Vol. 1 No. 1 (2026)
Inaugural Issue

Articles

The Impact of Artificial Intelligence on Corporate ESG Performance: A Theoretical Model

Published 2026-01-27

  • Michelle R Anderson
    • ,
  • Robert L Perry

Abstract

This paper proposes a conceptual framework to analyze the relationship between Artificial Intelligence (AI) adoption and corporate Environmental, Social, and Governance (ESG) performance. As ESG criteria increasingly determine access to capital and market valuation, firms are turning to advanced digital technologies to meet these evolving standards. Drawing upon Stakeholder Theory and the Resource-Based View (RBV), this study theorizes that AI serves as a dynamic capability that enhances a firm’s ability to monitor environmental externalities, optimize social engagement, and ensure governance compliance. The proposed model suggests that AI adoption positively influences ESG performance through three mediating mechanisms: enhanced data transparency, predictive risk management, and operational efficiency. Furthermore, the framework posits that this relationship is moderated by organizational data maturity and regulatory pressure. Three testable propositions are developed to guide future empirical research. The paper concludes that AI is not merely a tool for productivity but a strategic asset for sustainable value creation.

Keywords

Artificial Intelligence, AI, ESG

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References

  1. 1. Acemoglu, D., & Restrepo, P. (2020). The wrong kind of AI? Artificial intelligence and the future of labor demand. Cambridge Journal of Economics, 44(1), 25-35.
  2. 2. Barney, J. B. (2018). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99-120. (Retrospective Analysis).
  3. 3. Cheng, M., & Wu, Y. (2023). Algorithmic greenwashing: The risks of AI in corporate sustainability reporting. Journal of Business Ethics, 182(2), 345-360.
  4. 4. Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108-116.
  5. 5. Eccles, R. G., & Klimenko, S. (2019). The investor revolution. Harvard Business Review, 97(3), 106-116.
  6. 6. Freeman, R. E., & Dmytriyev, S. (2017). Corporate social responsibility and stakeholder theory: Learning from each other. Symphonya. Emerging Issues in Management, (1), 7-15.
  7. 7. Gupta, A., & Sharma, D. (2024). AI-driven supply chain resilience and social responsibility: A dynamic capabilities perspective. International Journal of Operations & Production Management, 44(3), 112-135.
  8. 8. Henderson, R. (2021). Reimagining capitalism in a world on fire. Public Affairs.
  9. 9. Ivanov, D. (2022). Viable supply chain model: Integrating agility, resilience, and sustainability perspectives—lessons from and thinking beyond the COVID-19 pandemic. Annals of Operations Research, 319, 1411–1431.
  10. 10. Johnson, K. L., & Martinez, V. (2025). The predictive governance: Using machine learning for regulatory compliance. Academy of Management Perspectives, 39(1), 45-68.
  11. 11. Porter, M. E., & Heppelmann, J. E. (2014). How smart, connected products are transforming competition. Harvard Business Review, 92(11), 64-88.
  12. 12. Zhang, T., & Wang, L. (2022). Digital sustainability: The role of AI in achieving the UN Sustainable Development Goals. Technological Forecasting and Social Change, 176, 121445.
  13. 13. Al-Fayed, S., & Jensen, M. (2023). The algorithmic bureaucrat: Accountability in automated welfare systems. Journal of Public Administration Research and Theory, 33(2), 215–230. https://doi.org/10.1093/jopart/muac022
  14. 14. Bennett, L. K., & Roberts, D. (2021). Beyond access: The second-level digital divide in e-government adoption. Government Information Quarterly, 38(4), 101614. https://doi.org/10.1016/j.giq.2021.101614
  15. 15. Chen, Y., & Li, Q. (2024). Smart cities and the privacy paradox: A comparative analysis of citizen trust in Asia and Europe. Urban Governance, 5(1), 45–58.
  16. 16. Davila, R., & Gomez, P. (2020). From silos to platforms: Interoperability challenges in Latin American public sectors. Public Management Review, 22(8), 1150–1172.
  17. 17. Ekwueme, O., & Smith, J. (2022). Ransomware and the resilient city: Case studies in municipal cyber-defense. Cybersecurity & Policy, 18(3), 302–319.
  18. 18. Harding, T. (2019). The 'Once-Only' principle: Efficiency versus privacy in European digital governance. European Journal of Information Systems, 28(6), 612–629.
  19. 19. Kovacs, A., & Muller, H. (2025). Digital twins in urban planning: Simulation, prediction, and democratic oversight. Computers, Environment and Urban Systems, 102, 103456.
  20. 20. Lee, S.-H., & Park, M. J. (2018). Mobile governance: The transition from desktop to smartphone-based service delivery in South Korea. International Review of Administrative Sciences, 84(1), 185–205.
  21. 21. Mbembe, A., & O’Connor, F. (2024). AI in the Global South: Post-colonial perspectives on digital governance. Development Informatics, 12(2), 88–104.
  22. 22. Nielsen, B. B., & Varga, S. (2022). Function creep: The long-term implications of COVID-19 contact tracing data retention. Ethics and Information Technology, 24(1), 12–25.
  23. 23. Patel, R., & Zhang, W. (2023). Blockchain for public records: A immutable truth or a technical illusion? Journal of Digital Governance, 9(3), 145–162.
  24. 24. Rodriguez, M. (2021). The user experience of the state: Applying design thinking to federal agency websites. Public Admin-istration, 99(4), 880–895.
  25. 25. Tanaka, H. (2025). The automated state: Predictive policing and the erosion of due process. Law and Technology Review, 19(1), 55–80.
  26. 26. Voronova, E., & Sokolov, D. (2019). Cybersecurity readiness in the public sector: A five-year longitudinal study. Computers & Security, 88, 101602.
  27. 27. Williams, C., & Davis, L. (2020). Digital by default, excluded by design: The impact of e-government mandates on rural populations. Social Policy & Administration, 54(5), 789–805.