Paper Type
Complete
Abstract
This study examines how artificial intelligence (AI)–enabled analytics enhances Environmental, Social, and Governance (ESG) reporting in resource-constrained museums. Drawing on the resource-based view and dynamic capabilities theory, we conceptualize AI-enabled ESG reporting as a strategic organizational capability. Using a mixed-method design, we combine qualitative case studies of two East Asian museums with quantitative analysis of 50 U.S. museums using Candid transparency ratings as a proxy for ESG disclosure. Results show that greater reliance on donor contributions increases incentives for transparency, particularly in competitive urban environments. However, fragmented data, weak governance, and organizational inertia limit ESG effectiveness. To address these challenges, we propose an AI-enabled ESG reporting architecture integrating diverse data sources, real-time analytics, and explainable AI. The findings demonstrate how AI can transform ESG reporting into a decision-support capability that improves transparency, operational efficiency, and funding sustainability.
Paper Number
1791
Recommended Citation
Grimmer, Teri; Kim, Wookyoung; Jung, Harim; and Eom, Mike Taein, "Building ESG Reporting Architecture & Capabilities through Artificial Intelligence (AI): Dynamic Capabilities Perspective-enabled ESG Reporting in Museums" (2026). AMCIS 2026 Proceedings. 4.
https://aisel.aisnet.org/amcis2026/sig_lead/sig_lead/4
Building ESG Reporting Architecture & Capabilities through Artificial Intelligence (AI): Dynamic Capabilities Perspective-enabled ESG Reporting in Museums
This study examines how artificial intelligence (AI)–enabled analytics enhances Environmental, Social, and Governance (ESG) reporting in resource-constrained museums. Drawing on the resource-based view and dynamic capabilities theory, we conceptualize AI-enabled ESG reporting as a strategic organizational capability. Using a mixed-method design, we combine qualitative case studies of two East Asian museums with quantitative analysis of 50 U.S. museums using Candid transparency ratings as a proxy for ESG disclosure. Results show that greater reliance on donor contributions increases incentives for transparency, particularly in competitive urban environments. However, fragmented data, weak governance, and organizational inertia limit ESG effectiveness. To address these challenges, we propose an AI-enabled ESG reporting architecture integrating diverse data sources, real-time analytics, and explainable AI. The findings demonstrate how AI can transform ESG reporting into a decision-support capability that improves transparency, operational efficiency, and funding sustainability.
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