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 () and Selim Zaim ()
Additional contact information
Ibrahim Yazici: Istanbul Technical University
Omer Faruk Beyca: Istanbul Technical University
Omer Faruk Gurcan: Istanbul Technical University
Halil Zaim: Business Administration, American University of the Middle East
Dursun Delen: Oklahoma State University
Selim Zaim: Istanbul Sabahattin Zaim University

Annals of Operations Research, 2022, vol. 308, issue 1, No 28, 753-776

Abstract: 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.

Keywords: Knowledge management; Individual tacit knowledge; Machine learning; Support vector machines (SVM); Artificial neural networks (ANN); Fuzzy analytic hierarchical process (AHP) (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s10479-020-03697-3 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:annopr:v:308:y:2022:i:1:d:10.1007_s10479-020-03697-3

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10479

DOI: 10.1007/s10479-020-03697-3

Access Statistics for this article

Annals of Operations Research is currently edited by Endre Boros

More articles in Annals of Operations Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().