IDEAS home Printed from https://ideas.repec.org/a/eee/eneeco/v150y2025ics0140988325006383.html
   My bibliography  Save this article

Can big data development drive green technology innovation? The spillover role of supply chain partners

Author

Listed:
  • You, Wanhai
  • You, Bingbing
  • Guo, Yawei

Abstract

The development of corporate big data has been widely recognized as a significant driver of internal green technology innovation. Moreover, supply chain networks, serving as essential channels for innovation resources, have substantial potential to facilitate green technology innovation. However, the potential spillover effects through supply chain networks remain underexplored. This study analyzes how supply chain partners' big data development affects enterprise green technology innovation. Using the data of Chinese A-share listed companies from 2012 to 2022, we employ the Word2vec model to measure supply chain partners' big data development. Our results reveal that supply chain partners' big data development significantly enhances focal firms' green technology innovation. Heterogeneity analysis demonstrates that the effect is particularly pronounced for large companies and those in low-marketization regions, with suppliers' big data development exhibiting a stronger impact than that of customers. Notably, technology integration capability weakens this relationship, whereas market competition strengthens it. These findings provide novel insights and valuable guidance for policymakers seeking to promote strategic green transformation, thereby supplementing existing literature.

Suggested Citation

  • You, Wanhai & You, Bingbing & Guo, Yawei, 2025. "Can big data development drive green technology innovation? The spillover role of supply chain partners," Energy Economics, Elsevier, vol. 150(C).
    • Handle: RePEc:eee:eneeco:v:150:y:2025:i:c:s0140988325006383
    DOI: 10.1016/j.eneco.2025.108811
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0140988325006383
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.eneco.2025.108811?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

    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • R15 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Econometric and Input-Output Models; Other Methods

    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:eneeco:v:150:y:2025:i:c:s0140988325006383. 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/eneco .

    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.