IDEAS home Printed from https://ideas.repec.org/a/eee/finana/v99y2025ics1057521925000146.html
   My bibliography  Save this article

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

Author

Listed:
  • 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.

Suggested Citation

  • Cheng, Xuanmei & Du, Anna Min & Yan, Chengnuo & Goodell, John W., 2025. "Internal business process governance and external regulation: How does AI technology empower financial performance?," International Review of Financial Analysis, Elsevier, vol. 99(C).
  • Handle: RePEc:eee:finana:v:99:y:2025:i:c:s1057521925000146
    DOI: 10.1016/j.irfa.2025.103927
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1057521925000146
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.irfa.2025.103927?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:finana:v:99:y:2025:i:c:s1057521925000146. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/620166 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.