IDEAS home Printed from https://ideas.repec.org/a/spr/jknowl/v16y2025i3d10.1007_s13132-024-02259-3.html
   My bibliography  Save this article

Unraveling the Mystery of Sustainable-Oriented Innovation: The Role of Big Data Knowledge Management, Resource Orchestration Capacity, and Competitive Strategy

Author

Listed:
  • Rui Zhao

    (Liaoning Technical University)

  • Lixia Niu

    (Liaoning Technical University)

Abstract

In this study, we propose big data knowledge management as an important enabler of sustainable-oriented innovation and investigate how and when it enhances sustainable-oriented innovation. We examined this by considering resource orchestration capacity as a mediator and competitive strategy as an important contingency variable. Based on the resource orchestration theory, using 308 survey data collected from innovative firms in China, through Mplus and SPSS software, the results indicate that resource orchestration capacity mediates the effect of big data knowledge management on sustainable-oriented innovation. The contingency analyses also revealed that the positive impact of resource orchestration capacity on sustainable-oriented innovation is stronger at high levels of differentiation strategy. However, the relationship between resource orchestration capacity and sustainable-oriented innovation is weaker when the low-cost strategy is high. The implications of the findings are discussed.

Suggested Citation

  • Rui Zhao & Lixia Niu, 2025. "Unraveling the Mystery of Sustainable-Oriented Innovation: The Role of Big Data Knowledge Management, Resource Orchestration Capacity, and Competitive Strategy," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 16(3), pages 11117-11137, September.
  • Handle: RePEc:spr:jknowl:v:16:y:2025:i:3:d:10.1007_s13132-024-02259-3
    DOI: 10.1007/s13132-024-02259-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13132-024-02259-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s13132-024-02259-3?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:spr:jknowl:v:16:y:2025:i:3:d:10.1007_s13132-024-02259-3. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.