The Impact of Perceived Algorithmic Control on Gig Workers’ Turnover Intention: Mediating Roles of Perceived Usefulness and Psychological Contract Breach

In recent years, digital technologies have been widely applied in the gig economy, and platform algorithms have become the core tool for managing gig workers. This study collected data through a multi-phase questionnaire survey and conducted empirical regression tests and fuzzy-set Qualitative Comparative Analysis (fsQCA). It explored how perceived algorithmic control affected gig workers' turnover intention through psychological contract breach and perceived usefulness, analyzed the moderating role of time pressure, and investigated its relevant configurational pathways. The results indicated that perceived algorithmic control negatively affected gig workers' turnover intention. Psychological contract breach and perceived usefulness both partially mediated the relationship between perceived algorithmic control and turnover intention. Time pressure negatively moderated the relationship between perceived algorithmic control and perceived usefulness, thereby weakening the indirect effect of perceived algorithmic control on turnover intention via perceived usefulness; however, it did not significantly moderate the relationship between perceived algorithmic control and psychological contract breach. Configurational analysis revealed that high psychological contract breach served as a core condition leading to turnover intention, and its combination with low perceived usefulness or low perceived algorithmic control constituted multiple pathways toward high turnover intention. This study extends the literature on the behavioral consequences of perceived algorithmic control and provides theoretical insights and practical implications for gig platforms seeking to optimize algorithm design, alleviate time pressure, and reduce worker turnover.