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Sustainable Development and Corporate Profitability: Data Mining Approach
Department of Management, Faculty of Social Sciences and Economics, Alzahra University, Tehran, Iran.
Department of Management, Faculty of Social Sciences and Economics, Alzahra University, Tehran, Iran.ORCID iD: 0000-0003-3623-1284
University of Gävle, Faculty of Education and Business Studies, Department of Business and Economic Studies, Business administration.ORCID iD: 0000-0002-2536-0446
Department of Management, Faculty of Social Sciences and Economics, Alzahra University, Tehran, Iran.
2025 (English)In: Information Systems Frontiers, ISSN 1387-3326, E-ISSN 1572-9419Article in journal (Refereed) Epub ahead of print
Abstract [en]

With the expansion of business activities around the world and the importance of sustainability in various fields, corporate sustainability has become a strategic imperative for management plans and investment decision. Therefore, this study focuses on examining the contribution of sustainability variables, i.e., economic, social, and environmental (ESG), to corporates profitability at 5936 companies distributed globally in an industry sectors using the data mining methods. The data extracted from Thomson Reuters database (ASSET4 ESG) for the period of 2002-2017 was used for modelling. Different algorithms, such as decision tree, support vector machine, and Na & iuml;ve Bayes, were used for modelling. Since the current study uses a multi-class classification, the Kappa criterion was used to assess the quality of the classification algorithm. The results of the study confirmed that none of the sustainability dimensions had a negative impact on corporate profitability.

Place, publisher, year, edition, pages
Springer , 2025.
Keywords [en]
Data mining, Machine learning, Artificial intelligence, Sustainability, Environmental, social, and governance (ESG) index, Corporate profitability
National Category
Economics and Business
Identifiers
URN: urn:nbn:se:hig:diva-46316DOI: 10.1007/s10796-024-10576-wISI: 001391740300001OAI: oai:DiVA.org:hig-46316DiVA, id: diva2:1928028
Available from: 2025-01-16 Created: 2025-01-16 Last updated: 2025-01-16Bibliographically approved

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Homayoun, Saeid

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