Title: Knowledge management evaluation criteria for industries: identification and interpretive structural modelling
Authors: Mohit Maheshwarkar; Nagendra Sohani
Addresses: Department of Mechanical Engineering, Oriental University, Indore, MP 453555, India; Department of Mechanical Engineering, Institute of Engineering and Technology, Devi Ahilya Vishwavidyalaya, Indore, MP 452017, India ' Department of Mechanical Engineering, Oriental University, Indore, MP 453555, India; Department of Mechanical Engineering, Institute of Engineering and Technology, Devi Ahilya Vishwavidyalaya, Indore, MP 452017, India
Abstract: Today, organisations are focusing on different aspects of knowledge management (KM). KM has emerged itself as one of the promising disciplines for the organisations. In spite of having a lot of literature available and ongoing research, the concept of KM suffers from some of the basic problems; and one of such problems is evaluation. Organisations face problems in evaluating the efforts of KM. One of the basic reasons behind this problem is intangible nature of knowledge. Another but equally important problem is the generalisation of KM evaluation procedures. No, doubt there are a lot of research papers available heading evaluation of KM, but all govern different KM evaluation procedures. This research paper tries to find out the generalised KM criteria using the concept of factor analysis. The research work includes the identification of 44 KM evaluation criteria followed by a survey of 232 candidates, belong to both manufacturing and service organisations from different provinces of India, and, then generalisation of results using principal component analysis. As the last stage of the research, interpretive structural modelling (ISM) approach is also applied to an identified set of criteria in order to investigate the existing interrelationships among them.
Keywords: knowledge management; KM; evaluation criteria; principal component analysis; PCA; interpretive structural modelling; ISM.
International Journal of Knowledge Management Studies, 2019 Vol.10 No.3, pp.227 - 250
Received: 19 Jan 2018
Accepted: 05 Feb 2018
Published online: 01 Aug 2019 *