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Knowledge Management Implementation: Modeling the Barriers

Author

Listed:
  • R. Kant

    (Department of Mechanical Engineering, Motilal Nehru National Institute of Technology, Allahabad, 211004, India)

  • M. D. Singh

    (Department of Mechanical Engineering, Motilal Nehru National Institute of Technology, Allahabad, 211004, India)

Abstract

The purpose of this paper is to present an approach to successful KM implementation by understanding the dynamics among various knowledge management barriers (KMBs) that hinder the implement of KM in organisations. Using interpretive structural modeling (ISM), the research presents a hierarchy-based model and the mutual relationship among the KMBs. The research shows that there is a group of KMBs having high driving power and low dependence which require maximum attention and are of strategic importance. Another group consists of those KMBs which have high dependence and low driving power which necessitate the resultant actions. This categorisation provides a useful tool to top management to differentiate between independent and dependent KMBs and their mutual relationships which would help them to focus on those key KMBs that are most significant for effective KM implementation. Arrangement of KMBs in a hierarchy and the categorisation into driver and dependent categories is an exclusive effort in the area of KM implementation.

Suggested Citation

  • R. Kant & M. D. Singh, 2008. "Knowledge Management Implementation: Modeling the Barriers," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 7(04), pages 291-305.
  • Handle: RePEc:wsi:jikmxx:v:07:y:2008:i:04:n:s021964920800210x
    DOI: 10.1142/S021964920800210X
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    Cited by:

    1. Ali Rezaeian & Rouhollah Bagheri, 2018. "Modeling the Factors that Affect the Implementation of Knowledge Networks," Foresight and STI Governance (Foresight-Russia till No. 3/2015), National Research University Higher School of Economics, vol. 12(1), pages 56-67.
    2. Wang, Rui & Guo, Li-ming & Cao, Chao & Chen, Yan-sheng, 2023. "The key success factors of the AI industry entrepreneurial process in China Great Bay Area: A systematic approach study," Technological Forecasting and Social Change, Elsevier, vol. 186(PB).

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