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IJML 2012 Vol.2(6): 878-881
doi: 10.7763/IJMLC.2012.V2.257

Evaluation of Knowledge Management Maturity

Hossein Amoozad Khalili1 , Anahita Maleki2 , Azita Sherej Sharifi3 , Mohamad Hasan Salmani4 , Somaye Rahnama Farshami4

  • 1Industrial Engineering Department, Islamic Azad University Nowshahr Branch, Nowshahr, Mazandaran, Iran.
  • 2Industrial Engineering Department, Iran University of Science and Technology, Tehran, Iran.
  • 3Management and accounting Department, Islamic Azad University, Nowshahr Branch, Nowshahr, Mazandaran, Iran.
  • 4Industrial Engineering Department, Payamnur University, Tehran Center Branch, Tehran, Iran.

Abstract

Confronting fierce global competition, organizations have implemented knowledge management to enhance competitive advantages. With increasing investments in resources for knowledge management implementation in many organizations, measuring knowledge management maturity has become an important agenda among researchers and practitioners. This study develops a knowledge navigator model (KNMTM) to navigate knowledge management (KM) implementation journey. The KNM comprises two frameworks: evaluation and calculation framework .

Keywords

  • Knowledge
  • management;
  • maturity;
  • knowledge
  • navigator
  • model
257-L40095

How to Cite

Copied

Hossein Amoozad Khalili, Anahita Maleki, Azita Sherej Sharifi, Mohamad Hasan Salmani, and Somaye Rahnama Farshami, "Evaluation of Knowledge Management Maturity," International Journal of Machine Learning, vol. 2, no. 6, pp. 878-881, 2012. https://doi.org/10.7763/IJMLC.2012.V2.257

Copyright & License

Copyright © 2012 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • DOI: 10.18178/IJML
  • Frequency: Quarterly
  • Average Days to Accept: 68 days
  • Acceptance Rate: 27%
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • DOI: 10.18178/IJML
  • Abstracing/Indexing: Google Scholar, Crossref, ProQuest, Electronic Journals Library, CNKI.
  • E-mail: editor@ijml.org
  • APC: 500USD