A comparative analysis of machine learning techniques and fuzzy analytic hierarchy process to determine the tacit knowledge criteria

Ibrahim Yazici, Omer Faruk Beyca, Omer Faruk Gurcan, Halil Zaim, Dursun Delen*, Selim Zaim

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)

Abstract

Knowledge management is widely considered as a strategic tool to increase firm performance by enabling the reuse of organizational knowledge. Although many have studied knowledge management in a variety of business settings, the concept of tacit knowledge, especially the individual one, has not been explored in due detail. The objective of this study is to identify and prioritize individual tacit knowledge criteria and to explain their effects on firm performance. In the proposed methodology, first, the most prevalent individual tacit knowledge variables are identified by means of knowledge elicitation and feature selection methods. Then, the extracted variables were prioritized using machine learning methods and fuzzy Analytic Hierarchy Process (AHP). Support vector machine (SVM), logistic regression, and artificial neural networks are used as the first approach, followed by fuzzy AHP as the second approach. Based on the comparative analysis results, SVM (as the best-performed machine-learning technique) and fuzzy AHP methods were identified for the subsequent analysis. The results showed that both SVM and fuzzy AHP determined time efficiency of employees, communication between employees and supervisors, and innovative capability of employees as the most important tacit knowledge criteria. These findings are mostly supported by the extant literature, and collectively shows the synergistic nature of the utilized analytics approaches in determining individual tacit knowledge criteria.

Original languageEnglish
Pages (from-to)753-776
Number of pages24
JournalAnnals of Operations Research
Volume308
Issue number1-2
DOIs
Publication statusPublished - Jan 2022

Bibliographical note

Publisher Copyright:
© 2020, Springer Science+Business Media, LLC, part of Springer Nature.

Keywords

  • Artificial neural networks (ANN)
  • Fuzzy analytic hierarchical process (AHP)
  • Individual tacit knowledge
  • Knowledge management
  • Machine learning
  • Support vector machines (SVM)

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