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.
Ma Z, Chen X, Jing B • Frontiers in public health • 2026
Perceived algorithmic management is significantly negatively associated with employees' physical and mental health, with frustrated work autonomy and workplace AI replacement anxiety constituting a significant chain mediating pathway in this relationship.
Key Findings
Results
Perceived algorithmic management was significantly negatively associated with employees' physical and mental health.
Data were collected from 338 employees across multiple industries in China who were directly affected by perceived algorithmic management.
Structural equation modeling (SEM) was used to estimate path coefficients via AMOS 28.0.
The relationship was characterized as a 'significant negative association' between perceived algorithmic management and occupational health outcomes.
Bootstrap resampling with 5,000 iterations was applied to assess indirect effects and confirm the robustness of findings.
Results
Frustrated work autonomy independently mediated the relationship between perceived algorithmic management and employees' physical and mental health.
Frustrated work autonomy functioned as a standalone mediator between perceived algorithmic management and health outcomes.
The mediation effect was tested using Bootstrap resampling with 5,000 iterations.
Perceived algorithmic management was proposed to reduce employees' sense of control and autonomy over their work, contributing to health impairment.
This pathway was identified as one of two independent mediating mechanisms in the model.
Results
Workplace AI replacement anxiety independently mediated the relationship between perceived algorithmic management and employees' physical and mental health.
Workplace AI replacement anxiety functioned as a standalone mediator between perceived algorithmic management and health outcomes.
The mediation effect was confirmed using Bootstrap resampling with 5,000 iterations.
This pathway was identified as a second independent mediating mechanism alongside frustrated work autonomy.
The study frames workplace AI replacement anxiety as an occupational psychosocial hazard associated with digital transformation.
Results
Frustrated work autonomy and workplace AI replacement anxiety together constituted a significant chain mediating pathway between perceived algorithmic management and employee health.
The chain mediation pathway operated sequentially: perceived algorithmic management → frustrated work autonomy → workplace AI replacement anxiety → physical and mental health.
Both independent and chain mediation effects were confirmed using SEM and Bootstrap resampling with 5,000 iterations.
The chain mediation model suggests that algorithmic management first frustrates autonomy, which in turn amplifies anxiety about AI job replacement, ultimately harming health.
Confirmatory factor analysis (CFA) was conducted in AMOS 28.0 to validate the measurement model prior to path estimation.
Background
Perceived algorithmic management was characterized as a widespread new type of occupational psychosocial hazard in the context of digital transformation.
The study adopted an occupational health perspective to frame algorithmic management as a psychosocial risk factor.
Participants were 338 employees from multiple industries in China directly affected by algorithmic management.
Preliminary statistical analyses included descriptive statistics, reliability testing, and correlation analysis using SPSS 27.0.
The study provides empirical evidence intended to inform public health intervention policies and workplace mental health protection.
What This Means
This research suggests that when employees perceive their workplace as being managed by algorithms — systems that automatically monitor, evaluate, or direct their work through digital tools — their physical and mental health tends to suffer. The study surveyed 338 workers across various industries in China and used statistical modeling to trace the pathways through which algorithmic management may harm health. Two psychological mechanisms were identified: first, algorithmic management appears to frustrate employees' sense of autonomy and control over their own work; second, it appears to increase anxiety about being replaced by artificial intelligence. Each of these factors was linked to worse health outcomes on its own, and they also worked together in sequence — reduced autonomy feeding into greater AI replacement anxiety, which then further harmed health.
This research suggests that the rise of digital management tools in modern workplaces is not just a technological or productivity issue, but potentially a public health concern. Workers who feel watched, controlled, and potentially replaceable by automated systems may experience compounding psychological stress that takes a toll on their overall wellbeing. The findings point to frustrated autonomy and job insecurity related to AI as key pressure points that organizations and policymakers might address.
The study's implications extend to both employers and governments. This research suggests that companies using algorithmic management systems should consider how such tools affect employees' sense of agency and job security, and that policymakers may need to develop workplace health protections that account for the psychosocial risks introduced by digital transformation. The study was conducted in China with a relatively modest sample size, which may limit how broadly the findings can be generalized to other cultural or economic contexts.
Ma Z, Chen X, Jing B. (2026). 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.. Frontiers in public health. https://doi.org/10.3389/fpubh.2026.1877221