IDEAS home Printed from https://ideas.repec.org/a/eur/ejserj/476.html

Navigating Digital Transformation: Sociological and Educational Factors in Corporate AI Strategy Adoption

Author

Listed:
  • Shijie Chen

    (Seoul School of Integrated Sciences & Technologies, South Korea)

  • Haoer Chen

    (Xi'an University of Finance and Economics, China)

Abstract

To address the practical challenge of the disconnect between AI strategy implementation and performance outcomes in enterprises, this study focuses on the three-dimensional execution mechanism encompassing "resourcesprocess-culture," examining its mediating role between AI strategies and corporate performance, as well as the moderating effects of firm size and industry characteristics. Data were collected via random sampling using the Wenjuanxing online survey platform, supplemented by a 12-month longitudinal case tracking study. Empirical analysis employed structural equation modeling, grouped regression, and interaction term testing. Results demonstrate that the three-dimensional execution mechanism fully mediates the relationship between AI strategies and corporate performance, with resource allocation playing the most significant mediating role. Firm size positively moderates the performance effect of the execution mechanism, with large firms exhibiting significantly better implementation outcomes than small and medium-sized enterprises. Industry characteristics exhibit heterogeneity: technology-intensive industries show stronger positive impacts of the execution mechanism compared to non-technology-intensive sectors. Differentiation in implementation pathways reveals adaptability— technology-intensive firms benefit from a "resource priority or lightweight resources plus cultural guidance" approach, while non-technology-intensive firms are better suited for a "culture priority plus lightweight processes" model. This study evaluates AI strategy implementation primarily through organizational financial and innovation performance indicators. Although these metrics serve as organizational-level proxies for implementation effectiveness, they do not directly measure employee-level social outcomes, such as workplace learning, well-being, psychological safety, re-skilling, or perceived equity; therefore, the social implications discussed in this study should be interpreted within this scope.

Suggested Citation

  • Shijie Chen & Haoer Chen, 2026. "Navigating Digital Transformation: Sociological and Educational Factors in Corporate AI Strategy Adoption," European Journal of Social Sciences Education and Research Articles, Revistia Research and Publishing, vol. 13(3), pages 16-39, September.
  • Handle: RePEc:eur:ejserj:476
    DOI: 10.26417/mck5nml6
    as

    Download full text from publisher

    File URL: https://revistia.com/files/articles/ejser_v13_i3_26/Chen.pdf
    Download Restriction: no

    File URL: https://libkey.io/10.26417/mck5nml6?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
    ---><---

    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:eur:ejserj:476. 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: Revistia Research and Publishing (email available below). General contact details of provider: https://revistia.com/index.php/ejser .

    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.