Navigating the nuanced realm of app-work, the current research draws from the Job Demands-Resources (JD-R) model, emphasizing not just job demands but also the untapped job resources embedded in algorithmic control. Our investigation then delves into how perceived algorithmic control (PAC) influences the multitasking performance of delivery riders in the gig economy. First, we developed and validated the PAC scale following rigorous methodological approaches. Second, through a 7-day diary survey of 102 Chinese delivery riders, we elucidated the mediating role of job crafting in the positive relationship between PAC and delivery riders’ multitasking performance. Moreover, we found that personal-level resources (i.e., proactive personality) and technological-level resources (i.e., algorithmic transparency) serve as vital catalysts that amplify the constructive pathway from PAC to job crafting. Our research reveals the deep-seated implications of algorithmic control on app-workers’ work outcomes and illuminates the conditional and multifaceted impacts of individual and algorithmic attributes. Implications, limitations, and future research trajectories are further discussed.
