The impact of perceived algorithmic management on employees' physical and mental health: a chain mediation analysis of frustrated work autonomy and workplace AI replacement anxiety - Zheng Ma; Xingzi Chen; Binhua Jing (Frontiers in Public Health, 2026)
With the acceleration of digital transformation, perceived algorithmic management has gradually evolved into a widespread new type of occupational psychosocial hazard. From the perspective of occupational health, this study explores the negative association between perceived algorithmic management on employees' physical and mental health and systematically examines the chain mediating role of frustrated work autonomy and workplace AI replacement anxiety. Using a questionnaire survey method, this study collected data from 338 employees in multiple industries in China who were directly affected by perceived algorithmic management, SPSS 27.0 was used for preliminary statistical analyses (descriptive statistics, reliability testing, and correlation analysis), while AMOS 28.0 was used to construct and test the structural equation model (SEM) for confirmatory factor analysis and path estimation. Bootstrap resampling with 5,000 iterations was applied to assess indirect effects. The results show that perceived algorithmic management is significantly negatively associated with employees' physical and mental health; frustrated work autonomy and workplace AI replacement anxiety not only each play an independent mediating role in this relationship, but also constitute a significant chain mediating pathway. This study suggests a potential mechanism through which perceived algorithmic management in the digital era may be associated with occupational health risks, providing important empirical evidence for governments to formulate public health intervention policies, for enterprises to optimize management systems, and for the implementation of workplace mental health protection.