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Artificial intelligence as a catalyst for ESG improvement: mechanisms and empirical evidence from manufacturing enterprises

Author

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  • Yang, Yefei
  • Zhang, Jiaxin
  • Zhang, Xu
  • Chen, Chenyi
  • Dong, Ciwei

Abstract

ESG (Environmental, Social, Governance) has emerged as a critical role for evaluating corporate long-term resilience. Meanwhile, artificial intelligence (AI) is reshaping the underlying logic of industrial operations. Thus, whether and how AI improves ESG performance is necessary to be explored. This study, grounded in the Schumpeter’s Innovation Theory, systematically examines the two main underlying influence mechanisms (i.e., green technology innovation and process improvement) and the heterogeneity of enterprise resource structure (i.e., labor, asset, and technology intensity) by conducting the longitudinal data from manufacturing firms. Empirical findings reveal that AI achieves greater ESG performance through such dual mechanisms. Additionally, AI can improve ESG performance in different enterprise resource structure; however, their underlying influence mechanism is different. These findings enrich the relevant literature and the empirical studies of Schumpeter’s Innovation Theory, and practically provide actionable insights for firms to design AI strategies to improve ESG performance.

Suggested Citation

  • Yang, Yefei & Zhang, Jiaxin & Zhang, Xu & Chen, Chenyi & Dong, Ciwei, 2026. "Artificial intelligence as a catalyst for ESG improvement: mechanisms and empirical evidence from manufacturing enterprises," Journal of Business Research, Elsevier, vol. 215(C).
    • Handle: RePEc:eee:jbrese:v:215:y:2026:i:c:s0148296326003371
    DOI: 10.1016/j.jbusres.2026.116302
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