A comparative analysis of machine learning systems for measuring the impact of knowledge management practices

Dursun Delen, Halil Zaim, Cemil Kuzey, Selim Zaim

Research output: Contribution to journalArticle

31 Scopus citations

Abstract

Knowledge management (KM) has recently emerged as a discrete area in the study of organizations and frequently cited as an antecedent of organizational performance. This study aims at investigating the impact of KM practices on organizational performance of small and medium-sized enterprises (SME) in service industry. Four popular machine learning techniques (i.e., neural networks, support vector machines, decision trees and logistic regression) along with statistical factor analysis (EFA and CFA) are used to developed predictive and explanatory models. The data for this study is obtained from 277 SMEs operating in the service industry within the greater metropolitan area of Istanbul in Turkey. The analyses indicated that there is a strong and positive relationship between the implementation level of KM practices and organizational performance related to KM. The paper summarizes the finding of the study and provides managerial implications to improve the organizational performance of SMEs through effective implementation of KM practices.

Original languageEnglish
Pages (from-to)1150-1160
Number of pages11
JournalDecision Support Systems
Volume54
Issue number2
DOIs
StatePublished - 1 Jan 2013
Externally publishedYes

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Keywords

  • Impact analysis
  • Knowledge management
  • Machine learning
  • Predictive modeling
  • Service industry

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