IDEAS home Printed from https://ideas.repec.org/a/eee/techno/v152y2026ics0166497226000210.html

Can artificial intelligence orientation enhance supply chain trust? evidence of corporate trade credit

Author

Listed:
  • Guan, Yunfang
  • Tian, Hao
  • Tang, Zhen
  • Tian, Ming
  • Li, Xiaotong

Abstract

Despite extensive research on the internal benefits of artificial intelligence (AI) for organizations, only limited attention has been given to the question of how AI orientation influences external supply chain relationships, particularly with respect to the trust mechanisms associated with financial cooperation. This study addresses this gap by investigating whether and how firms' AI orientation affects suppliers' trade credit provision decisions (a tangible indicator of supply chain trust). Drawing on signaling theory and knowledge spillover theory, we examine how AI orientation serves as both a quality signal and a source of knowledge spillovers that enhance interorganizational trust within supply chain networks. Utilizing a comprehensive dataset of 4183 publicly listed Chinese firms from 2000 to 2022, we find that AI orientation positively influences trade credit provision, with supply chain transparency, knowledge breadth, and generalist CEO serving as positive moderators, while supply chain efficiency acts as a negative moderator. Our findings deepen the understanding of AI's external value creation mechanisms and provide new insights for supply chain relationship management.

Suggested Citation

  • Guan, Yunfang & Tian, Hao & Tang, Zhen & Tian, Ming & Li, Xiaotong, 2026. "Can artificial intelligence orientation enhance supply chain trust? evidence of corporate trade credit," Technovation, Elsevier, vol. 152(C).
    • Handle: RePEc:eee:techno:v:152:y:2026:i:c:s0166497226000210
    DOI: 10.1016/j.technovation.2026.103486
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0166497226000210
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.technovation.2026.103486?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:techno:v:152:y:2026:i:c:s0166497226000210. 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.