Algorithmic control in app-work platforms: Exploring its curvilinear impact on work well-being

Abstract

Algorithmic control in app-work platforms has sparked widespread concerns regarding its impact on app workers' work well-being. Existing research has predominantly explored the economic and operational benefits yet largely ignored its effects on app workers' work well-being. This study investigates when and how algorithmic control promotes or inhibits app workers' well-being. Drawing on the job demands-resources model and the sociotechnical perspective of work design, we propose an inverted U-shaped relationship between algorithmic control and the work well-being of app workers. We find that this curvilinear relationship is further moderated by app-workers' multi-focus identification (i.e., organizational and occupational identification) and their perception of algorithmic fairness. A time-lagged study of 304 app workers supports the hypothesized model. Additionally, to address the lack of a validated measurement tool, we developed and validated a perceived algorithmic control scale based on three rational control mechanisms. Theoretical and practical implications are discussed.