Gorkem SariyerSachin Kumar Kumar ManglaSoumyadeb ChowdhuryMert Erkan SozenYigit KazancogluErkan Sozen, MertKumar Mangla, SachinKazancoglu, YigitSariyer, GorkemSozen, Mert ErkanChowdhury, SoumyadebMangla, Sachin Kumar2025-10-062024014829630148-29631873-797810.1016/j.jbusres.2024.1147422-s2.0-85195045020https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195045020&doi=10.1016%2Fj.jbusres.2024.114742&partnerID=40&md5=aec394a6c94de9f0feed3c86a031a4adhttps://gcris.yasar.edu.tr/handle/123456789/8177https://doi.org/10.1016/j.jbusres.2024.114742Given 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. © 2024 Elsevier B.V. All rights reserved.Englishinfo:eu-repo/semantics/openAccessDecision-making, Deep Learning, Esg Performance, Predictive Analytics, Prescriptive Analytics, SustainabilityESG PerformanceDeep LearningPrescriptive AnalyticsDecision-makingSustainabilityPredictive AnalyticsArticle