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Knowledge Management Enablers: Metadata Analysis for KM Implementation

Author

Listed:
  • Hiteshkumar G. Shah

    (Shri S’ad Vidya Mandal Institute of Technology, Old National Highway No. 8, Bharuch 392001, Gujarat, India)

  • Ravi Kant

    (Department of Mechanical Engineering, S.V. National Institute of Technology, Surat 395007, India)

Abstract

The purpose of the study is to exhibit total useful contents into the knowledge management enablers (KMEs) for knowledge management (KM) implementation. The literature review covers an exhaustive analysis of KMEs in KM implementation with the existing parameters as well as those which are not being covered in past investigations of KMEs. This study is for analysing the effects of KMEs in KM implementation within academics and industries in the last 20 years. The design of the study is based on exhaustive literature review of KMEs for KM implementation of 148 papers selected through an organised procedure, from about 1050 research investigations till August 2017. The findings reflect a deep interest in incorporating KMEs in KM implementation. They also indicate the model of survey scored better among all the research methods used in the domain as per major findings. In broad terms, most of the research is interpretative in nature and provides a framework for hypothetical analysis. The assessment just provides outline of statistics which is easy to identify for academicians as well as practitioners and offers improved insights on the present condition of study in support of KM implementation.

Suggested Citation

  • Hiteshkumar G. Shah & Ravi Kant, 2018. "Knowledge Management Enablers: Metadata Analysis for KM Implementation," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 17(04), pages 1-29, December.
    • Handle: RePEc:wsi:jikmxx:v:17:y:2018:i:04:n:s0219649218500363
    DOI: 10.1142/S0219649218500363
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    References listed on IDEAS

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    1. R. Kant & M.D. Singh, 2009. "Knowledge management implementation: modelling the variables," International Journal of Innovation and Learning, Inderscience Enterprises Ltd, vol. 6(3), pages 342-361.
    2. Piera Centobelli & Roberto Cerchione & Emilio Esposito, 2017. "Knowledge Management in Startups: Systematic Literature Review and Future Research Agenda," Sustainability, MDPI, vol. 9(3), pages 1-19, March.
    3. Pao-Lien Wei & Jen-Hung Huang & Gwo-Hshiung Tzeng & Shwu-Ing Wu, 2010. "Causal Modeling Of Web-Advertising Effects By Improving Sem Based On Dematel Technique," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 9(05), pages 799-829.
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