The model offers practical guidance for decision-makers to maintain or enhance their ESG scores.
Authors
Gorkem Sariyer, Yasar University, Department of Business Administration, Izmir, Turkey.
Sachin Kumar Mangla, Professor, Jindal Global Business School, O.P. Jindal Global University, Sonipat, Haryana, India; Knowledge Management and Business Decision Making, Plymouth Business School, University of Plymouth, United Kingdom.
Soumyadeb Chowdhury, Information, Operations and Management Sciences Department, TBS Business School, 1 Place Alphonse Jourdain, Toulouse, France.
Mert Erkan Sozen, Izmir Metro Company, Head of Business Development and Budget Planning, Turkey.
Yigit Kazancoglu, Yasar University, Department of Logistics Management, Izmir, Turkey.
Summary
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.
Published in: Journal of Business Research
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