Enterprise Digitalization and ESG Performance: Evidence from Interpretable AI Large Language Models
In recent years, enterprise digitalization has garnered significant attention in business practices, yet its impact on environmental, social, and governance (ESG) performance remains underexplored. This study focuses on two core questions: (1) How can enterprise digitalization be measured more effectively? (2) How does digitalization affect the three dimensions of ESG, and through what mechanisms? Based on annual reports of A-share-listed companies in Shanghai and Shenzhen from 2013 to 2022, we construct an innovative measurement of digitalization using an interpretable AI large language model. Panel regression and mechanism analysis reveal that digitalization significantly enhances ESG performance, primarily through three mechanisms: improving energy efficiency, increasing social concern, and strengthening internal controls. Heterogeneity analysis shows that this effect is more pronounced among large and state-owned enterprises. This study proposes a replicable and transferable approach to measuring digitalization, offering empirical evidence and practical insights for firms and policymakers.
Authors
- Ning Chen (ORCID: https://orcid.org/0000-0002-3153-8765)
- Rui Tang (ORCID: https://orcid.org/0000-0002-4890-459X)
- Hangzhang Kou (ORCID: https://orcid.org/0009-0009-7357-4025)
Institutions
- Sun Yat-sen University (CN)
- Renmin University of China (CN)
Publication Details
- Journal
- Systems
- Published
- 2025-09-22
- DOI
- https://doi.org/10.3390/systems13090832
- Citations
- 2
- Primary Topic
- Impact of AI and Big Data on Business and Society
- Type
- article
- Field-Weighted Citation Impact
- 4.21