Strategies for Knowledge Management Success

Strategies for Knowledge Management Success: Exploring Organizational Efficacy

Murray E. JennexSan Diego State University, USA

Stefan SmolnikEBS University of Business and Law, Germany

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Library of Congress Cataloging-in-Publication Data

Strategies for knowledge management success : exploring organizational efficacy / Murray Jennex and Stefan Smolnik, editors. p. cm. Includes bibliographical references and index. Summary: "This chapter presents results of a survey looking at how KM practitioners, researchers, KM students, and others interested in KM view what constitutes KM success, including background on KM success and then a series of perspectives on KM/KMS success"--Provided by publisher. ISBN 978-1-60566-709-6 (hbk.) -- ISBN 978-1-60566-710-2 (ebook) 1. Knowledge management. I. Jennex, Murray E., 1956- II. Smolnik, Stefan, 1970- HD30.2.S796 2010 658.4'038--dc22 2009052394

British Cataloguing in Publication DataA 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All work contributed to this book is new, previously-unpublished material.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.

List of Reviewers

Rodrigo Baroni de Carvalho, FUMEC University, Brazil Vittal S. Anantatmula, Western Carolina University, USA Kerstin Fink, University of Innsbruck, Austria Hannu Kivijrvi, Helsinki School of Economics, Finland P. Lpez Sez, Universidad Complutense de Madrid, Spain Shahnawaz Muhammed, Fayetteville State University, USA Alexander Orth, Accenture, Germany Vincent M. Ribire, Bangkok University, Thailand Silke Wei, Federal Ministry of Finance, Austria Suzanne Zyngier, LaTrobe University, Australia Thomas Menkhoff, Singapore Management University, Singapore

Preface ................................................................................................................................................. xiv

Section 1Knowledge Management Success

Chapter 1Towards a Consensus Knowledge Management Success Definition ...................................................... 1

Murray E. Jennex, San Diego State University, USAStefan Smolnik, EBS University of Business and Law, GermanyDavid T. Croasdell, University of Nevada, Reno, USA

Chapter 2A Model of Knowledge Management Success ..................................................................................... 14

Murray E. Jennex, San Diego State University, USALorne Olfman, Claremont Graduate University, USA

Chapter 3Market Knowledge Management, Innovation and Product Performance: Survey in Mediumand Large Brazilian Industrial Firms .................................................................................................... 32

Cid Gonalves Filho, FUMEC University, BrazilRodrigo Baroni de Carvalho, FUMEC University, BrazilGeorge Leal Jamil, FUMEC University, Brazil

Chapter 4Does KM Governance = KM Success? Insights from a Global KM Survey........................................ 51

Suzanne Zyngier, La Trobe University, Australia

Chapter 5An Evaluation of Factors that Influence the Success of Knowledge Management Practicesin US Federal Agencies ........................................................................................................................ 74

Elsa Rhoads, The George Washington University, Institute of Knowledge & Innovation, USAKevin J. OSullivan, New York Institute of Technology, USAMichael Stankosky, The George Washington University, USA

Table of Contents

Section 2 KM Measurements

Chapter 6Process Model for Knowledge Potential Measurement in SMEs ......................................................... 91

Kerstin Fink, University of Innsbruck, Austria

Chapter 7Developing Individual Level Outcome Measures in the Context of KnowledgeManagement Success .......................................................................................................................... 106

Shahnawaz Muhammed, American University of Middle East, KuwaitWilliam J. Doll, University of Toledo, USAXiaodong Deng, Oakland University, USA

Chapter 8Validating Distinct Knowledge Assets: A Capability Perspective ...................................................... 128

Ron Freeze, Emporia State University, USAUday Kulkarni, Arizona State University, USA

Chapter 9Assessing Knowledge Management: Refining and Cross-Validating the Knowledge Management Index (KMI) using Structural Equation Modeling (SEM) Techniques ......................... 150

Derek Ajesam Asoh, Southern Illinois University Carbondale, USA & National Polytechnic, University of Yaounde, Cameroon

Salvatore Belardo, University at Albany, USAJakov (Yasha) Crnkovic, University at Albany, USA

Chapter 10 A Relational Based-View of Intellectual Capital in High-Tech Firms................................................ 179

G. Martn De Castro, Universidad Complutense de Madrid, SpainP. Lpez Sez, Universidad Complutense de Madrid, Spain J.E. Navas Lpez, Universidad Complutense de Madrid, Spain M. Delgado-Verde, Universidad Complutense de Madrid, Spain

Section 3 KM Strategies in Practice

Chapter 11The Effect of Organizational Trust on the Success of Codification and Personalization KM Approaches .................................................................................................................................. 192

Vincent M. Ribire, Bangkok University, Thailand

Chapter 12Advancing the Success of Collaboration Centered KM Strategy ...................................................... 213

Johanna Bragge, Aalto University School of Economics, FinlandHannu Kivijrvi, Aalto University School of Economics, Finland

Chapter 13The Relevance of Integration for Knowledge Management Success: Towards Conceptual and Empirical Evidence .................................................................................... 238

Alexander Orth, Accenture, GermanyStefan Smolnik, EBS University of Business and Law, GermanyMurray Jennex, San Diego State University, USA

Chapter 14 Strategies for Successful Implementation of KM in a University Setting .......................................... 262

Vittal S. Anantatmula, Western Carolina University, USAShivraj Kanungo, George Washington University, USA

Chapter 15 DYONIPOS: Proactive Knowledge Supply ....................................................................................... 277

Silke Wei, Federal Ministry of Finance, AustriaJosef Makolm, Federal Ministry of Finance, AustriaDoris Ipsmiller, m2n development and consulting gmbh, AustriaNatalie Egger, Federal Ministry of Finance, Austria

Compilation of References .............................................................................................................. 288

About the Contributors ................................................................................................................... 317

Index ................................................................................................................................................... 325

Preface ................................................................................................................................................. xiv

Section 1Knowledge Management Success

Chapter 1Towards a Consensus Knowledge Management Success Definition ...................................................... 1

Murray E. Jennex, San Diego State University, USAStefan Smolnik, EBS University of Business and Law, GermanyDavid T. Croasdell, University of Nevada, Reno, USA

This chapter explores knowledge management, KM, and knowledge management system, KMS, suc-cess. The inspiration for this chapter is the KM Success and Measurement minitracks held at the Hawaii International Conference on System Sciences in January of 2007 and 2008. KM and KMS success are issues needing to be explored. The Knowledge Management Foundations workshop held at the Hawaii International Conference on System Sciences (HICSS-39) in January 2006 discussed this issue and reached agreement that it is important for the credibility of the KM discipline that we be able to define KM success. Additionally, from the perspective of KM academics and practitioners, identifying the fac-tors, constructs, and variables that define KM success is crucial to understanding how these initiatives and systems should be designed and implemented. This chapter presents results of a survey looking at how KM practitioners, researchers, KM students, and others interested in KM view what constitutes KM success. The chapter presents some background on KM success and then a series of perspectives on KM/KMS success. These perspectives were derived by looking at responses to questions asking academics and practitioners how they defined KM/KMS success. The chapter concludes by presenting the results of an exploratory survey on KM/KMS success beliefs and attitudes.

Chapter 2A Model of Knowledge Management Success ..................................................................................... 14

Murray E. Jennex, San Diego State University, USALorne Olfman, Claremont Graduate University, USA

Detailed Table of Contents

This chapter describes a knowledge management, KM, Success Model that is derived from observa-tions generated through a longitudinal study of KM in an engineering organization, KM success factors found in the literature, and modified by the application of these observations and success factors in various projects. The DeLone and McLean (1992, 2003) IS Success Model was used as a framework for the model as it was found to fit the observed success criteria and it provided an accepted theoretical basis for the proposed model.

Chapter 3Market Knowledge Management, Innovation and Product Performance: Survey in Mediumand Large Brazilian Industrial Firms .................................................................................................... 32

Cid Gonalves Filho, FUMEC University, BrazilRodrigo Baroni de Carvalho, FUMEC University, BrazilGeorge Leal Jamil, FUMEC University, Brazil

In a business environment characterized by a high level of competitiveness, the impact of new products on an organizations revenue is an important factor. This research was developed with the objective of examining empirically the relationships between market knowledge management, innovation and the performance of new products in the market. This chapter analyzes KM (Knowledge Management) success trough a market-oriented perspective because, at the end of the day, KM success must lead to better organizational performance. The research model was generated by the combination of market knowledge models and KM success and maturity models. By means of a survey, based on 387 medium and large industrial firms, and the use of structural equation modeling, the supremacy of the competitor knowledge management process over other constructs was verified, as the most important antecedent of new product performance in the market. The results also revealed that innovation was strongly im-pacted from technology knowledge management and customer knowledge management.

Chapter 4Does KM Governance = KM Success? Insights from a Global KM Survey........................................ 51

Suzanne Zyngier, LaTrobe University, Australia

This chapter examines factors that contribute to KM success by differentiating between KM leader-ship through management and through governance. We look at governance as a structural mechanism that both embeds KM into organizational activity, and lifts it from a series of initiatives to a structured program of activities that are subject to authority, policy, risk management, financial fiduciary duty, and evaluation. Using evidence from 214 respondents to a global internet based KM survey; we find that having a recognized and defined authority for KM that is well-resourced leads to strategically aligned benefits realized from investment in KM. We demonstrate that governance through assigned authority strongly contributes to strategic KM success.

Chapter 5An Evaluation of Factors that Influence the Success of Knowledge Management Practicesin US Federal Agencies ........................................................................................................................ 74

Elsa Rhoads, The George Washington University, Institute of Knowledge & Innovation, USAKevin J. OSullivan, New York Institute of Technology, USAMichael Stankosky, The George Washington University, USA

This research chapter investigates the status of knowledge management practices implemented across federal agencies of the U.S. government. It analyzes the extent to which this status is influenced by the size of the agency, whether or not the agency type is a Cabinet-level Department or Independent Agen-cy, the longevity of KM Practices implemented in the agency, whether or not the agency has adopted a written KM policy or strategy, and whether the primary responsibility for KM Practices in the agency is directed by a CKO or KM unit versus other functional locations in the agency. The research also tests for possible KM practitioner bias, since the survey was directed to members of the Knowledge Management Working Group of the Federal CIO Council who are KM practitioners in federal agencies.

Section 2 KM Measurements

Chapter 6Process Model for Knowledge Potential Measurement in SMEs ......................................................... 91

Kerstin Fink, University of Innsbruck, Austria

Knowledge measurement is developing into a new research field in the area of knowledge management. To ensure that a company is successful, business, technology, and human elements must be integrated and balanced into a knowledge measurement system. The introduction of a knowledge audit with the objective to uncovering the tacit knowledge in an organization and of identifying the existing manage-ment practices is needed. This chapter uses the quantum mechanical thinking as a reference model for the development of a knowledge potential measurement system. This system is influenced by three measurement components: (1) Person-dependent variables, (2) System-dependent variables and (3) knowledge velocity. Based on several case studies conducted in small and medium-sized enterprises, a process model for the implementation of the knowledge potential framework is discussed and intro-duced. Future research and limitations of the model are discussed in the final part.

Chapter 7Developing Individual Level Outcome Measures in the Context of KnowledgeManagement Success .......................................................................................................................... 106

Shahnawaz Muhammed, American University of Middle East, KuwaitWilliam J. Doll, University of Toledo, USAXiaodong Deng, Oakland University, USA

Success of organizational level knowledge management initiatives depends on how effectively indi-viduals implementing these initiatives use their knowledge to bring about outcomes that add value in their work. To facilitate assessment of individual level outcomes in the knowledge management context, this research provides a model of interrelationships among individual level knowledge man-agement success measures which include conceptual knowledge, contextual knowledge, operational knowledge, innovation, and performance. The model was tested using structural equation modeling based on data collected from managerial and professional knowledge workers. The results suggest that conceptual knowledge enhances operational and contextual knowledge. Contextual knowledge improves operational knowledge and is also a key predictor of innovations. The innovativeness of an

individuals work along with operational knowledge enhances work performance. The results support the proposed model. This model can potentially be used for measuring knowledge management success at the individual level.

Chapter 8Validating Distinct Knowledge Assets: A Capability Perspective ...................................................... 128

Ron Freeze, Emporia State University, USAUday Kulkarni, Arizona State University, USA

Identification and measurement of organizational Knowledge Management capabilities is necessary to determine the extent to which an organization utilizes its knowledge assets. We developed and opera-tionalized a set of constructs to measure capabilities associated with management of knowledge assets identified as distinct Knowledge Capabilities (KCs) comprising the overall Knowledge Management (KM) capability of an organizational unit. Each KC represents a distinct kind of knowledge that re-quires different organizational process and technological support. This delineation of knowledge al-lows targeted improvement to a specific KC. We present validation of these capability constructs with empirical evidence from two separate business units in a large semi-conductor manufacturing company, providing the basis of measurement standardization for KM Capability improvement. Confirmatory factor analysis affirmed four KCs, each identified as an overall factor influencing a set of latent descrip-tor variables. Second Order and General-Specific Structural Equation Models of each capability pro-vide evidence as to the validity of measurement of these knowledge assets. A standardized instrument for measuring knowledge capabilities would not only allow benchmarking, but also allow tracking capabilities over time and linking them to those performance metrics that are deemed appropriate by the organization.

Chapter 9Assessing Knowledge Management: Refining and Cross-Validating the Knowledge Management Index (KMI) using Structural Equation Modeling (SEM) Techniques ......................... 150

Derek Ajesam Asoh, Southern Illinois University Carbondale, USA & National Polytechnic, University of Yaounde, Cameroon

Salvatore Belardo, University at Albany, USAJakov (Yasha) Crnkovic, University at Albany, USA

With growing interest in KM-related assessments and calls for rigorous assessment tools, the objec-tive of this study was to apply SEM techniques to refine and cross-validate the KMI, a metric to assess the degree to which organizations are engaged in knowledge management (KM). Unlike previous KM metrics research that has focused on scales, we modeled the KMI as a formative latent variable, thereby extending knowledge on formative measures and index creation from other fields into the KM field. The refined KMI metric was tested in a nomological network and found to be robust and stable when cross-validated; thereby demonstrating consistent prediction results across independent data sets. The study also verified the hypothesis that the KMI is positively correlated with organizational performance (OP). Research contributions, managerial implications, limitations of the study, and direction for fur-ther research are discussed.

Chapter 10 A Relational Based-View of Intellectual Capital in High-Tech Firms................................................ 179

G. Martn De Castro, Universidad Complutense de Madrid, SpainP. Lpez Sez, Universidad Complutense de Madrid, Spain J.E. Navas Lpez, Universidad Complutense de Madrid, Spain M. Delgado-Verde, Universidad Complutense de Madrid, Spain

The Resource-Based Theory (RBT) has tried to test the role of strategic resources on sustained com-petitive advantage and superior performance. Although this theory has found several flaws in order to reach its objective effectively (Priem & Butler, 2001; Foss & Knudsen), recent proposals have sug-gested that these problems can be overcome (Peteraf & Barney, 2003). This solution requires paying a greater attention to the analysis of knowledge stocks, developing a mid-range theory: the Intellectual Capital-Based View (Reed, Lubatkin & Srinivasan, 2006). This mid-range and pragmatic theory allows the hypotheses development and empirical testing in a more effective way than the Resource Based View (RBV). There is a certain degree of general agreement about the presence of human capital and organizational capital as the main components of intellectual capital, as well as about the fact that the configuration of knowledge stocks will vary from one industry and firm to another one. Taking these assumptions as a starting point, this chapter explores the configuration of intellectual capital that can be empirically found on a sample of high-technology firms. Our findings highlight the importance of relational capital, which must be divided in business and alliance capital, so the strategic alliances play a relevance role in the type of firms that have been included in our research.

Section 3 KM Strategies in Practice

Chapter 11The Effect of Organizational Trust on the Success of Codification and Personalization KM Approaches .................................................................................................................................. 192

Vincent M. Ribire, Bangkok University, Thailand

Knowledge Management (KM) initiatives are expanding across all types of organizations worldwide. However, not all of them are necessarily successful mainly due to an unfriendly organizational culture. Organizational trust is often mentioned as a critical factor facilitating knowledge sharing. For this research we took an empirical approach to validate this assumption. The purpose of this research is to explore the relationships between organizational trust, a knowledge management strategy (codifica-tion vs. personalization) and its level of success. This study was conducted among 97 US companies involved in knowledge management. A survey tool was developed and validated to assess the level of trust, the level of success and the dominant KM strategy deployed by an organization. Six main research hypotheses and a conceptual model were tested. The findings show the impact of trust on the choice of the KM strategy as well as on the level of success.

Chapter 12Advancing the Success of Collaboration Centered KM Strategy ...................................................... 213

Johanna Bragge, Aalto University School of Economics, FinlandHannu Kivijrvi, Aalto University School of Economics, Finland

Knowledge is today more than ever the most critical resource of organizations. At the same time it is, however, also the least-accessible resource that is difficult to share, imitate, buy, sell, store, or evaluate. Organizations should thus have an explicit strategy for the management of their knowledge resources. In this chapter we pay special attention to a KM strategy called collaboration centered strategy. This strategy builds on the assumption that a significant part of personal knowledge can be captured and transferred, and new knowledge created through deep collaboration between the organizations mem-bers. A critical element in the collaboration centered KM strategy is the facilitation process that in-volves managing relationships between people, tasks and technology. We describe how the Collabora-tion Engineering approach with packaged facilitation techniques called ThinkLets is able to contribute to this endeavour.

Chapter 13The Relevance of Integration for Knowledge Management Success: Towards Conceptual and Empirical Evidence .................................................................................... 238

Alexander Orth, Accenture, GermanyStefan Smolnik, EBS University of Business and Law, GermanyMurray Jennex, San Diego State University, USA

Many organizations pursue knowledge management (KM) initiatives, with different degrees of success. One key aspect of KM often neglected in practice is following an integrated and holistic approach. Complementary, KM researchers have increasingly focused on factors that determine KM success and examined whether the metrics used to measure KM initiatives are reasonable. In this chapter, the impor-tance of integration issues for successful KM is analyzed by means of a case study of a KM initiative at an international consulting company. The investigations demonstrate the importance of an integrated KM approach an integrated view of KM strategy, KM processes, KM technology, and company cul-ture to ensure KM success.

Chapter 14 Strategies for Successful Implementation of KM in a University Setting .......................................... 262

Vittal S. Anantatmula, Western Carolina University, USAShivraj Kanungo, George Washington University, USA

Research has identified enabling factors and inhibitors for implementing knowledge management suc-cessfully and to accomplish its strategic objectives. However, it is important to understand how these factors interact with each other to improve or inhibit the performance. With this in mind, this chapter presents a model, based on a research study, to determine underlying relations among these factors and develop strategies implementing KM initiatives.

Chapter 15 DYONIPOS: Proactive Knowledge Supply ....................................................................................... 277

Silke Wei, Federal Ministry of Finance, AustriaJosef Makolm, Federal Ministry of Finance, AustriaDoris Ipsmiller, m2n development and consulting gmbh, AustriaNatalie Egger, Federal Ministry of Finance, Austria

Traditional knowledge management is often combined with extra work to recollect information which is already electronically available. Another obstacle to overcome is to make the content of the collected information easily accessible to enquiries, as conventional searching tools provide only documents and not the content meaning. They are often based on the search for character strings, usually resulting in many unnecessary hits and no or less context information. The research project DYONIPOS focuses on detecting the knowledge needs of knowledge users and automatically providing the required knowl-edge just in time, while avoiding additional work and violations of the knowledge workers privacy, proposing a new way of support. This knowledge is made available through semantic linkage of the relevant information out of existing artifacts. In addition DYONIPOS creates an individual and an or-ganizational knowledge base just in time.

Compilation of References .............................................................................................................. 288

About the Contributors ................................................................................................................... 317

Index ................................................................................................................................................... 325

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Organizations use KM (Knowledge Management), because it makes sense. KM, when done successfully, has an impact on the organization and its members. How do organizations define and measure success or its impact on the organization? Also, while knowing that KM improves an organization may be enough to encourage organizations to pursue a KM initiative, many organizations still need to quantitatively justify an investment in KM. Calculating Return on Investment (ROI), is a popular approach, but how is this done? There are some commonly accepted first steps:

Find a need or an opportunity that KM satisfies, supports, or resolves. Identify the costs with the need or the benefits of the opportunity. Identify the savings or potential earnings that implementing KM will provide. Identify the costs of implementing KM.

Easily stated but not easily done and the resulting financial numbers are often questionable. Do the numbers present the full story for KM? Many think they do not, and that stories and anecdotes about KM need to be included to make KM real to management (Moore, 2008). However, is this enough measurement for an organization?

This book is about how to implement successful KM initiatives. What is required for KM to be suc-cessful? Jennex and Olfman (2005) summarized and synthesized the literature on KM/KMSs critical success factors (CSF) into an ordered set of twelve KM CSFs identified from 17 studies of more than 200 KM projects. These CSFs were thereafter sequentially ordered according to the number of studies identifying them:

A knowledge strategy that identifies users, sources, processes, storage strategy, knowledge, and links to knowledge for the KMS;

Motivation and commitment of users, including incentives and training; Integrated technical infrastructure, including networks, databases/repositories, computers, software,

and KMS experts; An organizational culture and structure that supports learning and the sharing and use of knowledge; A common enterprise-wide knowledge structure that is clearly articulated and easily understood; Senior management support, including allocation of resources, leadership, and training; Learning organization; The KMS has a clear goal and purpose; Measures are established to assess the impacts of the KMS and the use of knowledge, as well as

verification that the right knowledge is being captured;

Preface

xv

The search, retrieval, and visualization functions of the KMS support facilitated use of knowledge; Work processes are designed that incorporate knowledge capture and use; and Knowledge is secured/protected.

While the above CSFs are useful for determining if the antecedents for KM success exist in an organization, they do not state what success is or how to assess it. This book attempts to answer these questions. Three sections are provided: Section 1 discusses KM success. It defines what KM success is, provides a model of KM success, and discusses KM success in a variety of contexts. Section 2 ad-dresses the issue of measuring KM. It is proposed that organizations cannot manage what they cannot measure. This section provides a variety of studies that provide KM measures based on various theoreti-cal perspectives. Finally, knowing how to define KM success and how to measure KM is important, but without a strategy for implementing the KM initiative the organization is not likely to succeed. Section 3 presents several KM strategies as implemented in a variety of contexts. The following paragraphs provide further description of the chapters.

Section 1: Knowledge ManageMent SucceSS

Chapter 1: Towards a Consensus Knowledge Management Success Definition by Murray E. Jennex, Stefan Smolnik, David T. Croasdell, explores knowledge management, KM, and knowledge manage-ment system, KMS, success. Identifying the factors, constructs, and variables that define KM success is crucial to understanding how these initiatives and systems should be designed and implemented. This chapter presents results of a survey looking at how KM practitioners, researchers, KM students, and others interested in KM view what constitutes KM success. The chapter presents some background on KM success and then a series of perspectives on KM/KMS success. These perspectives were derived by looking at responses to questions asking academics and practitioners how they defined KM/KMS success. The chapter concludes by presenting the results of an exploratory survey on KM/KMS success beliefs and attitudes.

Chapter 2: A Model of Knowledge Management Success by Murray E. Jennex, Lorne Olfman, describes a knowledge management, KM, Success Model that is derived from observations gener-ated through a longitudinal study of KM in an engineering organization, KM success factors found in the literature, and modified by the application of these observations and success factors in various projects. The DeLone and McLean (1992, 2003) IS Success Model was used as a framework for the model as it was found to fit the observed success criteria and it provided an accepted theoretical basis for the proposed model.

Chapter 3: Market Knowledge Management, Innovation and Product Performance: Survey in Medium and Large Brazilian Industrial Firms by Cid Gonalves Filho, Rodrigo Baroni de Carvalho, George Leal Jamil. In a business environment characterized by a high level of competitiveness, the impact of new products on an organizations revenue is an important factor. This research was de-veloped with the objective of examining empirically the relationships between market knowledge management, innovation and the performance of new products in the market. This chapter analyzes KM (Knowledge Management) success through a market-oriented perspective because, at the end of the day, KM success must lead to better organizational performance. The research model was gen-erated by the combination of market knowledge models and KM success and maturity models. By

xvi

means of a survey, based on 387 medium and large industrial firms, and the use of structural equation modeling, the supremacy of the competitor knowledge management process over other constructs was verified, as the most important antecedent of new product performance in the market. The results also revealed that innovation was strongly impacted from technology knowledge management and customer knowledge management.

Chapter 4: Does KM Governance = KM Success? Insights from a Global KM Survey by Suzanne Zyngier, examines factors that contribute to KM success by differentiating between KM leadership through management and through governance. We look at governance as a structural mechanism that both embeds KM into organizational activity, and lifts it from a series of initiatives to a structured pro-gram of activities that are subject to authority, policy, risk management, financial fiduciary duty, and evaluation. Using evidence from 214 respondents to a global internet based KM survey; we find that having a recognized and defined authority for KM that is well-resourced leads to strategically aligned benefits realized from investment in KM. We demonstrate that governance through assigned authority strongly contributes to strategic KM success.

Chapter 5: An Evaluation of Factors that Influence the Success of Knowledge Management Prac-tices in US Federal Agencies, by Elsa Rhoads, Kevin J. OSullivan, Michael Stankosky, investigates the status of knowledge management practices implemented across federal agencies of the U.S. gov-ernment. It analyzes the extent to which this status is influenced by the size of the agency, whether or not the agency type is a Cabinet-level Department or Independent Agency, the longevity of KM Practices implemented in the agency, whether or not the agency has adopted a written KM policy or strategy, and whether the primary responsibility for KM Practices in the agency is directed by a CKO or KM unit versus other functional locations in the agency. The research also tests for possible KM practitioner bias, since the survey was directed to members of the Knowledge Management Working Group of the Federal CIO Council who are KM practitioners in federal agencies.

Section 2: KM MeaSureMentS

Chapter 6: Process Model for Knowledge Potential Measurement in SMEs by Kerstin Fink, shows that knowledge measurement is developing into a new research field in the area of knowledge management. To ensure that a company is successful, business, technology, and human elements must be integrated and balanced into a knowledge measurement system. The introduction of a knowledge audit with the objective to uncovering the tacit knowledge in an organization and of identifying the existing manage-ment practices is needed. This chapter uses the quantum mechanical thinking as a reference model for the development of a knowledge potential measurement system. This system is influenced by three measurement components: (1) Person-dependent variables, (2) System-dependent variables and (3) knowledge velocity. Based on several case studies conducted in small and medium-sized enterprises, a process model for the implementation of the knowledge potential framework is discussed and introduced. Future research and limitations of the model are discussed in the final part.

Chapter 7: Developing Individual Level Outcome Measures in the Context of Knowledge Man-agement Success by Shahnawaz Muhammed, William J. Doll, Xiaodong Deng, Show how success of organizational level knowledge management initiatives depends on how effectively individuals implementing these initiatives use their knowledge to bring about outcomes that add value in their work. To facilitate assessment of individual level outcomes in the knowledge management context,

xvii

this research provides a model of interrelationships among individual level knowledge manage-ment success measures which include conceptual knowledge, contextual knowledge, operational knowledge, innovation, and performance. The model was tested using structural equation modeling based on data collected from managerial and professional knowledge workers. The results suggest that conceptual knowledge enhances operational and contextual knowledge. Contextual knowledge improves operational knowledge and is also a key predictor of innovations. The innovativeness of an individuals work along with operational knowledge enhances work performance. The results sup-port the proposed model. This model can potentially be used for measuring knowledge management success at the individual level.

Chapter 8: Validating Distinct Knowledge Assets: A Capability Perspective, by Ron Freeze, Uday Kulkarni, explain how identification and measurement of organizational Knowledge Management ca-pabilities is necessary to determine the extent to which an organization utilizes its knowledge assets. We developed and operationalized a set of constructs to measure capabilities associated with manage-ment of knowledge assets identified as distinct Knowledge Capabilities (KCs) comprising the overall Knowledge Management (KM) capability of an organizational unit. Each KC represents a distinct kind of knowledge that requires different organizational process and technological support. This delineation of knowledge allows targeted improvement to a specific KC. We present validation of these capability constructs with empirical evidence from two separate business units in a large semi-conductor manufac-turing company, providing the basis of measurement standardization for KM Capability improvement. Confirmatory factor analysis affirmed four KCs, each identified as an overall factor influencing a set of latent descriptor variables. Second Order and General-Specific Structural Equation Models of each capability provide evidence as to the validity of measurement of these knowledge assets. A standard-ized instrument for measuring knowledge capabilities would not only allow benchmarking, but also allow tracking capabilities over time and linking them to those performance metrics that are deemed appropriate by the organization.

Chapter 9: Assessing Knowledge Management: Refining and Cross-Validating the Knowledge Management Index (KMI) using Structural Equation Modeling (SEM) Techniques, by Derek Ajesam Asoh, Salvatore Belardo, Jakov (Yasha) Crnkovic, show how with growing interest in KM-related assessments and calls for rigorous assessment tools, the objective of this study was to apply SEM techniques to refine and cross-validate the KMI, a metric to assess the degree to which organizations are engaged in knowledge management (KM). Unlike previous KM metrics research that has focused on scales, we modeled the KMI as a formative latent variable, thereby extending knowledge on for-mative measures and index creation from other fields into the KM field.

The refined KMI metric was tested in a nomological network and found to be robust and stable when cross-validated; thereby demonstrating consistent prediction results across independent data sets. The study also verified the hypothesis that the KMI is positively correlated with organizational performance (OP). Research contributions, managerial implications, limitations of the study, and direction for further research are discussed.

Chapter 10: A Relational Based-View of Intellectual Capital in High-Tech Firms by G. Martn De Castro, P. Lpez Sez, J.E. Navas Lpez, M. Delgado-Verde. The Resource-Based Theory (RBT) has tried to test the role of strategic resources on sustained competitive advantage and superior performance. Although this theory has found several flaws in order to reach its objective effectively (Priem & Butler, 2001; Foss & Knudsen), recent proposals have suggested that these problems can be overcome (Peteraf & Barney, 2003). This solution requires paying a greater attention to the analysis of knowledge stocks,

xviii

developing a mid-range theory: the Intellectual Capital-Based View (Reed, Lubatkin & Srinivasan, 2006). This mid-range and pragmatic theory allows the hypotheses development and empirical testing in a more effective way than the Resource Based View (RBV). There is a certain degree of general agreement about the presence of human capital and organizational capital as the main components of intellectual capital, as well as about the fact that the configuration of knowledge stocks will vary from one industry and firm to another one. Taking these assumptions as a starting point, this paper explores the configuration of intellectual capital that can be empirically found on a sample of high-technology firms. Our findings highlight the importance of relational capital, which must be divided in business and alliance capital, so the strategic alliances play a relevance role in the type of firms that have been included in our research.

Section 3: KM StrategieS in Practice

Chapter 11: The Effect of Organizational Trust on the Success of Codification and Personalization KM approaches by Vincent M. Ribire, explains how Knowledge Management (KM) initiatives are expanding across all types of organizations worldwide. However, not all of them are necessarily suc-cessful mainly due to an unfriendly organizational culture. Organizational trust is often mentioned as a critical factor facilitating knowledge sharing. For this research we took an empirical approach to validate this assumption. The purpose of this research is to explore the relationships between or-ganizational trust, a knowledge management strategy (codification vs. personalization) and its level of success. This study was conducted among 97 US companies involved in knowledge management. A survey tool was developed and validated to assess the level of trust, the level of success and the dominant KM strategy deployed by an organization. Six main research hypotheses and a conceptual model were tested. The findings show the impact of trust on the choice of the KM strategy as well as on the level of success.

Chapter 12: Advancing the Success of Collaboration Centered KM Strategy by Johanna Bragge, Hannu Kivijrvi, shows that Knowledge is the most critical resource of organizations. At the same time it is, however, also the least-accessible resource that is difficult to share, imitate, buy, sell, store, or evaluate. Organizations should thus have an explicit strategy for the management of their knowledge resources. In this chapter we pay special attention to a KM strategy called collaboration centered strategy. This strategy builds on the assumption that a significant part of personal knowledge can be captured and transferred, and new knowledge created through deep collaboration between the organizations members. A critical element in the collaboration centered KM strategy is the facilita-tion process that involves managing relationships between people, tasks and technology. We describe how the Collaboration Engineering approach with packaged facilitation techniques called ThinkLets is able to contribute to this endeavour.

Chapter 13: The Relevance of Integration for Knowledge Management Success: Towards Concep-tual and Empirical Evidence by Alexander Orth, Stefan Smolnik, Murray Jennex. Many organizations pursue knowledge management (KM) initiatives, with different degrees of success. One key aspect of KM often neglected in practice is following an integrated and holistic approach. Complementary, KM researchers have increasingly focused on factors that determine KM success and examined whether the metrics used to measure KM initiatives are reasonable. In this chapter, the importance of integration issues for successful KM is analyzed by means of a case study of a KM initiative at an international consulting company. The investigations demonstrate the importance of an integrated

xix

KM approach an integrated view of KM strategy, KM processes, KM technology, and company culture to ensure KM success.

Chapter 14: Strategies for Successful Implementation of KM in a University Setting by Vittal S. Anantatmula, Shivraj Kanungo. Research has identified enabling factors and inhibitors for implementing knowledge management successfully and to accomplish its strategic objectives. However, it is important to understand how these factors interact with each other to improve or inhibit the performance. With this in mind, this chapter presents a model, based on a research study, to determine underlying relations among these factors and develop strategies implementing KM initiatives.

Chapter 15: DYONIPOS: Proactive Knowledge Supply by Josef Makolm, Silke Wei, Doris Ipsmiller, Natalie Egger. Traditional knowledge management is often combined with extra work to recollect infor-mation which is already electronically available. Another obstacle to overcome is to make the content of the collected information easily accessible to enquiries, as conventional searching tools provide only documents and not the content meaning. They are often based on the search for character strings, usually resulting in many unnecessary hits and no or less context information. The research project DYONIPOS focuses on detecting the knowledge needs of knowledge users and automatically providing the required knowledge just in time, while avoiding additional work and violations of the knowledge workers pri-vacy, proposing a new way of support. This knowledge is made available through semantic linkage of the relevant information out of existing artifacts. In addition DYONIPOS creates an individual and an organizational knowledge base just in time.

These chapters come from several sources: some were submitted just to this book, some are expansions of conference/journal articles, and some are taken directly from the International Journal of Knowledge Management (IJKM). Taken together, we believe this book provides researchers, students, and practi-tioners with an excellent overview of how to implement and measure successful KM and/or knowledge initiatives.

We hope you enjoy the book.

Murray E. JennexSan Diego State University, USA


referenceS

Jennex, M.E., & Olfman, L. (2005). Assessing Knowledge Management Success. International Journal of Knowledge Management, 1(2), 33-49.

Moore, M. (2008). Justifying Your Knowledge Management Programme. White Paper retrieved on March 30, 2009 from http://innotecture.files.wordpress.com/2008/11/justifying_your_km_prog3.pdf

Section 1

Knowledge Management Success

1Copyright 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Chapter 1

Towards a Consensus Knowledge Management

Success DefinitionMurray E. Jennex

San Diego State University, USA


David T. CroasdellUniversity of Nevada, Reno, USA

abStract

This chapter explores knowledge management (KM), and knowledge management system (KMS), suc-cess. The inspiration for this chapter is the KM Success and Measurement minitracks held at the Hawaii International Conference on System Sciences in January of 2007 and 2008. KM and KMS success are issues needing to be explored. The Knowledge Management Foundations workshop held at the Hawaii International Conference on System Sciences (HICSS-39) in January 2006 discussed this issue and reached agreement that it is important for the credibility of the KM discipline that we be able to define KM success. Additionally, from the perspective of KM academics and practitioners, identifying the fac-tors, constructs, and variables that define KM success is crucial to understanding how these initiatives and systems should be designed and implemented. This chapter presents the results of a survey looking at how KM practitioners, researchers, KM students, and others interested in KM view what constitutes KM success. This chapter presents some background on KM success and then a series of perspectives on KM/KMS success. These perspectives were derived by looking at responses to questions asking aca-demics and practitioners how they defined KM/KMS success. The chapter concludes by presenting the results of an exploratory survey on KM/KMS success beliefs and attitudes.

DOI: 10.4018/978-1-60566-709-6.ch001

2Towards a Consensus Knowledge Management Success Definition

bacKground on KM SucceSS

Jennex summarized various definitions of KM to propose that KM success be defined as reusing knowledge to improve organizational effective-ness by providing the appropriate knowledge to those that need it when it is needed (Jennex, 2005). KM is expected to have a positive impact on the organization that improves organizational effectiveness. DeLone and McLean use the terms success and effectiveness interchangeably and one of the perspectives proposed in this chapter does the same for KM (DeLone and McLean, 1992 and 2003).

Jennex and Olfman (2005) summarized and synthesized the literature on KM/KMS critical success factors, CSFs, into an ordered set of 12 KM CSFs. CSFs were ordered based on the num-ber of studies identifying the CSF. The following CSFs were identified from 17 studies looking at 78 KM projects:

A knowledge strategy that identifies us-ers, sources, processes, storage strategy, knowledge, and links to knowledge for the KMS;

Motivation and commitment of users in-cluding incentives and training;

Integrated technical infrastructure includ-ing networks, databases/repositories, com-puters, software, KMS experts;

An organizational culture and structure that supports learning and the sharing and use of knowledge;

A common enterprise wide knowledge structure that is clearly articulated and eas-ily understood;

Senior management support including al-location of resources, leadership, and pro-viding training;

Learning organization; There is a clear goal and purpose for the

KMS;

Measures are established to assess the im-pacts of the KMS and the use of knowl-edge as well as verifying that the right knowledge is being captured;

The search, retrieval, and visualization functions of the KMS support easy knowl-edge use;

Work processes are designed that incorpo-rate knowledge capture and use;

Security/protection of knowledge.

However, these CSFs do not define KM/KMS success; they just say what is needed to be suc-cessful. Without a definition of KM/KMS success it is difficult to measure actual success.

Measuring KM/KMS success is important

To provide a basis for company valuation, To stimulate management to focus on what

is important, and To justify investments in KM activities

(Jennex and Olfman, 2005) (Turban and Aronson, 2001).

Besides these reasons from an organizational perspective, the measurement of KM and KMS success is important for building and implement-ing efficient KM initiatives and systems from the perspective of KM academics and practitioners (Jennex and Olfman, 2005).

PerSPectiVeS on KM/KMS SucceSS

The KM workshop at the 2006 HICSS-39 found that there were several perspectives on KM success. This section briefly summarizes these perspectives.

KM Success and effectiveness

One perspective on KM success is that KM suc-cess and KM effectiveness are interchangeable


and imply the same construct or variable. This is based on the view that effectiveness is a manifes-tation of success. An example would be increas-ing decision-making effectiveness to generate a positive impact on the organization resulting in successful KM. This perspective uses both process and outcome measures.

KM and KMS Success as interchangeable

Another perspective is that KM and KMS success are interchangeable. KMS success can be defined as making KMS components more effective by improving search speed, accuracy, etc. As an ex-ample, a KMS that enhances search and retrieval functions enhances decision-making effectiveness by improving the ability of the decision maker to find and retrieve appropriate knowledge in a time-lier manner. The implication is that by increasing KMS effectiveness, KMS success is enhanced and decision making capability is enhanced leading to positive impacts on the organization. This is how KM success is defined and it is concluded that enhancing KMS effectiveness makes the KMS more successful as well as being a reflection of KM success. The Jennex and Olfman KM Success Model (Jennex and Olfman, 2006), based on the DeLone and McLean (1992, 2003) IS Success Model, combines KM and KMS success and utilizes this perspective.

KM and KMS Success as Separate

As opposed to the previous section, this perspec-tive views KM and KMS success as separate measures. It is based on a narrow system view that allows for KMS success that does not trans-late into KM success. KMS are often seen as a sub-function of KM comprising technical and organizational instruments to implement KM. Thus, KMS success addresses implementation and operation factors in terms of system or process metrics whereas KM success is an assessment of

the value that these systems and processes provide to an organization. KM focuses therefore more on the outcome, while KMS focus more on the process. These perspectives are introduced in the following sections.

KM Success as a Process Measure

This perspective views KM success as a process measure. KM success can be described in terms of the efficient achievement of well defined or-ganizational and process goals by means of the systematic employment of both organizational instruments and information and communication technologies for a targeted creation and utilization of knowledge as well as for making knowledge available. KM is a support function to improve knowledge-intensive business processes. An example would be supporting the technology-forecasting process in an IT consulting firm by technical components of a KMS (Henselewski, et al., 2006). Complementary, the effective imple-mentation of knowledge processes (i.e. acquisi-tion, creation, sharing, and codification) is seen as a part of KM success. This perspective focuses therefore on measuring how much KM contributes to improving the effectiveness of business and knowledge processes.

KM Success as an outcome Measure

In contrast, KM success can be viewed as an out-come measure. KM success is therefore seen as a measure of the various outcomes of knowledge process capabilities existing within an organization as a result of undertaken KM initiatives. Typical outcomes in terms of organizational performance are the enhancement of:

Product and service quality, Productivity, Innovative ability and activity, Competitive capacity and position in the

market,


Proximity to customers and customer satisfaction,

Employee satisfaction, Communication and knowledge sharing,

and Knowledge transparency and retention.

KM Success as combined Process and outcome Measures

The last perspective views KM success as a combination of process and outcome measures. Respective descriptions of KM success focus on improved process effectiveness as well as on achieving actionable outcomes. The first and third perspectives contain examples for this combined approach.

MetHodologY

This chapter is exploratory research with the goal of guiding the KM community towards a consensus definition of KM success. The chapter builds on the results of an exploratory and a confirmatory survey (discussed below) reported in Jennex, et al., (2007). These survey results included a definition of KM success and identification of a set of dimensions and measures. As part of the confirmatory survey, respondents were asked what dimensions/measures they would add or delete from a list of those presented. This chapter analyzes these comments by tallying them and then putting them into context by comparing the KM success definition dimensions and measures to the Jennex Olfman (2006) KM Success Model.

The exploratory survey was generated through an expert panel approach. The 30 members of the editorial review board of the International Journal of Knowledge Management, IJKM, were asked to provide their definitions of KM success. Thirteen responses were received. These responses were used to generate an exploratory survey of KM success, which used 5-point Likert scale items to

solicit agreement on various perspectives and pro-posed KM success definitions. The perspectives were generated through an analysis of the expert board responses that distinguished two groups. The first grouping examined the measures used to determine KM success. Three subgroups were then observed: process-based measures, outcome-based measures, and a combination of process and outcome based measures. The second group-ing of responses provided two subgroups: those that combined KM and KMS success measures and those that viewed KM and KMS success as separate measures. A final observation was that many proposed definitions used success and ef-fectiveness interchangeably.

The exploratory survey also collected data on the KM expertise and focus of the respondents. Furthermore, the survey offered text boxes that al-lowed for free form input of additional KM success factors or measures, KM success definitions, and thoughts on the differences between KM and KMS success. The exploratory survey was administered using a web form with data collected and stored automatically. Survey respondents were solicited via broadcast emails to the ISWorld and DSI email list servers, to lists of KM researchers maintained by the authors, and to the editorial review board and list of authors for the International Journal of Knowledge Management, IJKM. An initial request was sent followed by a second request approximately one week later.

One hundred and three usable survey responses were received. Thirteen were from KM practitio-ners, 70 were from KM researchers, 6 were from KM students, and 14 were from others including academics interested in KM but not active KM researchers. Likert items were analyzed using means and standard deviations as no hypotheses have been proposed and need testing.

The results of the exploratory survey were used to generate a second survey. This survey presented a composite definition of KM success and a set of measures for each of the indicated dimensions. A 7-point Likert scale was used to solicit agreement


on the composite definition and each set of mea-sures. Additionally, as in the exploratory survey items were provided for collecting data on KM expertise and respondent focus. Also, each set of measures had boxes where respondents could indicate measures they would add or remove from each set of measures.

The second survey was also administered using a web form with respondents solicited in the same manner as the exploratory survey. One hundred and ninety-four usable survey responses were received. Sixteen were from KM practitio-ners, 114 were from KM researchers, 23 from KM students, and 41 were from others including academics interested in KM but not active KM researchers. Likert items were analyzed using means and standard deviations as no hypotheses have been proposed and need testing.

reSultS

There was little consensus on KM success perspec-tive or definition from the first survey while we did find agreement on a definition of KM success and measures of success in the second survey. The results of the first survey are summarized in Tables 1-3 while the results of the second survey are presented in Table 4.

Table 1 presents opinions with respect to the perspectives on KM success. The only perspective that tends to have any consensus agreement is that KM success is a combination of process and

outcome measures and is NOT just process or just outcomes. We are undecided if success and ef-fectiveness are equivalent measures and tend to be undecided to slightly against the idea that KM and KMS success are equivalent.

Overall n = 103, researcher n = 70, practitioner n=13, academics n=14, and student n=6, Values are rounded to 2 significant digits

Table 2 summarizes opinions on five sug-gested components of KM and KMS success definitions. There appears to be consensus on using organization-specific subjective measures derived for KM process capabilities. Examples of these capabilities include knowledge reuse, quality, relevance, effectiveness of acquisition, search, and application of knowledge, etc. There also appears to be consensus that any KM success definition should include providing the appropriate knowledge when needed. Additionally, there is consensus that use is not a good measure of KMS success. It is interesting to note that practitioners and students support the use of firm performance measures as indicators of KM success while there is less support for these measures from researchers and academics. It is also interesting to note that academics and students tend to support the use of measures reflecting direct returns from organi-zational and individual learning and application of knowledge while researchers and practitioners are less favorable to them.


Table 1. Opinions on KM success perspectives, mean (std dev) (5-point Likert scale)

Definition Overall Research Practice Other Student

Success = Effectiveness 3.1 (1.4) 3 (1.4) 3.3 (1.3) 3.2 (1.5) 3.7 (0.5)

KM = KMS Success 2.6 (1.5) 2.5 (1.4) 3.2 (1.6) 3.4 (1.5) 2.2 (1)

KM = KMS Measures 2.6 (1.4) 2.4 (1.4) 3.2 (1.6) 3 (1.4) 2.4 (0.9)

KM Success = Process 2 (1) 1.9 (0.9) 2.2 (1.1) 1.9 (0.8) 3 (1.3)

KM Success = Outcomes 2 (1) 2 (1) 2.2 (1.4) 1.7 (0.8) 2.3 (1)

KM Success = Process & Outcomes 4 (0.9) 3.9 (1) 3.8 (1) 4.3 (0.6) 4.2 (0.8)


Table 3 summarizes opinions on five suggested definitions of KM and KMS success. There ap-pears to be little consensus on these definitions other than a general neutrality on KM success as the flow of knowledge and KMS success as improving effectiveness of the KMS components.

However, there are some interesting observa-tions. KM success as the ability to leverage knowl-edge resources to achieve actionable outcomes is overall supported with the strongest support

coming from practitioners. This is interesting but not surprising as practitioners tend to favor definitions and measures that are objective, read-ily measurable, and have an obvious impact on the organization.

This is also why practitioners favor KM success as reusing knowledge to improve organizational effectiveness and KM success as the efficient achievement of well defined organizational goals for targeted creation and utilization of knowledge.

Table 2. Opinions on KM and KMS success definition components, mean (std dev) (5-point Likert scale)

Overall Research Practice Other Student

Subjective measure of various outcomes of KM processes capabilities should be included in a definition of KM success

4.1 (0.8) 4 (0.9) 4.3 (0.8) 4.2 (0.9) 4.5 (0.8)

Achieving direct returns from learning and projection should be included in a definition of KM success

3.8 (1) 3.7 (1) 3.6 (1) 4 (1) 4.3 (0.5)

Success of KMS should be measured in terms of pure usage statistics should be included in a definition of KM success

2.5 (1.2) 2.5 (1.2) 2.2 (1.1) 2.6 (1.2) 2.8 (1.2)

Success of KMS should be measured in terms of firm performance should be included in a definition of KM success

3.7 (1) 3.6 (1.1) 4.1 (1) 3.5 (0.8) 4 (0.9)

Providing the appropriate knowledge when needed should be included in a definition of KM success

4.2 (0.9) 4.2 (0.9) 4.3 (0.9) 4.4 (0.6) 4.3 (0.5)

Table 3. Opinions on KM and KMS success definitions; mean (std dev) (5-point Likert scale)

Overall Research Practice Other Students

KMS success can be defined as making KMS components more effective by improving search speed, accuracy, etc.

3 (1.2) 2.8 (1.1) 3.6 (1.2) 3.1 (1.1) 3.2 (1)

KM success is the ability to leverage knowledge resources to achieve actionable outcomes.

4 (1) 4 (1) 4.3 (0.9) 3.9 (0.9) 3.7 (1)

KM success is reusing knowledge to improve organizational effectiveness by providing the appropriate knowledge to those that need it when it is needed.

3.9 (1) 3.8 (1.1) 4.4 (0.91) 4.1 (0.7) 3.8 (0.4)

KM success is knowledge tacit and explicit alike circulates freely throughout the organization, with no debilitating clumping, clot-ting, or hemorrhaging.

3 (1.2) 2.8 (1.2) 3.2 (1.5) 3.4 (0.8) 2.7 (1)

KM success is the efficient achievement of well defined organizational and process goals by means of the systematic employment of both organizational instruments and information and communication technologies for a targeted creation and utilization of knowledge as well as for making knowledge available.

3.7 (1.2) 3.5 (1.3) 4.2 (1.1) 3.8 (0.9) 3.8 (1.2)



Table 4 summarizes opinions from the second survey on a proposed success definition generated from the first survey and sets of measures for the dimensions listed in the proposed definition. There appears to be some level of consensus on the proposed definition and measures. However, we do not consider it strong consensus given that the mean response is between agree and somewhat agree. Still, this is considered a strong beginning to establishing a common definition and set of success measures.

Overall n = 194, researcher n = 114, practitioner n=16, others n=41, and student n=23, Values are rounded to 2 significant digits

The comments were used to adjust the measures identified in the survey. However, a simple tallying

of the comments and adjusting the measures based on this tally was not useful. Instead, the comments suggested that the entire list of dimensions and measures in the context of a KM success model and CSFs had to be reviewed. These findings are discussed in the following paragraphs.

The impact on business processes dimension. The comments suggested adding innovation and agility as measures. They also supported removing labor-saving measures, refining learning through mistakes or insights, and clarifying the differences between action and outcome measures.

The strategy dimension. In this study, strategy refers to KM that is designed to support orga-nization-wide strategic systems and initiatives. The comments first questioned the meaning of strategy. They also suggested that social network analysis, SNA, measures should be added to pro-vide indicators of cohesiveness, centrality, and the

Table 4. Opinions on KM and KMS success definition and sets of measures, mean (std dev) (5-point Likert scale)

Overall Research Practice Other Student

KM success is a multidimensional concept. It is defined by capturing the right knowledge, getting the right knowledge to the right user, and using this knowledge to improve organizational and/or individual performance. KM success is measured using the dimensions of impact on business processes, strategy, leadership, efficiency and effectiveness of KM processes, efficiency and effectiveness of the KM system, organizational culture, and knowledge content.

5.4 (1.4) 5.3 (1.5) 6.1 (1.4) 5.6 (1.4) 5.5 (1.2)

Impact on business process measures

5.5 (1.3) 5.3 (1.4) 5.8 (1.4) 5.7 (1.2) 5.7 (1.0)

Strategy measures

5.3 (1.4) 5.1 (1.6) 6.1 (0.6) 5.3 (1.4) 5.7 (1.0)

Leadership measures

5.2 (1.5) 5.1 (1.5) 5.3 (1.5) 5.3 (1.3) 5.4 (1.6)

KM process effectiveness and efficiency measures

5.7 (1.3) 5.5 (1.4) 6.2 (0.8) 5.8 (1.3) 5.7 (1.4)

KM system effectiveness and efficiency measures

5.6 (1.3) 5.5 (1.4) 6.0 (0.7) 5.8 (1.2) 5.4 (1.3)

Learning culture measures

5.6 (1.2) 5.5 (1.4) 6.0 (0.8) 5.7 (1.1) 5.6 (1.2)

Knowledge content measures

5.4 (1.4) 5.2 (1.5) 6.0 (1.0) 5.7 (1.2) 5.5 (1.3)


strength of ties. Additional issues were raised with respect to strategy or alignment to strategy also impacting employee performance, and the way in which social capital and knowledge integration measure strategy.

The leadership dimension. The comments suggested adding social network analysis, SNA, measures that provide indicators of cohesiveness, centrality, and the strength of ties.

The KM process efficiency and effectiveness dimension. The comments questioned whether the measures should be lifecycle-based rather than process-based. Additionally, they suggested considering scalability, changing safe and effec-tive storage of knowledge to secure, private, and reliable storage of knowledge. However, these terms have conceptual definitions that differ from safe2, while effective in terms of storage is difficult to define. The comments furthermore questioned whether increased collaboration is a true measure for this dimension.

The KMS effectiveness and efficiency di-mension. The comments queried the synonymous use of usability and adaptability, questioned whether this dimension does in fact differ from KM process, and suggested that measures like maintenance costs and system measures such as maintainability and availability should be added.

The learning culture dimension. The com-ments questioned change in leadership culture as a leadership measure, and suggested adding orga-nizational learning as well as incentive measures.

The knowledge content dimension. The com-ments questioned whether retrieval does in fact differ from KMS retrieval and suggested adding integrity, temporal, lifecycle, visualization, and multifacetness measures. They furthermore sug-gested that knowledge creation measures should be part of the KM process dimension.

The questions raised by the comments sug-gest that there may be issues with the dimen-sions. This drove the analysis of the dimensions with the CSFs and the Jennex Olfman (2006) KM Success Model. An inspection of the list of

CSFs reveals conflicts that can affect the success dimensions. CSFs such as organizational culture, learning organization, and senior management support are regarded as necessary for KM to succeed. This in turn raises the question whether a dimension can be a CSF and, simultaneously, a reflection of success. We conclude that this is not likely, that CSFs are indeed necessary for KM success to occur, but are not reflections of KM success in and of themselves. This is borne out by the Jennex Olfman (2006) KM Success Model, as it is a causal model. This suggests that the success dimensions leadership and learning organization should be removed. Moreover, the success dimensions in the Jennex Olfman (2006) KM Success Model leads us to question whether a KMS effectiveness and efficiency dimension and perhaps even a KM process efficiency and effectiveness dimension are required as reflections of KM success. The following section provides a discussion that leads to the final definition of the dimensions of KM success.

diScuSSion

This was exploratory research so few conclusions can be drawn. However, using two surveys has allowed us to reach some consensus on a KM success definition and set of success measures. The consensus KM success definition is:

KM success is a multidimensional concept. It is defined by capturing the right knowledge, getting the right knowledge to the right user, and using this knowledge to improve organizational and/or individual performance. KM success is mea-sured using the dimensions of impact on business processes, strategy, leadership, efficiency and effectiveness of KM processes, efficiency and effectiveness of the KM system, organizational culture, and knowledge content.


Also, there are a few points of consensus that can be identified from the initial survey:

KM success and KMS success may not be the same thing.

Usage is not a good measure of KM or KMS success.

Additionally, it is possible that there is a dif-ferent focus on KM success between practitioners and researchers. Researchers do not seem to have a clear idea of KM success while practitioners appear focused on KM success as being tied to its impact on organizational performance and ef-fectiveness. This cannot be stated conclusively, the number of practitioner responses are too low (n=13) making this supposition. However, it is not unexpected that practitioners would have a focus on organizational impact as a measure of KM and KMS success. Given that KM is an action discipline, researchers should accept this focus and incorporate it into their investigations.

The preliminary set of success dimensions must be examined critically, though, as previous discussions have shown that there is conflict between what is regarded as an antecedent and thus necessary for success, and what is regarded as a reflection of success. This is made more complex as factors that are antecedents to KM need to remain to sustain continued KM success. We therefore start this discussion by examining the research behind the CSF of organizational and learning cultures.

In an executive development program, Alavi and Leidner (1999) surveyed executive partici-pants with respect to what was required for a suc-cessful KMS. They found that organizational and cultural issues associated with user motivation to share and use knowledge are the most significant. Yu et al. find that KM drivers such as a learning culture, knowledge sharing intention, KMS qual-ity, rewards, and KM team activity significantly affect KM performance (Yu, et al., 2004). These conclusions were deduced from a survey of 66

Korean firms. Cross and Baird propose that KM will not improve business performance by simply using technology to capture and share the lessons of experience Cross and Baird (2000). They postulate that for KM to improve business performance, it had to increase organizational learning through the creation of organizational memory. Subsequently, 22 projects were examined to investigate this. The conclusion is that improving organizational learn-ing improves the likelihood of KM success. Chan and Chau (2005) deduce lessons learned from a failed case of KM in a Hong Kong organization and find a need for a knowledge-sharing culture. In their study of KM abandonment in four KM projects, Lam and Chua (2005) identify CSFs for KM from the literature, including a learning cul-ture. Other studies identifying a learning culture as a CSF include Goh (2002), McDermott and ODell (2001), Zolingen, et al. (2001).

The above research examined successful and failed KM and, on the whole, concludes that an appropriate organizational culture and learning culture are necessary antecedents to KM success, but are not an outcome of KM success although. Nevertheless, it can also be concluded that suc-cessful KM should lead to the strengthening of organizational and learning cultures. However, it is difficult to quantify measurements of change in culture, which leads to the decision that orga-nizational and learning cultural measures of KM success should be dropped and used only as CSFs.

Leadership is an interesting concept. The CSF of senior management support can be considered leadership and it has been found to be necessary for KM to succeed, but can leadership also be a re-flection of KM success? In their above-mentioned study, one of Chan and Chaus (2005) key findings is the need for continued top management support and involvement. Davenport et al. (1998) studied 31 projects in 24 companies (18 were successful, five were considered failures, and eight were too new to be rated). Eight CSFs, including senior management support, were common in successful KM projects. Jennex and Olfman (2000) studied

10

Towards a Consensus Knowledge Management Success Definition

three KM projects and also observed senior management support as a CSF. In their above-mentioned study, Lam and Chua (2005) also iden-tify continuous top management support (as also identified by Storey and Barnett, 2000) as a CSF. Holsapple and Joshi (2000) investigated factors that influenced the management of knowledge in organizations by using a Delphi panel consisting of 31 recognized KM researchers and practitioners and find leadership and top management commit-ment/support to be crucial. This finding is also supported by Bals et al.s (2007) study on key success factors for a successful KM initiative in a global bank. Furthermore, several researchers have demonstrated the need to create incentives and motivation within the organization to create and reuse knowledge (Davenport, et al. (1998), Ginsberg and Kambil (1999), Jennex and Olfman (2000), Lam and Chua (2005), Sage and Rouse (1999), Yu, et al. (2004)). Finally, Malhotra and Galletta (2003) identify the critical importance of user commitment and motivation through a survey study of users of a KMS implemented in a health care organization.

The above research found that continuous senior management support is a CSF and also necessary for sustaining KM success. Leadership indicates support for KM, providing the manage-ment environment that encourages KM through knowledge creation and reuse by members of the organization, and providing adequate resources for the KM/KMS initiative. This is an antecedent to KM success and also an outcome of KM success as successful KM reinforces knowledge leadership.

Why do we argue that culture is a CSF but not an output of KM success, while leadership is argued to be both? It is our opinion that culture is not changed quickly, that it takes much time to effect cultural changes but that individuals can be changed quickly, and that success breeds success, i.e. that successful KM will encourage senior management to push KM even more.

Strategy as a dimension can be discussed briefly as the only point of contention is what it actually

refers to. This dimension refers to the impact of KM on organizational strategy. This can occur through impacts on organizational and/or strategic systems, on strategic intelligence gathering, or merely on fulfilling strategy. This dimension dif-ferentiates between impacts on business systems and strategic systems; it examines organizational impacts instead of localized impacts. The decision is therefore that this dimension needs to be renamed and is thus changed to impacts on strategy.

The next dimensions needing discussion are KM and KMS efficiency and effectiveness. Since this chapter takes the perspective that KM and KMS success are essentially similar, it fol-lows that as success dimensions they should be similar. However, should they even be success dimensions? It is clear that they are antecedents to KM success, but are improvements in efficiency and effectiveness resultants and measures of KM success? Using the Jennex Olfman (2006) KM Success Model, we determine that these two di-mensions are not measures of KM success. While it is agreed that improving KM/KMS effectiveness and efficiency will enhance KM and knowledge reuse in an organization, we reject the notion that simply being more effective or efficient in KM/KMS is a reflection of KM success.

The final dimension needing discussion is knowledge content. At first, it seems as if this dimension should be treated the same as KM/KMS effectiveness and efficiency. This is, however, rejected. Instead, we accept that knowledge con-tent is a reflection and measure of KM success, as well as being an antecedent to KM success. The Jennex Olfman (2006) KM Success Model is the basis for this determination. The knowledge quality dimension is an antecedent to KM success; however, there is also a feedback process from the impact of KM use to guide further knowledge content and quality. Much like leadership, it is anticipated that KM success will be reflected in the increased quantity and quality of knowledge content; and that a lack of KM success will also be

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reflected in a decrease in the quantity and quality of knowledge content.

There are some limitations to this research. It is quite possible that the reason little consensus has been observed is because KM and KMS success are complex constructs that are multidimensional. It may be that KM and KMS success includes outcome measures, quality of knowledge, how well the KM processes function, organizational culture measures, usability measures, and strategy measures. This is consistent with the DeLone and McLean model of Information Systems success (DeLone and McLean, 1992 and 2003) and there is much empirical evidence to support the correct-ness of this model. This model is also the basis of the Jennex and Olfman KM Success Model (Jen-nex and Olfman, 2006). It is quite likely that the exploratory survey used for this research, while generated using an expert panel, probably did not capture the multidimensional nature of the pro-vided KM success definitions and therefore made it difficult for respondents to find statements they fully agreed with. This limitation was considered when generating the second survey and it appears that this has improved consensus with the KM success definition generated from the first survey.

concluSion

It is difficult to reach any conclusions with this research; no hypotheses were proposed or tested. This is okay as the purpose of this chapter is to propose a definition of KMS success. Before doing this it is important to identify areas of consensus and areas of disagreement. The following points are areas of agreement:

KM and KMS success are likely different definitions (note that at least one of the au-thors greatly disagrees with this point).

Use is a poor measure of KM and KMS success.

KM success is likely a multidimensional construct that will include process and out-come measures.

A base definition of KM success is: KM success is reusing knowledge to improve organizational effectiveness by providing the appropriate knowledge to those that need it when it is needed.

Additionally, a base definition of KM success can be established:

KM success is a multidimensional concept. It is defined by capturing the right knowledge, getting the right knowledge to the right user, and using this knowledge to improve organizational and/or individual performance. KM success is measured by means of the dimensions: impact on business processes, impact on strategy, leadership, and knowledge content.

Some areas of disagreement are in further need of discussion:

KM success and effectiveness are likely the same and will be able to use the same measures.

KM and KMS success are essentially the same (in deference to the authors and con-sistent with a Churchman view of a KMS and DeLone and McLean (DeLone and McLean, 1992 and 2003)).

The role of learning and firm performance in KM success.

The role of outcome measures such as speed, accuracy, amount of knowledge stored and used, etc. in KM and KMS success.

It is expected that it will take a great deal of research before consensus is reached on what KM and KMS success is. It is concluded that these findings from an exploratory survey are a good starting point for this discussion.

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