Enhancing Ethical Leadership Responses: Application of Dimension Reduction and Fermatean Fuzzy MEREC-WASPAS Methods

Cemre Çınar; Gergely Czukor; Hasan Dinçer; Dragan Pamucar; Serkan Eti; Serhat Yüksel

doi:10.31181/dma412026187

Authors

Cemre Çınar School of Business, Özyeğin University, Istanbul, Turkiye https://orcid.org/0000-0003-0740-4027
Gergely Czukor Faculty of Social Sciences and Humanities, İstanbul Bilgi University, İstanbul, Turkiye https://orcid.org/0000-0003-1612-2937
Hasan Dinçer 1) School of Business, Istanbul Medipol University, Istanbul, Turkiye; 2) Department of Economics and Management, Khazar University, Baku, Azerbaijan; 3) University College, Korea University, Republic of Korea https://orcid.org/0000-0002-8072-031X
Dragan Pamucar 1) UNEC Applied Artificial Intelligence Research Center, Azerbaijan State University of Economics (UNEC), Baku, Azerbaijan; 2) Faculty of Engineering, Dogus University, Umraniye, Istanbul, Turkiye https://orcid.org/0000-0001-8522-1942
Serkan Eti 1) Faculty of Applied Sciences, Marmara University, Istanbul, Turkiye; 2) ARUCAD Research Centre, Arkin University of Creative Arts and Design, Northern Cyprus, Mersin 10, Turkiye https://orcid.org/0000-0002-4791-4091
Serhat Yüksel 1) School of Business, Istanbul Medipol University, Istanbul, Turkiye; 2) Department of Economics and Management, Khazar University, Baku, Azerbaijan https://orcid.org/0000-0002-9858-1266

DOI:

https://doi.org/10.31181/dma412026187

Keywords:

Ethical Governance, Machine learning, Fermatean fuzzy sets, MEREC, WASPAS

Abstract

Identifying priority strategies for improving the ethical leadership response model in companies is essential for the effective allocation of financial and human resources. This study aims to evaluate and improve the performance of the ethical leadership response model by developing a novel fuzzy decision-making framework. In the first stage, decision-maker weights are calculated using a dimensionality reduction method. In the second stage, the weights of the selected criteria are determined using Fermatean fuzzy MEREC. In the third stage, policy alternatives are ranked using Fermatean fuzzy WASPAS. The findings indicate that sustainable risk policies constitute the most important criterion for improving the performance of the ethical leadership response model. Furthermore, rewarding employees is identified as the most effective policy alternative for enhancing ethical leadership performance. The main contribution of this study lies in the development of a novel framework that integrates machine learning with fuzzy decision-making theory to provide a comprehensive evaluation of strategies for improving ethical leadership responses. The machine learning approach enables the computation of expert weights, while the use of Fermatean fuzzy sets helps account for uncertainty in the evaluation process. Consequently, the proposed framework facilitates the identification of more effective policy alternatives for leaders’ responses to employees.

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