A multi-criteria sustainability performance assessment based on the extended CRADIS method under intuitionistic fuzzy environment: a case study of Turkish non-life insurers

A multi-criteria sustainability performance assessment based on the extended CRADIS method under intuitionistic fuzzy environment: a case study of Turkish non-life insurers

연구 분야: Verification

학회: Neural Computing and Applications

ESG (environmental, social and governance) indicators play a strategically vital role in improving the sustainability performance of companies. A truly sustainable company could succeed and survive by taking into account the environmental, social, and governance aspects of sustainability in line with the goals and values of society within the framework of corporate social responsibility. However, no past study has explored all aspects of sustainability, particularly in the context of evaluating insurance companies in emerging economies. Due to climate change and population growth, the need for products and services provided by insurance companies is increasing day by day. To address this issue, the current study puts forward an intuitionistic fuzzy (IF) decision support framework for assessing the sustainability performance of insurance companies based on ESG factors. More specifically, the present paper develops a novel fuzzy multi-criteria decision-making (MCDM) methodology to rank and assess the ESG performance of non-life insurance companies in Turkey through the combination of the compromise ranking of alternatives from distance to ideal solution (CRADIS) framework and IF sets, which results in a new approach called intuitionistic fuzzy CRADIS (IF-CRADIS). Concerning the results, human right, workforce, and environmental innovations are the vital drivers for the ESG performance of insurance companies. Additionally, the results of IF-CRADIS indicate that Türkiye Sigorta is the top-performing insurer, whereas Eureko Sigorta is the one with the lowest-performance. Finally, sensitivity analysis demonstrates that the introduced model has a maximally consistent and stable MCDM framework.