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Generative AI, ESG Sensemaking, and Environmental Performance: an OIPT Perspective

Author

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  • Surajit Bag
  • Gautam Srivastava
  • Susmi Routray
  • Andrea Chiarini

Abstract

Despite growing enthusiasm for generative artificial intelligence (GenAI) in sustainability management, it remains unclear how such technologies translate vast ESG information into meaningful environmental outcomes. This study addresses this gap by investigating how ESG sensemaking capability mediates the relationship between GenAI integration and environmental performance, analyzing how sustainability information overload moderates the relationship between technological adoption and ESG sensemaking, and exploring the influence of regulatory uncertainty on the link between ESG sensemaking and environmental performance. Drawing upon organizational information processing theory (OIPT), the study develops and tests a conceptual framework using data collected from 610 firms. The results indicate that GenAI integration enhances environmental performance both directly and indirectly through improved ESG sensemaking. However, when sustainability‐related information becomes excessive, this positive effect weakens. In contrast, regulatory uncertainty amplifies the beneficial relationship between ESG sensemaking and environmental outcomes. These findings highlight that technology adoption alone does not guarantee sustainability gains; organizational interpretive capacity is important. This study extends OIPT by introducing ESG sensemaking capability as a distinct interpretive mechanism that bridges information‐processing fit and sustainability outcomes, distinguishing it from absorptive and dynamic capabilities. In addition to empirical evidence, we validate our findings through triangulation with real‐world use cases.

Suggested Citation

  • Surajit Bag & Gautam Srivastava & Susmi Routray & Andrea Chiarini, 2026. "Generative AI, ESG Sensemaking, and Environmental Performance: an OIPT Perspective," Business Strategy and the Environment, Wiley Blackwell, vol. 35(5), pages 7196-7217, July.
    • Handle: RePEc:bla:bstrat:v:35:y:2026:i:5:p:7196-7217
    DOI: 10.1002/bse.70520
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