IDEAS home Printed from https://ideas.repec.org/h/spr/advbcp/978-94-6463-488-4_18.html

Digital Scenario Innovation Drives Enterprise Digital Transformation

In: Proceedings of the 2024 2nd International Conference on Digital Economy and Management Science (CDEMS 2024)

Author

Listed:
  • Shu Liu

    (CATARC Automotive Industry Engineering)

  • Fangqi Dong

    (CATARC Automotive Industry Engineering)

  • Boya Shang

    (CATARC Automotive Industry Engineering)

  • Mian Wang

    (CATARC Automotive Industry Engineering)

Abstract

As a value carrier and landing carrier for enterprise digital transformation, digital scenarios have become an important driving force for transformation, but their driving logic has not been clearly defined, which limits the role of digital scenarios. This article combines the basic framework of enterprise digital transformation to clarify the positioning of digital scenario innovation in the transformation process, and gives a general framework for scenario-driven enterprise digital transformation from the perspectives of scenario combination and decomposition, scenario construction and operation, clarifying its value path as two links and four carriers. It also describes the general logic of digital scenario-driven enterprise digital transformation at the enterprise level, industry level, and competition level.

Suggested Citation

  • Shu Liu & Fangqi Dong & Boya Shang & Mian Wang, 2024. "Digital Scenario Innovation Drives Enterprise Digital Transformation," Advances in Economics, Business and Management Research, in: Junfeng Liao & Hongbo Li & Edward H. K. Ng (ed.), Proceedings of the 2024 2nd International Conference on Digital Economy and Management Science (CDEMS 2024), pages 161-170, Springer.
    • Handle: RePEc:spr:advbcp:978-94-6463-488-4_18
    DOI: 10.2991/978-94-6463-488-4_18
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yingjie Xu & Bingchao Zheng & Baojie Guo, 2025. "How Do Industrial Robots Affect the Total Factor Productivity in Manufacturing Enterprises?," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 16(4), pages 14057-14093, October.

    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:advbcp:978-94-6463-488-4_18. 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.