IDEAS home Printed from https://ideas.repec.org/p/ehl/lserod/140342.html

A study on the influencing factors of perceived artificial intelligence substitution risk

Author

Listed:
  • Xing, Xianghui
  • Dai, Hongwei
  • Zhou, Yiwei
  • Lan, Zhou
  • Wu, Xinyue
  • Jiang, Jie
  • Cao, Ling

Abstract

Artificial intelligence technologies are rapidly penetrating and reshaping the labor market. As a result, perceived AI substitution risk among workers has become an important issue affecting employment stability and social well-being. Using data from the Chinese Social Survey 2023, this study empirically examines the determinants of workers' perceived AI substitution risk and explores group heterogeneity. The results show that the perceived unemployment risk, number of children, and unemployment insurance are significantly positively associated with perceived AI substitution risk. In contrast, gender (Female = 0, Male = 1), age, perceived life difficulties, overall job satisfaction, job skill level, perceived socioeconomic status, and ethnicity (non-Han ethnicity = 0, Han ethnicity = 1) have a significant negative impact. The mediation analysis indicated that job satisfaction and perceived socioeconomic status reduced perceived AI substitution risk by lowering individuals' subjective evaluations of perceived unemployment risk. The heterogeneity analysis further shows that the perceived unemployment risk functions as a common core pressure source across groups. Its effect strength is constrained by the degree of technological penetration and occupational stability. Meanwhile, the influence of individual characteristics and resource endowments follows a threat appraisal-stress response pathway, with significant differences across geographical regions, urban-rural attributes, and work patterns. This study provides empirical evidence from China and offers a new analytical perspective for countries seeking to address employment anxiety triggered by technological substitution, optimize education and skills training systems, and maintain stability in the global labor market.

Suggested Citation

  • Xing, Xianghui & Dai, Hongwei & Zhou, Yiwei & Lan, Zhou & Wu, Xinyue & Jiang, Jie & Cao, Ling, 2026. "A study on the influencing factors of perceived artificial intelligence substitution risk," LSE Research Online Documents on Economics 140342, London School of Economics and Political Science, LSE Library.
  • Handle: RePEc:ehl:lserod:140342
    as

    Download full text from publisher

    File URL: https://researchonline.lse.ac.uk/id/eprint/140342/
    File Function: Open access version.
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    ;
    ;
    ;

    JEL classification:

    • R14 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Land Use Patterns
    • J01 - Labor and Demographic Economics - - General - - - Labor Economics: General

    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:ehl:lserod:140342. 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: LSERO Manager (email available below). General contact details of provider: https://edirc.repec.org/data/lsepsuk.html .

    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.