Every coin has two sides: a self-determination perspective on the relationship between algorithmic management and gig workers’ job burnout

Purpose This research focuses on how algorithmic management, as a primary method of platform governance, affects job burnout among gig workers. Drawing on self-determination theory, our study examines the various effects of algorithmic management’s aspects on gig workers' job burnout. Design/methodology/approach This study targeted gig workers (car drivers and food-delivery workers) and was conducted in two waves. Data analysis was facilitated using SPSS 22.0 and MPlus 8.4, a tool for CB-SEM (covariance-based structural equation modeling). Findings Algorithmic evaluation and discipline increase job burnout by negatively impacting gig workers' basic psychological needs. Algorithmic direction, in contrast, alleviates job burnout by enhancing basic psychological needs among gig workers. Practical implications Platform companies should address gig workers’ burnout by implementing advanced algorithmic management and providing autonomy-supportive environments. Adopting human-centric algorithmic practices can strengthen the platform–worker relationship, boost competence and reduce resistance to oversight. Originality/value Our study contributes to the literature by examining the various effects of algorithmic management on gig workers. By applying self-determination theory, we provide a novel perspective on understanding the mechanisms of job burnout in the gig economy.