Exploring barriers to knowledge flow at different knowledge management maturity stages
Introduction
In implementing a KM system in an organization, it is important to understand what and how different barriers to knowledge flow affect its progress, as well as knowing how both your firm and your opponents can win in the competitive environment. Hence, it is important for organizations to assess difficulties they may meet while implementing KM initiatives.
During the last few decades, there have been several KM initiatives that have been widely studied. An industry survey of 811 large enterprises in North America and Europe conducted by Desouza [10] in 1999 revealed that 90% of them recognized the importance of KM, and most of them had KM activities underway. In addition, a study by AMR Research [3] estimated that companies in the United States would spend close to $85 billion on KM in 2008, an increase of nearly 16% from 2007.
To business entities, KM is an essential managerial activity if they are to sustain their competitive advantages in today's information economy. There has been a corresponding wave of interest both from researchers and practitioners recently. Knowledge has been recognized as a critical resource [28], as it provides the foundation for competitive advantages. Knowledge allows organizations to predict the nature and commercial potential of changes in the environment, as well as the appropriateness of their strategic decisions. The ability of firms to capture, organize, and disseminate knowledge allows them to improve the quality of decision making, process efficiency, customer satisfaction, and cost control. As knowledge has been widely recognized as a valuable resource in helping organizations to sustain competitive advantages, firms are increasingly investing in KM initiatives to promote the sharing, application, and creation of knowledge to develop more competitive situations and attain business goals [22], [16].
Still, there are a number of challenges that arise during the KM developing progress. For example, knowledge is a complex and multi-faceted concept in and is embedded in many entities and/or activities in an organization, including the organization's culture, policies, documents, and the employees [15]. The problems of KM implementations vary according to the context and KM maturity level. While research into and practices of KM have recently grown rapidly, the KM field has been criticized as being confusing due to lack clarity with respect to its definitions and framework. To overcome these problems, Knowledge Management Maturity (KMM) [21] provides a way to evaluate each level of KM progress; in this context maturity is the extent to which a specific process is explicitly defined, managed, measured, controlled, and effective. Although many studies have considered the potential benefits of KM, very little attention has been paid to surveying professionals about their way of developing KM and assessing the issues related to its maturity and clarity [4].
In our study, we first proposed that barriers to knowledge flow are likely to be different at different KM maturity levels. We therefore explored what and how they are changed when KMM levels change, and examined the influence and impact of change at each level. To achieve this, a revised CHAT model [20] was used to classify the barriers comprehensively, and a Knowledge Navigator Model (KNM) [14], was adopted to evaluate the KM maturity level. In order to explore the dynamics of the barriers to knowledge flow in different KMM levels, a longitudinal observation survey, questionnaires, in-depth face-to-face interviews, and quantitative analysis were conducted with the cooperation of KM experts in seven firms.
Section snippets
Knowledge flow
Although knowledge flow is invisible it works with any cooperative team, whether used it intentionally or not. It has been defined as a process of knowledge passing between people or knowledge processing mechanisms; Zhuge [30] stated that it was “the passing of knowledge between nodes according to certain rules and principles.” Here a knowledge node is a team member or role, or a knowledge portal or process. A node can generate, learn process, understand, synthesize, and deliver knowledge.
Research methodology
We approached our research mainly from a knowledge flow and KMM perspective, and the process primarily involved a longitudinal survey, using questionnaires containing items related to barriers to knowledge flow, KMM model, and the background of the sample companies. In order to probe more deeply into barriers differences, KMM level, and the sample firms’ background variables, face-to-face interviews using content analysis procedures were conducted with senior experts from the companies; our
Analysis process
The first stage in the qualitative analysis was to examine the transcripts of the face–face interviews in order to determine their structure and the direction of the interaction. All the information from the surveys and interviews was coded for statistical and content analyses. Descriptive statistical analysis was employed to the structured questions, while thematic analysis was used to analyze the interview transcripts to obtain open-ended data. Thus the interview data was narrowed down to
Discussion of findings
The related issues and implications are discussed from three perspectives:
- (1)
The barriers to knowledge flow are different at different KMM levels and they change in association with KM development.
The benefits of using maturity models to explore the barriers to knowledge flow are that they provide the following a better understanding of the current status of KM activities when organizations are implementing KM; a road map for navigating the barriers in different stages; and a guide to help KM
Conclusions
Although the literature about knowledge flow and KMM model has often been cited as significantly important, our study provided evidence that supported the concept that barriers to knowledge flow is different at various KMM levels. In addition, what and how KMM levels are different and associated with changing KM maturity levels was explored deeply.
Acknowledgements
This work was supported by National Science Council, Taiwan, ROC, under Grant NSC 96-2752-H-006-002-PAE. In addition, we particularly appreciate comments from the experts who participated in this research, especially the director of IBM in Taiwan and the data collection support from Tzu-Jung Huang, from the Institute of Information Management at National Cheng Kung University.
Chinho Lin is a distinguished professor of the Department of Industrial and Information Management & Institute of Information Management at National Cheng Kung University, Taiwan (ROC). He received his Ph.D. in business administration from the City University of New York. His works have been published in Information & Management, Decision Support Systems, Decision Sciences, European Journal of Operations Research, Omega, International Journal of Production Research, Journal of Operational
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Cited by (0)
Chinho Lin is a distinguished professor of the Department of Industrial and Information Management & Institute of Information Management at National Cheng Kung University, Taiwan (ROC). He received his Ph.D. in business administration from the City University of New York. His works have been published in Information & Management, Decision Support Systems, Decision Sciences, European Journal of Operations Research, Omega, International Journal of Production Research, Journal of Operational Research Society, and other journals. His current research interests include knowledge management, supply chain management, quality and reliability management, and technology management.
Ju-Chuan Wu is currently a lecturer of the Department of Information Management at National Penghu University of Science and Technology, Taiwan (ROC). She received her M.B.A. in Institute of Information Management at National Chung Cheng University and now is a Ph.D. candidate of Institute of Information Management at National Cheng Kung University. Her current research interests are in the field of knowledge management and performance measurement, with a special focus on intangible assets, intellectual capital, and knowledge sharing.
David C. Yen is currently a Raymond E. Glos professor in business and a professor of MIS of the Department of Decision Sciences and Management Information Systems at Miami University. He is active in research and has published books and articles which have appeared in Communications of the ACM, Decision Support Systems, Information & Management, Information Sciences, Computer Standards and Interfaces, Government Information Quarterly, Information Society, Omega, International Journal of Organizational Computing and Electronic Commerce, and Communications of AIS among others. His research interests include data communications, electronic/mobile commerce, database, and systems analysis and design.