Internal business process governance and external regulation: How does AI technology empower financial performance?

Cheng, Xuanmei; Du, Anna Min; Yan, Chengnuo; Goodell, John W.

Abstract

We examine the impact of artificial intelligence (AI) technology on the financial performance of financial firms, focusing on the transmission role of business process governance from the perspective of corporate governance. We examine whether there is heterogeneity in the effects of different institutional environments, conducting an empirical analysis of the sample data of 152 Chinese financial firms from 2011 to 2022. AI technology improves financial enterprises' operational quality through risk management, internal governance, and internal control. Moreover, AI provides technical support to enhance service derivative capability by enhancing the number of businesses across cross-regional operations, resource competition, and customer excavation. We also identify that the enabling effect of digital technology on business process governance is affected by imitation pressure from peer firms. In contrast, the impact of government regulatory pressure is not significant.

Citation

Cheng, X., Du, A. M., Yan, C., & Goodell, J. W. (2025). Internal business process governance and external regulation: How does AI technology empower financial performance?. International Review of Financial Analysis, 99, Article 103927. https://doi.org/10.1016/j.irfa.2025.103927

• Journal Article Type: Article
• Acceptance Date: Jan 11, 2025
• Online Publication Date: Jan 13, 2025
• Publication Date: 2025-03
• Deposit Date: Feb 18, 2025
• Journal: International Review of Financial Analysis
• Print ISSN: 1057-5219
• Electronic ISSN: 1873-8079
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 99
• Article Number: 103927
• DOI: https://doi.org/10.1016/j.irfa.2025.103927
• Keywords: AI technology, Corporate governance, Financial performance, Financial enterprises, Regulatory pressure, Imitation pressure
• Public URL: http://researchrepository.napier.ac.uk/Output/4122270