Algorithmic management and gig workers: engagement, exhaustion and citizenship behavior

Purpose This study explores the dual effects of algorithmic management on gig workers, focusing on how transparency, fairness, feedback, lack of personalization, surveillance and control influence work engagement, exhaustion and gig worker citizenship behavior (GWCB). Grounded in the job demands-resources (JD-R) model, this research aims to investigate both the positive and negative implications of algorithmic management in the gig economy. Design/methodology/approach Data were collected from 285 gig workers in South Korea and analyzed using structural equation modeling (SEM) with SmartPLS 4. Findings The results indicate that algorithmic transparency, fairness and feedback significantly enhance work engagement, while lack of personalization, surveillance and control contribute to worker exhaustion. Additionally, work engagement positively influences GWCB, whereas exhaustion negatively impacts GWCB. Originality/value These findings provide a balanced perspective on the implications of algorithmic management, offering valuable insights for designing systems that optimize performance and support worker well-being. Practical implications include recommendations for enhancing transparency, fairness and feedback mechanisms, while mitigating the negative effects of lack of personalization, surveillance and control. The study concludes with a discussion of limitations and directions for future research.