A grey-based hybrid decision support framework for assessing the Environmental, Social, and Governance (ESG) sustainable performance: a case study of BIST-listed banks - Işık, Özcan; Hashemkhani Zolfani, Sarfaraz; Shabir, Mohsin; Šaparauskas, Jonas (Technological and Economic Development of Economy, 2025)
The present research has been designed to address two significant gaps in the existing literature pertaining to the banking industry. Firstly, it presents a set of criteria derived from the Refinitiv database for the evaluation of ESG sustainability performance. Secondly, it puts forward a novel methodological framework that is both novel and noteworthy in the MCGDM field. This framework employs a grey-based multi-criteria group decision-making (MCGDM) technique with Bonferroni aggregation to comparatively analyze banks’ ESG sustainable performance. The developed methodology uses extended versions of three very recent methods, like the Modified Standard Deviation (MSD), Symmetry Point of Criterion (SPC), and Simple Ranking Process (SRP), based on the utilization of interval grey numbers. The Bonferroni aggregation operator is utilized for the aggregation of the experts’ evaluations concerning the alternatives based on the selected criteria. A real-life case study on seven publicly traded banks in the Borsa Istanbul Sustainability Index is conducted with the aid of five experts. The research results imply that among the three main ESG dimensions, environmental management practices emerged as the most important factor influencing banks’ sustainable performance. This finding also signals that banks that adopt sound environmental management practices into their business models may gain a competitive edge over their competitors in terms of environmental regulations, resilience to environmental risks, and achieving high performance and stability. Finally, the model’s validity is checked via comparison and sensitivity assessments. The outcomes of the two-stage validation analysis corroborate the robustness and dependability of the suggested grey MCGDM model.