Predictive and prescriptive analytics for ESG performance evaluation: A case of Fortune 500 companies

Sariyer, Gorkem, Mangla, Sachin Kumar, Chowdhury, Soumyadeb, Erkan Sozen, Mert and Kazancoglu, Yigit (2024) Predictive and prescriptive analytics for ESG performance evaluation: A case of Fortune 500 companies. Journal of Business Research, 181. ISSN 0148-2963 (In Press)

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Given the growing importance of organizations’ environmental, social, and governance (ESG) performance, studies employing AI-based techniques to generate insights from ESG data for investors and managers are limited. To bridge this gap, this study proposes an AI-based multi-stage ESG performance prediction system consolidating clustering for identifying patterns within ESG data, association rule mining for uncovering meaningful relationships, deep learning for predictive accuracy, and prescriptive analytics for actionable insights. This study is grounded in the big data analytics capability view that has emerged from the dynamic capabilities theory. The model is validated using an ESG dataset of 470 Fortune listed 500 companies obtained from the Refinitiv database. The model offers practical guidance for decision-makers to maintain or enhance their ESG scores, crucial in a business landscape where ESG metrics significantly affect investor choices and public image.

Item Type: Article
Keywords: Environmental, Social, and Governance (ESG) performance | AI-based techniques | ESG data | 470 Fortune listed 500 companies
Subjects: Social Sciences and humanities > Business, Management and Accounting > Marketing
JGU School/Centre: Jindal Global Business School
Depositing User: Subhajit Bhattacharjee
Date Deposited: 10 Jun 2024 05:52
Last Modified: 10 Jun 2024 05:52
Official URL:


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