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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
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    File URL: https://researchonline.lse.ac.uk/id/eprint/140342/
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    References listed on IDEAS

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    Keywords

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    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

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