IDEAS home Printed from https://ideas.repec.org/a/mes/emfitr/v61y2025i14p4351-4365.html
   My bibliography  Save this article

Digital Transformation and News Sentiment

Author

Listed:
  • Jia Liu
  • Min Hua
  • Tianyi Wang
  • Lu-Jui Chen

Abstract

It is unclear whether adopting digital transformation in firms can affect media tone. This paper investigates the impacts of corporate digital transformation on news sentiment about the firms. We use Cloud Natural Language API to capture the sentiment of textual news content about the Chinese public listed firms. Our main finding indicates corporate digital transformation improves news sentiment about firms. We also find that digital transformation leads to better news sentiment by improving firms’ innovative and financial performance. In addition, our heterogeneity analysis suggests that the effects of DT on firms’ news sentiment are stronger for state-owned firms, firms with high institutional ownership and small-sized firms. Finally, we show that the effects of digital transformation are stronger for firms located in undeveloped areas and belong to high-tech industries. Our paper provides implications for firms and managers about the non-economic consequences when they make decisions.

Suggested Citation

  • Jia Liu & Min Hua & Tianyi Wang & Lu-Jui Chen, 2025. "Digital Transformation and News Sentiment," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 61(14), pages 4351-4365, November.
    • Handle: RePEc:mes:emfitr:v:61:y:2025:i:14:p:4351-4365
    DOI: 10.1080/1540496X.2025.2513346
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/1540496X.2025.2513346
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/1540496X.2025.2513346?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

    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:mes:emfitr:v:61:y:2025:i:14:p:4351-4365. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/MREE20 .

    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.