Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32127
Using Interpretive Structural Modeling to Determine the Relationships among Knowledge Management Criteria inside Malaysian Organizations

Authors: Reza Sigari Tabrizi, Yeap Peik Foong, Nazli Ebrahimi


This paper is concerned with the establishment of relationships among knowledge management (KM) criteria that will ensure an essential foundation to evaluate KM outcomes. The major issue under investigation is to assess the popularity of criteria within organizations and to establish a structure of criteria for measuring KM results. An empirical survey was conducted among Malaysian organizations to investigate KM criteria for measuring success of KM initiatives. Therefore, knowledge workers as the respondents were targeted to establish a structure of criteria for evaluating KM outcomes. An established structure of criteria based on the Interpretive Structural Modeling (ISM) is used to map criteria relationships inside organizations. This structure is portrayed to identify that how these set of criteria are related. This network schema should be investigated and implemented to promote innovation and improve enterprise performance. To the researchers, this survey has significant insights into relationship between KM programs and business success.

Keywords: Knowledge Management, Knowledge ManagementOutcomes, KM Criteria, Innovation, Interpretive Structural Modeling

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3484


[1] Vittal S. Anantatmula,Outcomes of Knowledge Management Initiatives. 2005, International Journal of Knowledge Management, pp. 50-67.
[2] Chong Siong. Choy, Criteria for measuring KM performance outcomes in organisations. Kuala Lumpur : s.n., 2006. Knowledge Management Conference & Exhibition (KMICE). pp. pp. 123-131.
[3] Mark E. Van Buren, A Yardstick for Knowledge Management. 1999, Training & Development, pp. 71-78.
[4] E. Turban and J.E. Aronson, Decision support systems and intelligent systems. 6th edition. s.l. : Prentice Hall, 2001.
[5] R. Austin and P. Larkey, The future of performance measurement: Measuring knowledge work.
[book auth.] In A. Neely (Ed.). Business Performance Measurement. Theory and Practice. s.l. : Cambridge University Press, 2002.
[6] J. Ahn and S. Chang, Valuation of knowledge: A business performanceoriented methodology. . Hawaii : HICSS35, IEEE Computer Society. , 2002. The 35th Hawaii International Conference on System Sciences, .
[7] A. Fairchild, Knowledge manage metrics via a balanced scorecard methodology. Hawaii : s.n., 2002. 35th Hawaii International Conference on System Sciences.
[8] Vittal. Anantatmula and Shivraj. Kanungo, Establishing and Structuring Criteria for Measuring Knowledge Management Efforts. 2005. 38th Hawaii International Conference on System Sciences. pp. 1-11.
[9] Jr. Ronald D. Fricker, Sampling Methods for Web and E-mail Surveys. Naval Postgraduate School.
[Online] October 9, 2006.
[Cited: May 5, 2010.] 20Internet%20Survey%20Sampling%20Chapter.pdf.
[10] Chong Siong. Choy, Kuan Yew. Wong, and Binshan. Lin, Criteria for measuring KM performance outcomes in organisations. 7, 2006, Industrial Management & Data Systems, Vol. 106, pp. 917-936.
[11] Rick Gorvett and Ningwei. Liu, Using Interpretive Structural Modeling to Identify and Quantify Interactive Risks. (Online) 2007. (Cited: May 1, 2010.)
[12] AP. Sage, Interpretive Structural Modeling: Methodology for Largescale Systems. New York, NY : McGraw Hill, 1977. pp. 91-164.
[13] M. D. Singh and R. Kant, Knowledge management barriers: An interpretive structural modeling approach. 2, 2008, International Journal of Management Science and Engineering Management, Vol. 3, pp. 141- 150.