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

Xianghui Xing, Hongwei Dai, Yiwei Zhou, Zhou Lan, Xinyue Wu, Jie Jiang and Ling Cao

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

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.

Keywords: artificial intelligence substitution; perceived artificial intelligence substitution risk; determinants (search for similar items in EconPapers)
JEL-codes: J01 R14 (search for similar items in EconPapers)
Date: 2026-09
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Published in Acta Psychologica, September, 2026, 269, pp. 107441. ISSN: 0001-6918

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