How Algorithmic Management Shapes Agility in the Gig Economy: The Role of Job Autonomy

The rapid expansion of digital platform–based services has intensified the use of algorithmic management (AM) to coordinate gig workers, raising concerns about its implications for workforce agility. Drawing on the Job Demands–Resources (JD–R) model, this study examines the effects of AM functions (i.e., monitoring, goal setting, scheduling, performance rating, and compensation) on gig worker agility (i.e., proactivity, adaptability, and resilience) and the mediating role of job autonomy within platform-based delivery work in Saudi Arabia. Data were collected from 378 frontline couriers working on major app-based delivery platforms and analyzed using partial least squares structural equation modeling (PLS-SEM). The findings indicate that AM is not a uniform construct, as its functions exert differential effects on job autonomy and gig worker agility. While some AM functions show partially significant direct negative effects on agility dimensions, all AM functions exhibit significant negative indirect effects through erosion of job autonomy. These results suggest that AM operates as a hindrance demand that reduces autonomy, a critical job resource, thereby constraining workers’ capacity to remain proactive, adaptable, and resilient. The study contributes to the literature on AM and digital platform work by providing empirical evidence from the e-commerce delivery context and highlighting the need to design platform governance systems that preserve worker autonomy and agility.