IDEAS home Printed from https://ideas.repec.org/a/eee/riibaf/v82y2026ics0275531925004817.html

Artificial intelligence innovation and financial information quality: Evidence from firm patent data

Author

Listed:
  • Li, Zheng
  • Li, Haitong
  • Dai, Pengyi

Abstract

The rapid development of artificial intelligence (AI) is reshaping corporate practices, making its unintended implications for firms’ governance a topic worth exploring. This study examines the association between AI innovation and financial information quality from a governance perspective. Based on patent-level textual data, we construct an AI keyword dictionary using machine learning and measure firm-level AI innovation using text analysis method. Our empirical results indicate that AI innovation is positively associated with firm’s financial information quality. The mechanisms include improvements in internal control and increased external market attention. In cross-sectional analyses, our main finding is more pronounced in firms with poor corporate governance, high-tech certifications, and strong government digital initiatives. We further find the dual role of AI innovation in enhancing governance by reducing two types of agency costs. Our study provides new insights into the role of AI innovation in improving corporate governance and financial information quality.

Suggested Citation

  • Li, Zheng & Li, Haitong & Dai, Pengyi, 2026. "Artificial intelligence innovation and financial information quality: Evidence from firm patent data," Research in International Business and Finance, Elsevier, vol. 82(C).
    • Handle: RePEc:eee:riibaf:v:82:y:2026:i:c:s0275531925004817
    DOI: 10.1016/j.ribaf.2025.103225
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0275531925004817
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ribaf.2025.103225?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

    for a different version of it.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:riibaf:v:82:y:2026:i:c:s0275531925004817. 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/ribaf .

    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.