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.