IDEAS home Printed from https://ideas.repec.org/a/eee/techno/v145y2025ics0166497225001038.html
   My bibliography  Save this article

The performance implications of R&D collaborations on artificial intelligence

Author

Listed:
  • Ming, Xin
  • Wang, Qiang
  • Liu, Yan

Abstract

With the increasing prevalence of cooperative research and development (R&D) in artificial intelligence (AI) among companies, the performance implications of these collaborations remain underexplored. This study examines the financial impact of AI-related R&D collaborations, focusing on how AI type, partnerships with top-tier technology companies, and digitalization capabilities moderate their effects on firm value. Analyzing announcements from Chinese firms between 2016 and 2023, we find a positive association between AI-related R&D collaborations and firm value, with this effect being particularly strong for collaborations on language/text understanding and with top-tier technology partners. However, this positive effect diminishes as firms’ digitalization capabilities increase. This research provides empirical evidence of the benefits of AI-related R&D collaborations and offers insights into how firms can maximize the value of such partnerships.

Suggested Citation

  • Ming, Xin & Wang, Qiang & Liu, Yan, 2025. "The performance implications of R&D collaborations on artificial intelligence," Technovation, Elsevier, vol. 145(C).
    • Handle: RePEc:eee:techno:v:145:y:2025:i:c:s0166497225001038
    DOI: 10.1016/j.technovation.2025.103271
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0166497225001038
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.technovation.2025.103271?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:techno:v:145:y:2025:i:c:s0166497225001038. 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.sciencedirect.com/science/journal/01664972 .

    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.