Perceived algorithmic evaluation and app-workers' service performance: The roles of flow experience and challenges of gig work

Algorithmic evaluations are becoming increasingly common among app-workers. However, there is limited research on how app-workers' perceptions of these evaluations (perceived algorithmic evaluation, or PAE) affect service performance. Our study addresses this gap in three ways: first, we introduce a new method to measure PAE among app-workers. Second, building on flow theory, we explore how app-workers' flow experience mediates the relationship between PAE and service performance. Third, by integrating the conservation of resources theory and flow theory, we examine how viability challenges might reduce the positive impact of PAE on app-workers' flow experience. Using both interviews and surveys, our research reveals that PAE positively influences app-workers' flow experience and, in turn, their service performance. Notably, we find that when workers face more viability challenges, the positive effects of PAE on their flow experience and service performance decrease. Our findings highlight the importance of algorithmic evaluation in shaping app-workers' work experiences and outcomes in the gig economy and have significant theoretical and practical implications.