IDEAS home Printed from https://ideas.repec.org/h/spr/advbcp/978-94-6463-636-9_24.html

The Impact of Digital Transformation on Enterprise Human Resource Management Models and Strategic Responses

In: Proceedings of the 2024 International Conference on Digital Economy and Marxist Economics (ICDEME 2024)

Author

Listed:
  • Xiaoling Nong

    (Baise University, School of Business Administration)

  • Shaolai Li

    (Baise University, School of Marxism)

Abstract

Having explored the profound impact of digital transformation on enterprise human resource management models, this study proposed the challenges and strategic responses faced by enterprise human resource management in the context of digital transformation, and conducted a questionnaire survey on the human resource management situation of various enterprises under the backdrop of digital transformation. The survey results were analyzed using SPSS24.0 and revealed the following: the majority of enterprises have seen their human resource planning, recruitment and selection, training and development, and performance management affected by digital transformation, digital technologies, and platform tools, Over half of the enterprises agree that it is necessary to customize compensation and benefits under the backdrop of digital transformation; Most enterprises face multiple challenges in human resource management, such as data security and privacy protection, differences in digital literacy among employees, rapid technological updates, and the integration of corporate culture with digital transformation; At least nearly half of the enterprises recognize strategies for response, including enhancing digital literacy, optimizing processes, utilizing big data analysis, and constructing a digital human resource platform.

Suggested Citation

  • Xiaoling Nong & Shaolai Li, 2024. "The Impact of Digital Transformation on Enterprise Human Resource Management Models and Strategic Responses," Advances in Economics, Business and Management Research, in: Yongjun Guan & Yan Duan & Tao Wang & Chuan Liang (ed.), Proceedings of the 2024 International Conference on Digital Economy and Marxist Economics (ICDEME 2024), pages 267-280, Springer.
    • Handle: RePEc:spr:advbcp:978-94-6463-636-9_24
    DOI: 10.2991/978-94-6463-636-9_24
    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.

    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:978-94-6463-636-9_24. 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.