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

YAZICI İ., Beyca Ö. F., Gürcan Ö. F., Zaim H., Delen D., Zaim S.

ANNALS OF OPERATIONS RESEARCH, cilt.308, sa.1-2, ss.753-776, 2022 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 308 Sayı: 1-2
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1007/s10479-020-03697-3
  • Dergi Adı: ANNALS OF OPERATIONS RESEARCH
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, ABI/INFORM, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Computer & Applied Sciences, INSPEC, Public Affairs Index, zbMATH, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.753-776
  • Anahtar Kelimeler: Knowledge management, Individual tacit knowledge, Machine learning, Support vector machines (SVM), Artificial neural networks (ANN), Fuzzy analytic hierarchical process (AHP), INTERNATIONAL JOINT VENTURES, RESOURCE-BASED VIEW, FIRM PERFORMANCE, PRACTICAL INTELLIGENCE, MANAGEMENT, AHP, INNOVATION, INTEGRATION, SELECTION, MODEL
  • Sivas Cumhuriyet Üniversitesi Adresli: Evet

Özet

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 determinedtime efficiency of employees,communication between employees and supervisors, andinnovative capability of employeesas 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.