Artificial Intelligence and ESG Disclosure Quality: A Boundary Conditional Analysis Using Quantile Regression

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🔬研究者:Provides empirical evidence on AI-ESG disclosure nexus using quantile regression, contributing to technology-ESG literature.

🏢実務担当者:Highlights the need to align AI investments with stakeholder engagement and regulatory compliance to improve ESG disclosure quality.

🏛政策担当者:Suggests promoting AI-enabled transparent ESG reporting frameworks to support responsible corporate behavior.

ABSTRACT The rapid diffusion of artificial intelligence (AI) is reshaping corporate reporting and sustainability practices, yet empirical evidence on how AI adoption improves environmental, social, and governance disclosure quality remains limited. This study investigates the effect of artificial intelligence adoption (AIA) on ESG disclosure quality (ESGQ) among manufacturing firms in G7 economies and examines how organizational and institutional conditions shape this relationship. By focusing on advanced economies with strong regulatory frameworks and high digital readiness, the study provides a suitable setting to assess technology‐driven sustainability outcomes. Using a panel dataset of 5600 firm‐year observations from 2017 to 2024, the analysis employs distribution‐sensitive estimation techniques and complementary robustness tests to capture heterogeneity and mitigate endogeneity concerns. The findings reveal a consistently positive relationship between AIA and ESGQ across firms with varying levels of disclosure quality, indicating that AI enhances the accuracy, timeliness, and credibility of sustainability reporting. Moreover, stakeholder engagement, regulatory pressure, and institutional ownership significantly strengthen this relationship, highlighting the importance of governance structures and institutional pressures in translating technological capability into substantive disclosure improvements. The study contributes theoretically by integrating stakeholder theory, the resource‐based view, and institutional theory to explain how AI functions as a strategic capability whose sustainability impact is contingent on governance and institutional contexts. Practically, the results underscore the need for managers to align AI investments with stakeholder engagement and regulatory compliance, while policymakers and regulators are encouraged to promote AI‐enabled, transparent ESG reporting frameworks that support responsible and sustainable corporate behavior in the digital era.

• crossref https://doi.org/10.1002/bse.71425first seen 2026-08-17 05:56:45 · last seen 2026-08-18 05:25:25