, all employees should embrace loyalty to the organization strategy and management concepts, and should steadily coordinate them towards the corporate policy. As schematically shown in

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Figure 1, every employee sufficiently understands their individual goal and produces various kinds of effort for invention in order to fulfill their responsibility. The employees are constantly made aware of top-management strategy and policy, and sometimes aggressively exert their influence to the top-management itself. This behavior model between the organization and each individual employee creates vibrant organization and results in maximized enterprise capabilities. Therefore, the key premise to empower employees is located within the preparation of management framework which enables a top-management strategy to each employee. Based on the management framework, a substantial relationship between an individual person and organization through the system of business target setting’ has to mach various mission levels that are subjected to each section or individual person. One example of empowering management is ‘Management by Objectives’, a concept which was first advocated by Peter Drucker (1954) is shown in Figure 2.

PROBLEMS THAT PREVENT INNOVATION Leading industries sometimes lose their market leadership when they faced with destructive

changes that occur within the technology field or in market structure, even though they have listened to the customer’s opinions and invested in new technologies. Harvard Business School professor Clayton Christensen called it, ‘The Dilemma of Innovation’. In his book of the same title, (the original title was ‘The Innovator’s Dilemma’), he discusses the possibility where a company, who not only maintains good relations with customers, but also has an excellent management system, is rivaled by competitors and is forced to depart the market (Christensen, 2003). The dilemma of innovation offers the following three suggestions. First, neither technology nor products can survive in the market forever. Second; no one can predict in advance, either a future star technology or a star product in the mainstream of the next generation market. Third, successful companies who know the life cycles of the technology and product are akin to adhere to their individual successful experiences. Companies who have made great strides in technological innovation often commit the ‘three follies ‘such as: 1. The technology or the product does not exist in the mainstream market after the completion of their innovative activity; 2. The technology and the product have no competence in the market;

Figure 2. MBO in organization structure (Kamoshida, 2004)

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3. The companies do not seriously work for next generation technologies and products because of their persistence in the existing mainstream market. It is essential to not ignore the problem caused by such reasons as: ‘We really do not know the situation of the future market.’, beside that, the problem of paternalistic management such as: ‘Having to wait a long time because any innovation usually takes a lot of time and we dare not start that’, or: ‘ The innovative work is now underground, but in the future it may molt into a star product’, this allows the postponement of managerial decision-making, and rears the problem that: ‘We have to first focus thoroughly on the treading of technologies or products, so we must not address any innovation of which the outcome is currently ambiguous.’, or the problem of the ‘endorsement’ based on shallow knowledge and easy decisions such as, ‘Technologies and products that are never going to become a future mainstream product’. These whole problems are apt to be serious obstacles for the company’s operation associated with flexible management strategy as well as ‘change’ -preventing activities.

HOLONIC MANAGEMENT TO CREATE INNOVATION In business itself, once managers reflect that, everything flows to maintain the ‘status quo’ and falls into the extrapolative extension of the current situation. That is, for better or worse, resulted from a human being’s weaknesses. Many ‘Innovations’, without looking at an analogy in biological evolution, were born in the parochial area, that is an ‘off-centered’ field, or from ‘heretics’. This fact is in accordance with the complexity theory which says ‘fluctuation’ occurs at the ‘chaos edge’ and the fluctuation resonantly generates the evolution of the whole system. Thus, one of the most important roles of a CEO is to focus on how to

incubate the shoots of innovation and to nurture them healthily without ‘arbitrarily’ breaking them. A ‘change agent’ is at anytime heretic and relatively minor in any company. A CEO is needed to enthusiastically prepare the environment for the change agent, in order to create change and to help the innovation shoot grow in the same way as that of a ‘newly born life’. ‘Zero degree management’ by Kuniaki Hanamura (2000) states ‘What CEOs have to do is environment preparation for the ‘field’ in which intelligence is created from anywhere in the ‘Intelligent field’, that means new construction of a holonic environment activated by the intelligence without arbitral manipulation for proactively obtaining ‘intelligence’. In other words, the ‘Zero degree management ‘is an ‘arbitral treatment-free management’, or an ‘ideology free-management’. Figure 3 shows the schematic illustration for the ‘Zero degree management’ (Hanamura 2000). Under the heretic-tolerant CEOs, a shorter approach with regards to generating the changing dynamics in the enterprise can be achieved by embracing innovation seeds that have multiple potentialities. This means that managers are required to wait, and observe without a precautious CEO’s thoughts intervening until the internal innovation seeds or entrepreneurial ideas grow into a shoot. From this point, innovative or entrepreneurial ideas are chosen by their ‘outcomes’ and unsatisfied ones are disregarded, without expecting any form of comeback. This ‘clear-cut edge’ management conducted with leadership is highly important in order to create innovative and new products. It suggests that frontage for new project ideas and proposals should be as wide as possible, and that many employees and organizations should be encouraged to engage in creative activities, which are imposed to be cleared by everyone within a certain guideline, so that the company is able to obtain the field for creation of actual ideas or new business with enough competence. This is the thought of ‘Holonic management’ in itself.

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Figure 3. Concept of the Zero Degree Management (Hanamura, 2000)

A SIEVING MECHANISM TO PROMOTE INNOVATION A management that strives to create innovation needs the following essential process elements: 1. Cultivation of the organization culture which can grow innovation shoots; 2. The introduction of an appropriate competition principle; 3. The preparation of a strict evaluation and proper supporting system. Above all, the corporate organization culture is not to be built overnight, but to be carefully nurtured over many years. Because the organizational culture is built on an integrated set of values, which are owned by each employee embracing it as a common standard and it can take many years to behave unconsciously based on this standard (Schein 1999). The term ‘chaos edge’ is used in complexity studies and is adaptable to the management field as is mentioned above. The chaos edge represents neither complete order nor complete disorder, but rather an intermediately ordered situation, and the mostly ‘bio-scientific’ starting point of ‘evolution’. That means that innovative management should foster the ‘evolution fluctuation’

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as an appropriate visual form which is born at the ‘chaos edge’, and should be met by a strict selection policy which prunes them. This aspect shows that innovative management is nothing other than ‘complexity management’. How can we select and prune new ideas or business plans in management? The selection system is termed here as the ‘sieving mechanism’, and it adapted to the process elements 2 and 3 above. As for the process involved in process element 1 above, cultivating the organization culture to nurture innovation is typical of the model: ‘Easier said than done’. The top managers should conduct their leadership to foster the corporate culture which accepts heretics and the need to renew a conventional organization culture based on the ‘principle of precedent’, the’ merit system’, or the ‘seniority system’, which can often leads the corporation astray. Managers should also construct a framework which allows excellent project ideas and proposals to be discussed with the managers’ initiative. The top manager’s responsibilities often infer many obstacles in big corporations. Some traditional corporations are extremely quick to make mistakes in which entrepreneurial ideas are obstinately evaluated by only upper-class managers such as department heads or boards of members. The ultimate decision-making, of course, should be done by management executives.

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However, many of these executives must not make their own rash assessment of the market; in other words, ‘the voice of the market’ would ensure that they are more appropriate judges. What is an important factor for companies is the ‘Everyone to his trade’ idea, which message should be spread throughout their corporation. The executive committee members are only good enough if they have excellent management strategies and good professionalism, and not necessarily an innovative sense of the market. It is their responsibility to find experts that exist not only internally but also externally, and the opinions offered by these people will be reflected on the managerial ideas and decisions. In-house, with a strong focus on the younger market, setting up the field for these young players will produce a strong sprout of innovation.

THE SIEVEING MECHANISAM AND ITS METHODOLOGY In process element 2 above, in order to introduce an appropriate level of internal competition, management must firstly seek to ensure an unrestrained environment and foster creative activities regardless of an individual’s official position and role. Secondly, during a period of given time, it is necessary for managers to allow a redundancy in multiple activities towards a similar objective. In many companies, specifically in traditionally large companies, each sector, or each position determines the official duty matters in detail. However, a novel or innovative idea is sometimes born at a cross-functional area, or in an off-mainstream job. In companies, similar approaches to the same target are often carried out in multiple sections. The consolidation of these similar activities has to be achieved after a manager’s deep and careful consideration, because from the effectivenessweighing standpoint, consolidation seems more effective. However, internal competition generates

the possibility of enhancing more external competence enhancement, and the prospect of more interesting and more novel products. Whether this in-house competition is accepted or not, of course, depends on an individual corporate physical fitness; we call it ‘risk-toughness’. As a result, in process element 2, rapid consolidation or evaluation for the internal new activities is not a first step, but an assessment for the coherency between corporate integrity, vision, or whole strategies and innovative activities. Due to the nature of how novel ideas and entrepreneurial plans are expected, too large a gap against corporate vision should be rejected. In process element 2, visualization of the innovation outcome using an evaluation method with numerating qualification is effective. In general, the company’s new investment is settled and distributed under an enterprise-wide portfolio. For each new business issue raised, exactly how investment is allocated is determined by the company’s overall business strategy, the ‘coherency’, which pits the ‘prioritization’, the ‘risk toughness’ against the operating loss, and finally by top ‘management decision’ making. The important factor here is, according to the concept of holonic management, the business plan ‘frontage’ being opened as wide as possible to a definite stage, and above that stage to a priority process to precede mostly competitive business with focusing on investment that needs to be established. A classic example of a business failure is the case of a priority embarking on a ‘predictable business’ helping the innovative plan. For example, when required to choose the most appropriate plan among many candidates for new business, the ‘project evaluation’ or ‘business plan’ is chosen in an excessively simplistic manner and requires a huge investment, resulting in a lot of failures. This means that failure is generally resulted from improper assessment and an urgent expectation to start a new business. In response to the failure, the ‘objective assessment’ for the ‘consequences’

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incorporated in the sieving process would vastly increase the possibility of success. In today’s corporate management, no matter how great a strategy appears, or how novel an idea seems, there are many examples when it is actually conducted (= ‘Execute’), the outcomes (= ‘Results’) have not been satisfactory. That may be caused by a problem within the organizations management, a problem with improper business speed, or a problem due to low technology capability and poor cost structure. The most important point in process element 3 is critical screening or the sieving of the most expectable business plan among its many candidates. In order to do that, a discriminative standard or threshold value between ‘necessary conditions’ and ‘sufficient conditions’ should be built. The ‘necessary conditions’ to sieve are achieved by settling a numerating screening standard and conducted ‘objectively’ with ‘focusing results’. For instance, ROI (Return on Investment) or turnaround from a loss in a single fiscal year surplus is accepted as one of the numerating thresholds. Or, even though the investment has been recognized following process element 1 ‘single-year surplus within three years, and, if within five years wiping out the accumulated losses could not be achieved, the project would be automatically shut down’. ‘After three years elapsed, the top five projects on the maximum rate of ROI are screened’ with the necessary criteria which should be built before embarking on new businesses. In the business strategy forming and planning process, a break-even analysis is essential when starting a new business. However, many reviews on cases studies of failed new businesses sometimes show a very shallow analysis on the break-even point. In that case, ‘predictable business’ was allowed to launch without critically analyzing the business building that results in a consisting figure when the predictable business was arbitrarily made (Aaker, 2004).

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A ‘SIEVING MECHANISM’ FOR INVESTMENT DECISIONS In the following section, sufficient conditions to sieve the innovative plan are discussed. In the previous section, it was concluded that to sieve ‘necessary conditions’ require numerical threshold values and a selection process has to be conducted ‘objectively’ by focusing on the ‘result’. The necessary condition aims to avoid an arbitrary choosing of any innovative ideas and prevent wasteful investment merged from corporate compassionate intention for employees. Therefore, ‘sufficient conditions’ for screening relies on not just coherency between company-wide strategies, but also involves investment decision requirements by prioritizing new business plans on the premise of investing the capacity of the corporation. In general, ‘necessary conditions’in the above criteria such as ROI and profitability are adapted from ‘financial perspectives’ and ‘cutting foot=cutting off below the threshold value’ oriented. On the other hand, the ‘sufficient conditions’ settled threshold value has been adapted to e company strategies as a whole and to the amount of investment associated with evaluation to the ‘risk toughness’ from the ‘financial perspectives’ as well as the strategic standpoint of the ‘non-financial view’. As a result of these actions, top management can finally strategically select the most prioritized plan strategically by using an objective assessment of the financial perspective, taking advantage by ‘cutting off below the threshold value’ properties of the numerating evaluation.

CORRECT RECOGNITION OF THE STATUS QUO The ‘Essence of Failure’, which analyzed old military organizations and operations, states that sectionalism in the organization first prevented information exchanges by forming an information barrier, which meant that information from front-

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line to upper administrative levels had not been carried out exactly (Tobe et al., 1984) In one of the failed factors, as pointed out by the ‘Essence of Failure’, the way that the information barrier is easily apt to induce an incorrect recognition of status-quo suggests ways to the current enterprise management can improve. The most important aspect of this is for top-management decision making on the dismissal of strategy, enabling them to assess the correct recognition of the corporate situation. How could managers take the correct recognition of the corporate situation? In the following section, it is explained which key factors are required to obtain situation recognition and the effect of numeration for the dismissal process with the other concerned items.

THREE RECOGNITION FACTORS OF THE STATUS QUO The following three factors have to be grasped by top management in order to allow the correct status quo’; 1. In the past, how much resources have been invested in the business? (→ grasp of the ‘past invested amount’); 2. Currently, how much outcome has been proven? (→ grasp of ‘strategy achievement’); 3. In the future, how much additional investment is required in order to obtain a successful result? (→ grasp of ‘additional input’). A correct perception on the status quo should be applied highly cautiously to the factors in 2 and 3. As for factor 1, it is not difficult to show information objectiveness on the ‘past invested amount’ because the amount is basically composed of invested money and human resources. The factors 2 and 3 are vital, as the act as a precautious for the recognition processes, as some additional evaluations depend on multiple examiners’ aspects. As for factor 2, the ‘strategy achievement’,

a numerating index such as a ‘financial index’, as well as non-financial indicators of the ‘customer index’ or ‘business process index’, and ‘learning and growth index,’ with the comprehensive operation review will give them a rough comprehension of the strategy progress required. In general, obtaining a strong knowledge of business issues is achieved by examining sales results and profit margins as ‘financial index’, as well as market share and defect rates of ‘non-financial indexes’. With regards to factor 3; ‘additional input’, since this is an area that focuses on expected future potential, it is the most difficult to obtain objective data, and will vary with each managers’ own assumptions. Therefore, the company’s ‘risk toughness’ has to be taken into consideration, as it is desirable to show the investment limits in advance as marginal investments.

BUSINESS WITHDRAWAL MANAGEMENT When a strategic mistake is apparent, management promptly dismisses the strategy or makes a sharp shift away from it. The ‘dilemma of innovation’ has pointed out this most obvious matter, but also that it is the most difficult to implement. When top management ultimately makes the decision to withdraw from a strategy, it is not to say that the comprehensive decision must be resulted from a variety of perspectives. However, a ‘comprehensive decision’ sometimes takes place too late in the process for managerial to withdrawal their decision so that the in-house management frame for retreat must be built in appropriately. The author calls it ‘Business Withdrawal Management’. The following five items are the actual framework for the managed withdrawal: First, the objective evaluation for the status quo and the ‘strategy trigger’ are to be thoroughly prepared. A numeral index is a very useful tool for objectivity when qualifying whether the initial progress for the strategy has been achieved at an expected level

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or not. It is a natural characteristic for those who propose and promote the strategy to adhere to their proposed strategies. It is vital that managers who are required to make any final decisions, should not be l be overwhelmed by compassion for the project, as well as not misjudging the best time to withdrawal. For instance, in the case of starting a business, where contrary to initial speculation, the market had cooled off, so the recovery of initial investment could not be expected for a certain period of time, the built-in strategy trigger such as ‘surplus turnaround within three years’ or ‘ROI of 15 percent or more’, makes it possible to issue the decision to strategically pull out, thus not missing out on the optimal timing. Second, the strategy team should not confuse the ‘empowerment’ with ‘management responsibility’. The ‘Holonic management’ inevitably relies on empowerment of sections and individual employees. However, managers who conduct the empowerment have to keep watch at all times as it’s their responsibility and they should not hesitate to intervene if and when necessary. That is not micro-management for daily operations, but macroscopic management that is required to orientate the whole project. Third, in the case of a critical decision for a comprehensive strategy, governance of outside directors as well as shareholders has to be executed as effectively as possible. ↜Considering the dismissal of strategy, it is sometimes hard to execute effectively because that strategy involves a relationship between the concerned people who proposed the project and the people who actually promoted it. Compassionate feeling and consideration for them may cloud their judgment, making it fairly difficult to conduct the final decision. In this case, an in-house framework in which advice from outside directors and from non-concerned third parties can be reflected in the final decision and should be built well in advance of the project commencing. Fourth, the ‘risk comprehension’ related to financial loss with assumptions of an emergent

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withdrawal should be prepared. The failure inherently exists in the strategy. Everyone, of course, commits to the success under individual obligation. However, when it fails unexpectedly, the risks resulted in failure have to be firmly grasped. In particular, the size of any financial losses caused by the business failure should be accurately assessed in advance, and the ‘risk toughness’ process has to be examined in order to escape from the worst risk case scenario. Fifth, in the event of an unfortunate decision to withdraw from a project, supporting and helping them to learn from their failures is essential. It is obvious that any failure, even if it is only temporary, can result in a huge blow to an organization. Financial loss, time loss and spiritual loss are all aspects. Once the strategy is rejected and the strategic withdrawal decision has been issued, managers have to make a conscious effort to heal any mental damages that may have been caused and ensure a fast return of the people concerned as soon as possible, by promoting the next challenges (Holtshouse, 2001). In addition, it is almost impossible to say that the any previous failures will provide a clue to the next success, so all the people it concerns must face up to the failure, and show a strong willingness to learn from it, acting as a safeguard to any possible future failings.

CONCLUSION This paper proposes a ‘Holonic Management Model’ with respect to organization management, which is able to create an innovation. In order to create an innovation, appropriate leadership is essential. However, not only leadership is required to evoke an innovation. In order to realize its full potential; management, the organization culture and operations should find the best use of the persons who fulfill the role of change-agents, who are not tied to traditional fixed ideas, but have new thoughts and different values from that of the older ones. These ideas are necessary. This

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new management concept is suggested by the theory of complexity. In innovative companies such as Google, or Apple, a kind of the holonic management has been inherited as its DNA and has embodied in itself. Our future studies will be focused to the exemplification of the holonic management in real firms and attempt to induce a universal solution for the knowledge based society.

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About the Contributors

Alan Eardley is the Director of the MRes Scheme in the Faculty of Computing, Engineering and Technology at Staffordshire University, as well as being Associate Head of Postgraduate Research Studies. He has considerable commercial experience in management and systems analysis in a variety of industries, and has worked as an academic for over twenty four years in schools of business and computing. He has participated in a number of industrial collaborations and consultancy projects and has co-authored five text books on IS and the use of IT in business. Eardley has a first class honours degree in Business, a Masters degree in Computer Science and a PhD in Strategic Information Systems. His current research into knowledge management, research methods and strategic planning in business and healthcare applications has resulted in a number of publications in international journals and conferences. Lorna Uden is Professor of IT systems at the Faculty of Computing, Engineering and Technology in Staffordshire University. Her research interests include Technology Learning, HCI, Activity Theory, Knowledge management, Web Engineering, Multimedia, E-business, Service science, semantic web, and Problem-Based Learning. Professor Uden is program committee member for many international conferences and workshops. She is on the editorial board of several international journals. Professor Uden is also visiting professor to universities in Australia, China, Finland, Italy, Malaysia, Slovenia, Spain, South Africa and Taiwan. She has been keynote speaker at several international conferences. On the international front, she collaborates widely with colleagues worldwide. *** Hisbel Arochena is a Senior Lecturer in Computer Sciences in the Faculty of Engineering and Computing, Coventry University. Prior to this appointment, she was working on internet-related applications in the business sector and as an Associate Lecturer in the University of Havana, Cuba. Her work dealt with the mathematical modeling of tide, storm surges, greenhouse effect and other oceanographic processes. She has published journals and conference papers on the optimization of bi-dimensional cutting, as well as on the greenhouse effect and the mathematical modeling of tide. Her current research interests fall within the area of applications of statistics and optimization to medical research. Rajeev K. Bali is a Reader in Healthcare Knowledge Management at Coventry University. His main research interests lie in clinical and healthcare knowledge management (from both technical and organizational perspectives). He founded and leads the Knowledge Management for Healthcare (KARMAH) research subgroup (working under BIOCORE). He is well published in peer-reviewed journals and

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About the Contributors

conferences and has been invited internationally to deliver presentations and speeches. He serves on various editorial boards and conference committees and is the Associate Editor for the International Journal of Networking and Virtual Organizations as well as the International Journal of Biomedical Engineering and Technology. He is currently working on his sixth textbook. Vikraman Baskaran is an engineer by profession with an interest in Biomedical Computing. His research interest is on finding a viable application of the KM paradigm in healthcare application. His special interest in developing HL7 messaging and healthcare informatics has provided opportunities in excelling in these fields. He is a member in HL7 UK and Canada. His current activities overlap KM, e-Health, AI and healthcare Informatics. He has been teaching at graduate and undergraduate level as lecturer at Coventry University since 2003. Prior to this, he has been engaged in a wide area of industrial projects (software and engineering at senior levels). Hilary Berger is a Senior Lecturer in the Cardiff School of Management at University of Wales Institute Cardiff (UWIC) where she currently combines teaching duties in parallel with extended research activities. She has a breadth of teaching expertise and experience across a variety of subject areas and audiences within both the private and public sectors. Her research within the Information Systems domain has been published widely in both European and International academic arenas. Mihaela Dan is Associate Professor, PhD, at the UNESCO Chair for Business Administration, Academy of Economic Studies, Bucharest. She teaches Customer Relationship Management, Communication in Business and Media, International Business, in English and German. Her research interests include: Relational Management, International Marketing, Organizational Behavior. Paul Beynon-Davies is professor of business informatics at Cardiff Business School, Cardiff University. Before taking up an academic post, Beynon-Davies worked for several years in the Information Systems industry in the UK. He still regularly acts as a consultant to the public and private sector particularly in the area of ICT and its impact on organisational performance. He has researched and published widely in the field having 11 books and over sixty academic papers to his name. BeynonDavies has engaged in a number of projects related elated to the impact of ICT on the economic, social and political spheres, both in the UK and Europe. Constantin Bratianu is professor of Strategic Management and Knowledge Management at the Academy of Economic Studies, Bucharest, Romania. He is the Head of UNESCO Department for Business Administration and Director of the Research Center for Intellectual Capital, Academy of Economic Studies, Bucharest. His main academic and research interests are knowledge dynamics, knowledge management, intellectual capital, strategic management and university management. He is co-editor of the academic journal Management & Marketing, and co-president of the annual International Conference on Business Excellence, Brasov, Romania. He has held the position of General Director for Higher Education in the Ministry of Education and Research in Romania (1998-2000), and Expert for Science and Education in the Romanian Parliament (2001). He has been Visiting Professor at the University of Applied Sciences, Steyr, Austria at Our Lady of the Lake University, San Antonio, Texas, USA and at the Tokyo University of Science and Technology, Kobe University, and Osaka University, Japan.

375

About the Contributors

Anikó Csepregi is a PhD student at the School of Economics and Management at the University of Pannonia, Hungary. Her main fields of interest include knowledge management and knowledge-sharing. Won-Chen Chang received his BS in environmental engineering from the National Cheng Kung University, Taiwan and an MS in environmental engineering from the George Washington University, USA in 1993. He obtained his PhD degree in the Department of Industrial and Information Management at National Cheng Kung University, Taiwan, 2008. He works at Metal Industries Research and Development Centre and has been responsible for chief positions in several sections including industrial information analysis, knowledge property management, and promotion and information. His research interests include knowledge management, patent engineering and industrial information analysis. Ching-Wen Chen is an associate professor of the Department of Information Management, National Kaohsiung First University of Science and Technology, Kaohsiung, Taiwan, R.O.C. He received his PhD in production and operations management (in Information System and Quantitative Sciences) at Texas Tech University, Lubbock, TX. Chen spent his time in studying quality issues, such as the topics concerning service quality and user satisfaction, including the information systems success measuring by the user satisfaction. He also worked in the e-commerce, fuzzy logic related topics and expert systems. His research has appeared in International Journal of Quality & Reliability Management, International Journal of Information and Management Sciences, Engineering Economist, Quality Engineering, Journal of Electronic Commerce in Organizations, and Expert System with Applications. Elayne Coakes is a Senior Lecturer in Business Information Management in the Westminster Business School, University of Westminster. She is Module Leader of a number of key modules in this area. Her research interests lie in the socio-technical aspects of information systems, the contribution of the stakeholders to the process of strategic planning for information systems and in particular knowledge management. Her current research relates to knowledge sharing in sustainable science. As a member of the BCS Sociaotechnical Group she is active in promoting this view of information systems and has edited three books of international contributions in the field, including, ‘The New SocioTech: Graffiti on the Long Wall’ (2000), ‘Knowledge Management in the Sociotechnical World: the Graffiti Continues’ (2002) and SocioTechnical and Human Cognition Elements of Information Systems (2002). In addition, ‘Knowledge Management Challenges and Issues’ was published in 2003. Angelo Corallo is a Researcher at the eBMS of Scuola Superiore ISUFI. His research and teaching activities are focused on technology and organizational strategies in complex industries. He is involved in activities related to knowledge management and collaborative working environments in project and process-based organizations, with specific reference to the aerospace industry and interest towards languages, methodologies and technologies for knowledge modeling. He has coordinated efforts in the EU research programs for DBE (Digital Business Ecosystem) and OPAALS (Open Philosophies for Associative Autopoietic Digital Ecosystems). Marco De Maggio has a PhD in “e-business” at eBusiness Management Section of Scuola Superiore ISUFI at the University of Salento, Italy, a Bachelor Degree in Economics at University of Lecce, and is a Chartered Accountant. His research field concerns the development of Methodologies for the analysis and management of Organizational Learning Patterns inside Organizations and Communities of Practice.

376

About the Contributors

As a Visiting Scholar at the Center for Digital Business of MIT, he experimented with the application of Content and Social Network analysis supported by computer-aided systems for the improvement in the analysis of Virtual Communities. Lecturer at the Faculty of Engineering of the University of Salento and in Master's and PhD programs of eBMS – ISUFI and in corporate education programs such as the FHINK Master's of Finmeccanica. Alina Mihaela Dima is Associate Professor, PhD, at the UNESCO Chair for Business Administration, Academy of Economic Studies, Bucharest. She teaches International Business and Negotiations, in English. Her research interests include Competition Policy, International Commercial Law, and Organizational Culture. Gianluca Elia is an Assistant Professor at the University of Salento (Italy), and he is strongly involved in the higher education programs and research activities of Scuola Superiore ISUFI. His research interests concerns the “Learning, Innovation and Value Network” research field, with a specific focus on innovative methodologies, strategies and tools enhancing collaborative learning and knowledge management. He is also the coordinator of the Mediterranean School initiative, a program launched by Scuola Superiore ISUFI in Southern Mediterranean Countries aimed at creating a network of Competence Centres specialized in Digital, Organizational and Strategic Innovation. He is also involved in the management of complex research projects, in collaboration with leading companies, universities and research centres. He had a major role in the design and implementation of the Virtual eBMS, a technology platform integrating knowledge management and web learning applications. This platform was awarded the Brandon Hall Research prize in learning technology in 2006. Many book chapters and publications in international journals and conference proceedings document his research activities. Zoltán Gaál is an acknowledged expert in the field of enterprise theory and practice. He is Professor of the Department of Management of the University of Pannonia, Veszprém, Hungary. He is member of Science and Arts Habilitation Committee of Budapest Technical University, board member of Scientific Society of Management, member of Arts Habilitation Committee of University of Miskolc, elected member of Expert Committee of Management Science, Hungarian Academy of Sciences, elected CoPresident of Union of Technical and Scientific Societies of Veszprém County, chairman of Corporate Cybernetics Task Force of the Veszprém Academic Committee of the Hungarian Academy of Sciences. He has published numerous articles and presented his work at national and international conferences. His main fields of interest include strategy management, corporate culture, maintenance management. Francesca Grippa is an Assistant Professor in Management Engineering at the e-Business Management Section, Scuola Superiore ISUFI, University of Salento, Italy. Her current research interest is in applying social network analysis to business and learning communities. Grippa holds a PhD in e-Business Management and an MA in Business Innovation Leadership from the University of Salento and a BS in Communication Sciences from the University of Siena, Italy. In 2005 and 2006 she was visiting scholar at the MIT Sloan Center for Collective Intelligence. Aziz Guergachi is an associate professor in the Ted Rogers School of Information Technology Management at Ryerson University. Prior to becoming part of the Ryerson community, Guergachi was involved in the development of a large software system for trade promotion management and collab-

377

About the Contributors

orative sales forecasting. His current research interests lie in the fields of advanced system modeling and machine learning with applications to business management and engineering systems. He is the recipient of the New Opportunities Award of the Canada Foundation for Innovation, and currently runs a research Lab for Advanced System Modeling. Chao-Hen (Weissor) Hsueh obtained his MBA in the Department of Business Administration from SooChow University, Taiwan. Currently he is an assistant professor in Department of Finance at National Kaohsiung First University of Science and Technology, Taiwan. His research and teaching interests are in financial analysis and knowledge-based systems. He is also a senior CPA and has in depth expertise in financial statement analysis. Akira Kamoshida is a Professor of Center for Innovation Systems Research ,Tokyo Institute of Technology. He is a visionary leader of E-business and Networked Society, having written many books and articles on magazines like Harvard Business Review. He holds a BE from Yokohama National University, an MBA from Keio Business School. He was also educated at London Business School. The main themes he has focused in are IT enabled knowledge society and transformation of business, enterprise and industries. His current research themes and interests are Service Science, Innovation Management and Computational Modeling. Ah-Lian Kor is a Postdoctoral Researcher and Lecturer at Leeds Metropolitan University. In 2001 she obtained a PhD from the University of Leeds for research work entitled “A Computer Based Learning Environment for the Exploration of Buoyancy”. Her diverse research interests are: information and e-Government systems; innovation systems; and the investigation of reasoning and learning styles users adopt when interacting with computer-based learning systems. Her research also includes modeling qualitative understanding based on semantic networks or causal maps. She has also published papers in qualitative spatial reasoning and is a paper reviewer for: American Conference of Information system, AMCIS; Journal Information Technology and Politics (JITP); Special Issue of Government Information Quarterly: An International Journal of Information Technology Management, Policies, and Practices. She is a Committee Member for the International Conference of e-Government (ICEG) 2008-2010 and Co-Chair of the mini-track entitled “Public Sector ICT & Innovation”; Program Committee Member for e-Democracy 2009 Conference; Technical Committee Member for Cyberlaws 2010 Conference. She is a Special Guest Co-Editor for the journals Library History and Transforming Government: People, Process, Policy. Zoltán Kovács is Assistant Professor at the Department of Management of the University of Pannonia, Hungary. His main fields of interest include intercultural management and organizational culture. He completed his MSc in Entrepreneurial Engineering and Management at the University of Pannonia in 2001 with a focus on strategic challenges of international companies. Since then he has worked as a researcher with co-operation of THT Intercultural Management Consulting, Amsterdam completing a survey on national and organizational culture in Hungary in 2005. The research project surveyed the peculiarities of the Hungarian national culture as well as the relations of its attitudes on the one hand and the connection between national culture and competitiveness on the other. He obtains his PhD in Economics and Management in 2007.

378

About the Contributors

Sheng-Tung Li received his BS and MS degrees in computer engineering from Tamkang University, Taiwan, and his PhD in computer science from University of Houston, University Park, Texas, USA. He is currently a professor at the Institute of Information Management and Department of Industrial and Information Management at National Cheng Kung University, Taiwan. Li is author/co-author of five books, one patent, over 50 journal articles, and numerous conference papers. His research interests include knowledge management system, knowledge engineering, data mining, and soft computing. Marianne Loock is a Senior Lecturer in the School of Computing at the University of South Africa, focusing on the lecturing of Software Engineering, Digital Logic and Information Security. Loock is interested in research that focuses on Information Security and access control, Data Base Management of very large data bases as well as Knowledge Management in Enterprises. She has supervised Master students successfully within the field of Computing. She is also part of the Information and Computer Security Architectures (ICSA) research group at the University of Pretoria where she is a registered student and in the final stages of her PhD in access control within a Data Mining environment. Loock lives in Pretoria, South Africa with her husband Johan and children Corlia and Werner Rémy Magnier-Watanabe is Assistant Professor in the MBA Program in International Business at the Graduate School of Business Sciences of the University of Tsukuba in Tokyo. He graduated from the Ecole Supérieure de Commerce de Grenoble in France, holds an MBA from the Georgia Institute of Technology in the United States, and received his PhD in Industrial Engineering and Management from the Tokyo Institute of Technology in Japan. His present research focuses on knowledge management, institutionalization processes, and cross-cultural management. Alessandro Margherita is a Researcher in Business Engineering at the eBMS – Scuola Superiore ISUFI (University of Salento). He possesses a PhD in e-Business Management and his research is characterized by a cross-disciplinary focus, with a major interest towards areas such as organizational innovation, process engineering and organizational learning. In 2006 he was visiting at the Center for Digital Business of MIT (USA). At the eBMS, he's involved in a laboratory focused on new product development processes in aerospace. He's a lecturer at the Faculty of Engineering of the University of Salento, and since 2004 he's regularly involved as teacher in Master's and PhD programs of ISUFI as well as in corporate education programs such as the FHINK Master's of Finmeccanica. Raouf N.G. Naguib is Professor of Biomedical Computing and Head of BIOCORE. Prior to this appointment, he was a Lecturer at Newcastle University, UK. He has published over 240 journals and conference papers and reports in many aspects of biomedical and digital signal processing, image processing, AI and evolutionary computation in cancer research. He was awarded the Fulbright Cancer Fellowship in 1995–1996 when he carried out research at the University of Hawaii in Mãnoa, on the applications of artificial neural networks in breast cancer diagnosis and prognosis. He is a member of several national and international research committees and boards. Marja Naaranoja is a faculty member at Vaasan Ammattikorkeakoulu, University of Applied Sciences (VAMK), Finland, where she teaches project management, use of computers and environmental economics and is responsible of the development of the master program in construction. Her research

379

About the Contributors

area has been use of information technology and knowledge management. She has published about 30 articles in journals or refereed conference proceedings. Nóra Obermayer-Kovács is an Assistant Professor at the Department of Management, University of Pannonia, Hungary. Her main fields of interest include knowledge management, knowledge-sharing culture and knowledge-sharing behaviour. She holds an MSc in Entrepreneurail Engineering and Management. Since then she has worked as a researcher with co-operation of KPMG-BME Academy, Budapest realizing a survey on knowledge management in Hungary in 2005/2006. The research project investigated the characteristic features of knowledge management among Hungarian organizations. She obtained her PhD in Economics and Management in 2008. She has published numerous articles and presented her work at national and international conferences. Graham Orange is a Reader in Information Systems at Leeds Metropolitan University. His research focuses on knowledge management and organisational learning but includes other areas such as HE in FE, business process modeling, IS strategy, IS development, and e-Government systems. Graham supervises doctoral students and teaches on the Faculty’s Masters programme. Prior to joining Leeds Metropolitan University Graham was a professional information systems consultant with one of the UK’s most prestigious firms. He is now an active researcher writing and reviewing journal (e.g. European Journal of Information Systems) and conference papers, conference chair (e.g. Americas Conference on Information Systems and International Conference on E-Government mini track chair), reviewing research council proposals etc. He has links with many other institutions around the world and is Visiting Professor at the University of Malaya, University of Palermo and at the Danube University Krems. He has close links with industry and local authorities with regard to both research and consultancy and has just completed a European project on youth citizenship (POLITEIA) with municipalities in Europe. Dai Senoo is Associate Professor in the Department of Industrial Engineering and Management at the Tokyo Institute of Technology in Japan. He received his PhD from Hitotsubashi University in Japan. His numerous publications, include several books like "On Practice: Knowledge Creation and Utilization", Hakuto-shobou, 2001 written with Satoshi Akutsu, and Ikujiro Nonaka (Eds.). His present research interests are in knowledge management, business development, and workplace reformation. Hanifa Shah is Associate Dean (Research) and Professor of Information Systems at Birmingham City University. Previously at Staffordshire University, she was Director of the University’s Centre for Information, Intelligence and Security Systems and Head of the Information & Knowledge Management Research Group. She has over 30 years in-depth knowledge and experience of both the higher education sector and business. Shah has been at the forefront of the development of industrial links and partnerships for research, enterprise and teaching by Universities and has supervised over twenty PhDs. Her current research includes research methodologies for information systems research with collaborating corporations (ALTAR), organisational and professional development in IT (STEP), the exploitation of mobile technologies in health informatics, and the improvement of patient processes in hospitals. She is investigating enterprise architecture based approaches for information systems and knowledge management (ADaPPT) in areas such as healthcare and e-government. She has published over seventy papers in important information systems conferences and journals such as Communications of the ACM and European Journal of Information Systems.

380

About the Contributors

Peter Sharp is based at Regents College, London, where he is Module leader in Research Methods and Skills and MA Programme Manager. Peter has a Bachelors in Law from Oxford University (Oxon), and a Masters in Computing from Staffordshire University. Sharp is a Member of the UK Academy of Information Systems, the Law Society, the Higher Education Academy, the City Information Group and the Commerce and Industry Group. He graduated from Oxford University and practised as a solicitor before doing a Masters in Computing Science and working in a computer consultancy firm. He is an expert in Knowledge Management and Information Systems, a field in which he obtained his doctorate and continues to conduct research and provide consultancy advice. Sharp has published widely in this field and is on the editorial board of the Electronic Journal of Knowledge Management. He is a professionally qualified lecturer in higher and professional education and is also actively involved in commercial and academic groups in the field of research methods and skills. Li-Yen Shue is a professor of the Department of Information Management, National Kaohsiung First University of Science and Technology, Kaohsiung, Taiwan. He received his PhD in Industrial Engineering from Texas Tech University, Lubbock, TX. His research and teaching interests are in scheduling, system development, intelligent systems, and knowledge management. He taught in Taiwan as well as Australia. His research has appeared in Journal of Operational Research Society, Expert Systems with Applications, Journal of Intelligent Manufacturing, Computers & Industrial Engineering, and Journal of Information Management. Peter A.C. Smith is President of The Leadership Alliance Inc., a consulting practice servicing the needs of public and private sector organizations in the Americas and Europe. TLA designs and carries out consulting assignments, workshops, and training programs aimed at achieving success and sustainability in today's complex and dynamic environments. TLA maintains close relationships with a number of universities and a network of international authorities in academe and practice. Research assignments are undertaken on topics of relevance to the company and its clients and are reported as appropriate to sponsors and/or through the professional literature. Peter is also an Alliance Partner of KonvergeandKnow KM Solutions and of NewMindsets e-Learning Systems. He is also Managing Editor of the Journal of Knowledge Management Practice and Consulting Editor of The Learning Organization. In addition he as a member of the Editorial Review Board of Management Decision journal and is Visiting Professor of Management Learning Processes in the Canadian School of Management and is a past Chair of the International Community of Action Learners (ICAL). Hanlie Smuts is currently an employee of Mobile Telephone Networks (Pty) Ltd (MTN), one of three mobile operators in South Africa. Her research interests include knowledge management and innovation and in particular how it relates to customer experience—and subsequently market share growth—in the mobile market today. Such knowledge also fosters innovation in an environment where all mobile operators in South Africa have access to the same mobile networks, devices and similar products and services. Innovation is a key differentiator and knowledge management processes play a key role in facilitating innovation cycles. Smuts is currently busy with her PhD in the knowledge management field examining specifically how it relates to Information Technology outsourcing. She lives in Johannesburg with her husband André and children Stefan and Corlia.

381

About the Contributors

Lajos Szabó is an Associate Professor and Head of Department of Management at the University of Pannonia, Veszprém. He is founder member of the Hungarian Project Management Association. He has published numerous articles and presented his work at national and international conferences. His main fields of interest include intercultural, project and maintenance management. Alta van der Merwe is a researcher at the CSIR (Meraka Institute) within the HuFEE (Human Factors and Enterprise engineering) Group, focusing on issues affecting the development of socio-technical systems, both within enterprises and for the individual. Van der Merwe is interested in research that focus on elicitation of knowledge, conceptual modeling of knowledge and knowledge management in Enterprises. She is also passionate about the research process and has supervised many Master and PhD students successfully within the field of Computing. She lives in Pretoria, South Africa with her husband Derick and children Christiaan, Bennie and Arina. Simona Vasilache is Assistant Professor at the UNESCO Chair for Business Administration, Academy of Economic Studies, Bucharest. She teaches Cross-cultural Management, Business and Strategic Management, Communication in Business and Media, in English. Her research interests include Knowledge Management, Organizational Culture, Organisational Intelligence. Nilmini Wickramasinghe researches and teaches in several areas within information systems including knowledge management, e-commerce and m-commerce, and organizational impacts of technology with particular focus on the applications of these areas to healthcare and thereby effecting superior healthcare delivery. She is well published in all these areas with more than 100 referred scholarly articles, several books and an encyclopaedia. In addition, she regularly presents her work throughout North America, as well as in Europe and Australasia. Wickramasinghe is the U.S. representative of the Health Care Technology Management Association (HCTM), an international organization that focuses on critical healthcare issues and the role of technology within the domain of healthcare. She is the associate director of the Center for the Management of Medical Technologies (CMMT), a unique research-oriented center with key research foci on knowledge management, healthcare, and the confluence of these domains and holds an associate professor position at the Stuart School of Business, IIT. In addition, Wickramasinghe is the editor-in-chief of two scholarly journals: International Journal of Networking and Virtual Organisations and International Journal of Biomedical Engineering and Technology, where she was also the journals founder, both journals are published by InderScience. Sean Tung-Xiung Wu attempts in his research to prove there is a “Knowledge Spectrum” to bridge physics/materials sciences, biology/life sciences and the social/behavioral sciences. There are continuous linkages between these fields, however, the fundamental theories and measurement tools should not be the same. Wu theorizes “Adoption Models in Humans’ Behavior” to explain his ideas, models which need long-term experiments tracking behavioral evidence. Therefore, he has conducted research on “Internet Users' Behavior” and “Voters' Behavior” for almost thirty years. He also has developed a KM system to manage his longitudinal data that might contribute to a new paradigm after observing full cycles of humans’ certain behaviors. Wu has been a faculty at the Department of Information Management of Shih Hsin University and was the founding chair. He is also an affiliate faculty for the National Taiwan University and a leading coordinator of an international research team for Georgia Tech in the U.S..

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Index

A accounts_receivable_turnover (ART) 137 action research 289, 293, 295 added value 216 antitrust 163, 165, 166, 169, 171, 175 architecture, engineering and construction (AEC) 308, 316 artificial intelligence (AI) 125, 126, 140, 178, 184, 185 Asynchronous JavvaScript and XML (AJAX) 209 authorization switching network (RCB) 165, 166, 168, 169 automated teller machines (ATMs) 162, 163, 164, 166, 168 axiomatic 1

B balanced approach 177, 178, 179 Bologna process 5, 14 breast cancer screening 187, 188, 189 Breast Screening Attendance Messaging Protocol (BSAMP) 183, 184, 185, 186 Breast Screening Unit (BSU) 183 business intelligence (BI) 274

C capability maturity model (CMM) 255, 256 Carte Bancaire (CB) 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174 Centre for Excellence in Professional Learning from the Workplace (CEPLW) 210, 211, 213

chaos edge 319, 323, 324 clinical decision support systems (CDSS) 191 codifiability 95, 104 cognitive science 95, 96, 120, 122 collaboration economy 141, 142, 153 collaborative innovation networks (COINs) 145, 268 collaborative interest networks (CINs) 145 collaborative knowledge networks (CKN) 145, 146, 268, 282 Collaborative Learning Networks (CLNs) 145, 268 collaborative technologies 141 collaborative working environment 264, 265, 271 collective stupidity 231 Common KAD 302, 315 communication nets 283, 284 Communities of Innovation (CoInv) 243, 249, 250 communities of practice (COPs) 95, 96, 115, 117, 150, 178, 207, 218, 219, 223, 225, 245, 249 community of interest (CoI) 245, 268, 276 competitive advantage 7, 19, 20, 22, 24, 25, 56, 61, 95, 105, 118, 121, 123, 142, 144, 146, 154, 157, 158, 169, 170, 171, 173, 189, 254, 266, 288, 302, 303, 304, 306, 312 computational fluid dynamics (CFD) 151 computed tomography (CT) 182 computer aided design (CAD) 150 computer aided engineering (CAE) 150 computer fear syndrome (CFS) 77, 78, 79, 80, 81, 82, 83, 84, 88, 89, 90

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Index

computer-mediated communication (CMC) 204 continuing professional development (CPD) 207, 210 core competency 56 cross organisational learning approach (COLA) 109, 122 customer relationship management (CRM) 151, 274

D data transfer service (DTS) 185 decision makers 18, 46 decision making 190, 191, 193, 194, 198, 199, 200, 218, 225 degree centrality 264, 268 dependent variables (DV) 79, 80, 86 desktop publishing (DTP) 79, 80, 81, 82, 83, 88, 89 diffusion 40, 41, 42, 43, 44, 45, 47, 48, 51, 52, 53, 54, 55, 74 digital divide 143 digital mock-up (DMU) 151 dissipative structure theory 321 domain knowledge 126, 128, 129, 131, 132, 133, 134, 138, 139 dynamic system development methodology (DSDM) 45, 53

E e-business 264, 265, 271, 273, 274, 275, 278 e-Business Management Section (eBMS) 264, 265, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284 Economic Interest Group (EIG) 161, 162, 164, 168 effability 95, 104 eGain Knowledge 32 e-government 200, 201 e-health 200, 201, 232 e-learning 264, 265, 274, 278, 279 embrained 191, 192 encultured 191, 192 enterprise resource planning (ERP) 151, 274 epistemology 95, 96, 100, 101, 119, 121, 122, 123

384

equity investors 125 ethnographic study 40 European Community (EC) 40, 46, 47, 49 expert system 125, 126, 133, 137, 138, 139 explicit knowledge 3, 7, 10, 20, 21, 27, 30, 34, 35, 36, 57, 61, 62, 71, 73, 191, 217, 243 external learning 234, 236

F fast moving consumer goods (FMCG) 289 financial ratios 127, 130, 131, 132, 133, 135, 136, 137 financial statements 125, 126, 127, 128, 129, 130, 131, 135, 138, 139, 140 finite elements analysis (FEM) 151 fixed_assets_turnover (FAT) 137 formal roles 265 French Electronic Data Privacy Agency (CNIL) 167 functional analysis 1, 3

G general physician (GP) 184, 185, 186 general problem solver (GPS) 111 global markets 254, 300, 301, 303, 307 group betweenness centrality (GBC) 268, 277, 278 Groupement Cartes Bancaires (GCB) 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172

H halo effect 1, 5, 12, 14 health electronic records (HER) 191 Health Level 7 (HL7) 184, 185 heretic management 319 heuristic knowledge 126 higher education 1, 2, 5, 12, 13, 15, 16 high performance computing (HPC) 151 holonic management 319, 320, 321, 325, 329 holonic organization 321 holonic pass 321 holonic system 321 human capital 242, 249, 267 Humboldtian paradigm 2

Index

I independent variables (IDV) 80, 86 industrial technology and information services (ITIS) 58 Industrial Technology Research Institute (ITRI) 58, 59 informal roles 265, 277, 280, 282, 284 information and communication technology (ICT) 142, 143, 144, 150, 153, 190, 191, 195, 200, 201, 254, 265, 266, 273 information chaos 190, 191 information overload 190 information sharing 95, 96, 105, 113, 114, 115, 118 information system development method (ISDM) 40, 41, 42, 43, 44, 46, 47, 48, 50, 51, 52 information systems (IS) 40, 41, 42, 44, 46, 53, 54, 217, 227 information technology (IT) 19, 21, 22, 24, 33, 34, 39, 40, 41, 42, 43, 46, 47, 49, 51, 52, 137, 232, 238, 257, 258, 276, 277, 278, 279, 280, 282, 283 infrastructure 18, 21, 25, 29, 35, 41, 58, 59, 65, 71, 75, 85 infusion 40, 44, 52, 54 innovation 254, 262, 263, 264, 265, 266, 267, 268, 270, 277, 278, 280, 281, 282, 283, 284, 287, 297, 298, 300-316, 319, 320, 321, 322, 323, 324, 325, 327, 328 innovation champions 250 innovative management 283, 319, 320, 321, 324 innovative potential 306 instant messaging (IM) 207 Institute for Information (III) 58 institutional impetus 158, 160, 161 institutional isomorphic 157, 160, 161, 169, 170, 171, 172, 173 intangible assets 20, 25 integrated advanced information management system (IAIMS) 198 intellectual capital 260, 265, 266, 267, 285, 296, 313 intellectual capital management (ICM) 265 internal competition 218

Internet Relay Chat (IRC) 208 interventions 177, 184, 185, 186 isomorphism 160, 164, 167, 168, 173 iterative application development (IAD) 44, 45, 46, 47, 48, 49, 51, 52

J JAVA 133, 138, 139 joint application development (JAD) 45, 47, 48, 49, 50, 51, 52

K knowledge application 307 knowledge assets 19, 37, 56, 57, 58, 61, 62, 69, 75, 141 knowledge-based diffusion 41, 51 knowledge based engineering (KBE) 151 knowledge-based society 320 knowledge blocks 289, 291, 292, 295, 296 knowledge bundle 40, 41, 42, 44, 46 knowledge creation 1, 2, 7, 8, 14, 17, 18, 21, 22, 25, 27, 35, 36, 38, 39, 57, 62, 73, 93, 179, 180, 185, 186, 191, 192, 193, 203, 205, 244, 300, 305, 306, 310, 313, 314, 315, 316 knowledge democracy 141, 142, 143, 146, 148, 149, 150, 152, 154 knowledge discovery in databases (KDD) 192, 193 knowledge dynamics 6, 14, 15 knowledge economy 254, 262, 312, 315 knowledge engineering (KE) 179, 185 knowledge exploitation 159 knowledge exploration 159 knowledge generation 5, 7, 190, 191, 192, 193, 194, 195 knowledge identification 306 knowledge intensive organizations 1, 14 knowledge management capability assessment model (KMCA) 255, 256 knowledge management framework 22, 58 Knowledge Management (KM) 18-33, 36, 37, 39, 56-77, 84-91, 105, 113, 121, 124, 157-162, 164, 169, 170, 172, 173, 174, 177-181, 184-190, 197, 215, 216, 217, 218, 224, 225, 226, 227, 228, 242-266,

385

Index

271, 273, 274, 278, 285-298, 301, 302, 303, 310 knowledge management model 115, 116, 117 knowledge management portal 60, 62, 63, 64, 65, 66, 67, 68, 72, 73 knowledge management systems (KMS) 16, 21, 36, 37, 39, 75, 77, 78, 265, 266 knowledge network gap (KNG) 70 knowledge objects (KOs) 62 knowledge organizations 1, 14 knowledge processing 1, 2 knowledge process quality model (KPQM) 255, 256 knowledge sharing 145, 146, 153, 158, 169, 170, 179, 180, 181, 184, 185, 186, 187, 212, 215, 216, 217, 218, 223, 224, 225, 237, 242, 243, 244, 245, 249, 250, 301, 303, 304, 305, 307, 309, 310, 312 knowledge sharing networks 57 knowledge society 320 knowledge strategy 296 Knowledge Strategy Dilemma 286, 296 knowledge target 292, 294 knowledge vacuum 62, 64 knowledge worker 178

N National Centre for Work Based Learning Partnerships (NCWBLP) 211 National Health Service (NHS) 177, 182, 184, 185, 187 network metrics 265, 269, 271 network visualization and analysis (NVA) 248, 249 new generation knowledge management (NGKM) 96 new product development (NPD) 141, 143, 144, 146, 147, 148, 149, 150, 151, 153, 154, 155

O

laggards 247 learning organizations 1, 2, 5, 6, 7, 10, 11, 12, 13, 14, 17, 23 Likert scale 234 logistic regression (LR) 183

Observation followed by Orientation, then by Decision, and finally Action (OODA) 193, 194, 195, 196 on-line analytic processing (OLAP) 196 ontology 125, 126, 127, 132, 133, 139 open innovation 146, 148 open source API 133 Open University’s Practice-Based Professional Learning Centre (OUPBPL) 209 operating revenue 130, 131 opinion leaders 246, 247, 248, 249, 250, 252 organizational culture 218, 230, 231, 232, 233, 234, 237, 238, 239, 240, 249 organizational field 159 organizational learning 230, 231, 233, 234, 235, 236, 237, 239

M

P

magnetic resonance imaging (MRI) 182 MaKE 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 298, 299 maturity model 253, 254, 255, 258, 260, 262, 263 Metal Industries Research & Development Centre (MIRDC) 58, 60, 61, 62, 63, 64, 65, 66, 68, 69, 70, 71, 72, 73, 74 metal industry 56, 60 mimetic isomorphism 160, 167 multidisciplinary design optimization (MDO) 149, 151

paradigm 99, 111, 117, 123, 141, 142, 146, 153, 173, 177, 178, 179 paternalistic management 323 perceptual knowledge 104, 112, 114 personal identification number (PIN) 162, 163, 164, 171 personal knowledge management system (PKMS) 245 personal learning environments (PLEs) 212 picture archive computerized systems (PACS) 191 pluralist epistemology 96, 101

L

386

Index

point of sale terminals (POS) 162, 168 pragmatic 96, 97, 98 pragmatists 246 problem-based learning (PBL) 215, 216, 219, 220, 221, 222, 223, 224, 225, 226 product data management (PDM) 151 product innovation 141, 142, 143, 147, 153, 154 productivity paradox 190 professional learning 204, 206, 207, 210, 211, 212, 213, 214 professional learning community (PLC) 206, 207, 209, 210, 212, 213 professional learning from the workplace (PLW) 206, 207 pro-forma innovation 232 project management 255, 264, 271, 274, 313 Protégé 132, 133, 134, 138, 139, 140 publishing systems 77

R rapid application development (RAD) 42, 44, 45, 46, 47, 48, 51, 54 rationalist 96 research and development (R&D) 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 67, 68, 69, 70, 72, 73, 74, 75, 76, Return on Investment (ROI) 326, 328 rolling loop 194

S SCRUM 45, 54 SECI process 10 service oriented architecture (SOA) 151 SharePoint 32 Single European Payment Area (SEPA) 168, 171, 173, 176 Smartcard 157, 169 SOAP 184, 185 social capital (SC) 243, 244, 245, 246, 249, 251 social collectivity 266 social fabric 243, 244 socialization 8, 10, 159

socialization externalization combination internalization (SECI) 180 social network 209, 246, 250 social network analysis (SNA) 245, 246, 248, 264, 265, 267, 284 social network scorecard (SNS) 264 social software 204, 205, 207, 208, 209, 211, 212, 213 social-technical perspective (S-T perspective) 72 sociogram 269 Software Engineering Institute (SEI) 255 software tools 18, 21, 22, 36 suboptimal 200 supply chain management (SCM) 151, 274 Surrey Centre for Excellence in Professional Training and Education (SCEPTre) 210 systematic analysis 40

T tacit knowledge 3, 7, 8, 9, 10, 16, 17, 21, 23, 24, 34, 35, 36, 38, 57, 59, 60, 62, 69, 70, 73, 75, 191, 217, 218, 224, 225, 227, 243 Taiwan Textile Research Institute (TTRI) 58 taxonomy 29, 33, 34, 35 test bed 264 total_assets_turnover (TAT) 137 traceability 286, 289, 291, 292, 296 transparency 286, 289, 291, 292, 296 two factor theory (T-F theory) 72

U User Alienation Syndrome (UAS) 77, 78, 88, 89, 90

V video-conferencing 21, 32 virtual communities 144, 145 virtual eBMS 264, 265, 271, 272, 273, 274, 275, 277, 278, 279, 280, 281, 282, 283, 284

W Warwickshire, Solihull and Coventry Breast Screening Unit (WSCBSU) 183, 186

387

Index

Web 2.0 204, 207, 213, 214, 266 Web 3.0 266 Web learning (WL) 271, 274 WHIG 195, 196, 197, 198, 200, 201 wikinomics 141, 142 word processing (WP) 80, 81, 82

388

work integrated learning (WIL) 206 World Association for Co-operative Education (WACE) 211

Z zone of proximal development (ZPD) 99