Navigating the light and dark sides of algorithmic control: a double-sided impact on gig worker well-being

Purpose In the digital economy, gig platforms use AI-based algorithms for resource allocation and operations management. However, evidence on algorithmic control's incentive effect is mixed. Moreover, it remains unclear how and when algorithmic control impacts gig workers' work well-being. Consequently, this study develops and tests a theoretical model to address these paradoxes of algorithmic control in gig platforms. Design/methodology/approach Based on the job demands-resources (JD-R) model, we propose that the three dimensions of algorithmic control provide gig workers with job demands and job resources, respectively. We developed a theoretical framework linking algorithmic control and work well-being, with a focus on the moderating role of perceived algorithmic fairness. We conducted a time-lagged study on Chinese gig platforms across three time points. Findings The results show that: (1) Algorithmic tracking evaluation and behavioral constraint, as job demands, increase gig workers' occupational burnout, thereby reducing their work well-being. (2) In contrast, algorithmic standardized guidance, as a job resource, enhances gig workers' work autonomy, which in turn improves their work well-being. (3) Perceived algorithmic fairness weakens the positive relationship of algorithmic tracking evaluation and behavioral constraint with occupational burnout. Originality/value This study shows that algorithmic control exerts opposing influences on gig workers' work well-being via job demands and job resources. We reveal the paradox of algorithmic control and address how and when gig platforms affect well-being through algorithmic control. Our findings provide practical insights for gig platforms utilizing algorithmic technology in staff management.