Deprivation of Sense of Control Under Algorithmic Management and Gig Worker Burnout: A Mediating Model Based on Autonomy Need Frustration

The expansion of the platform economy has established algorithmic management as a primary governance mechanism in gig work. Although this mode enhances operational efficiency, it may also be associated with increased worker burnout. Existing research has not sufficiently examined the differentiated mechanisms through which distinct dimensions of algorithmic management operate. Based on self-determination theory and the job demands-resources model, this study constructs a dual-path mediating model with sense of control deprivation and autonomy need frustration as mediators. A questionnaire survey was conducted to collect data from 342 gig workers, and empirical tests were performed using partial least squares structural equation modeling (PLS-SEM). The PLS-SEM results revealed that the direct paths from tracking evaluation and behavioral constraints to job burnout were negative in direction (TE → WK: β = −0.141, p = 0.018; BC → WK: β = −0.115, p = 0.052), contrary to our hypotheses. Sense of control deprivation showed statistically significant indirect effects in both paths, though in directions opposite to those hypothesized. Autonomy need frustration showed a significant indirect effect only for the relationship between tracking evaluation and job burnout, also in the opposite direction. The model explained only 9.7% of the variance in job burnout, suggesting that other unmeasured factors play substantial roles. This study provides preliminary and counterintuitive findings. However, given the unexpected direction of the mediation effects and the model’s low explanatory power (R2 = 0.097), these results are exploratory and require replication.