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Knowledge Management Implementation: A Predictive Model Using an Analytical Hierarchical Process

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  • A. Anand
  • R. Kant
  • D. Patel
  • M. Singh

Abstract

The aim of this paper is to understand knowledge management enablers (KMEs) and to identify priority weights to evaluate the strength of the corresponding factors present before knowledge management (KM) implementation. It uses analytic hierarchy process (AHP) methodology to prioritize KMEs that support the KM implementation in organizations. Further, a questionnaire-based survey was also conducted to rank the KMEs. These KMEs were selected from literature reviews and expert discussion. The AHP method, which has the ability to structure complex, multiperson, multiattribute, and multiperiod problem hierarchically, has been used. Pairwise comparisons of KMEs (usually, alternatives and attributes) can be established using a scale indicating the strength with which one KME dominates another with respect to a higher level KME. This scaling process can then be translated into priority weights. The AHP can be a useful guide in the decision-making process of KM implementation. It has been observed that KME11 has high priority weights. Copyright Springer Science+Business Media New York 2015

Suggested Citation

  • A. Anand & R. Kant & D. Patel & M. Singh, 2015. "Knowledge Management Implementation: A Predictive Model Using an Analytical Hierarchical Process," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 6(1), pages 48-71, March.
    • Handle: RePEc:spr:jknowl:v:6:y:2015:i:1:p:48-71
    DOI: 10.1007/s13132-012-0110-y
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    References listed on IDEAS

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    1. Tomas Cherkos Kassaneh & Ettore Bolisani & Juan-Gabriel Cegarra-Navarro, 2021. "Knowledge Management Practices for Sustainable Supply Chain Management: A Challenge for Business Education," Sustainability, MDPI, vol. 13(5), pages 1-15, March.

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