IDEAS home Printed from https://ideas.repec.org/a/oup/rfinst/v39y2026i1p253-296..html

Dissecting Corporate Culture Using Generative AI

Author

Listed:
  • Kai Li
  • Feng Mai
  • Rui Shen
  • Chelsea Yang
  • Tengfei Zhang

Abstract

We conduct the first large-scale study of how different stakeholder groups assess corporate culture and quantify the economic implications of those differences. We employ generative AI to analyze analyst reports, call transcripts, and employee reviews, and organize the

Suggested Citation

  • Kai Li & Feng Mai & Rui Shen & Chelsea Yang & Tengfei Zhang, 2026. "Dissecting Corporate Culture Using Generative AI," The Review of Financial Studies, Society for Financial Studies, vol. 39(1), pages 253-296.
    • Handle: RePEc:oup:rfinst:v:39:y:2026:i:1:p:253-296.
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1093/rfs/hhaf081
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    More about this item

    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:oup:rfinst:v:39:y:2026:i:1:p:253-296.. 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: Oxford University Press (email available below). General contact details of provider: https://edirc.repec.org/data/sfsssea.html .

    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.