IDEAS home Printed from https://ideas.repec.org/h/spr/advbcp/978-94-6239-672-2_24.html

Exploring Job Happiness Across Asian Countries: Factors, Challenges, and Strategies for Enhancing Employee Well-Being and Organizational Performance

Author

Listed:
  • Shifeng Chen

    (The University of Hong Kong)

Abstract

With the generalized usage of artificial intelligence (AI) in the workplace, the influence on the efficiency, Human–AI trust, and task suitability of knowledge workers has become increasingly profound. Through literature analysis and theoretical exploration, this study develops an integrated model of AI tools and knowledge worker performance, examining how AI enhances the performance of knowledge workers. The findings indicate that AI tools can enhance efficiency in tasks of low complexity and AI tools in high complexity tasks tend to function as assistive tools. The establishment of Human–AI trust depends on the interpretability, transparency and feedback mechanisms of AI systems, which promote greater willingness of using AI tools. Excessive reliance on AI may weaken the capacity of knowledge workers for independent thinking and innovation. By dynamically allocating decision authority and establishing Human–AI trust, organizations can promote collaborative development between humans and AI and improve the performance of the employees.

Suggested Citation

  • Shifeng Chen, 2026. "Exploring Job Happiness Across Asian Countries: Factors, Challenges, and Strategies for Enhancing Employee Well-Being and Organizational Performance," Advances in Economics, Business and Management Research,, Springer.
    • Handle: RePEc:spr:advbcp:978-94-6239-672-2_24
    DOI: 10.2991/978-94-6239-672-2_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:978-94-6239-672-2_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.