When algorithms watch: the threat of algorithmic tracking and evaluation to app-workers’ decent work

Purpose Algorithmic tracking and evaluation (ATE) has become prevalent in the management of online labor platforms. However, empirical research on its impact on app-workers’ decent work remains scarce. Drawing on an integrated framework of cognitive appraisal theory of stress and procedural fairness theory, this research examines how and when ATE erodes decent work among app-workers. Design/methodology/approach This study aims to reveal the impact of ATE on app-workers’ decent work, while also exploring the mediating role of perceived procedural unfairness and the moderating role of relative deprivation. Data collected from an experimental study (Study 1; N = 214) and a multi-wave field survey (Study 2; N = 526) were used for empirical study. Analytical procedures incorporated moderated mediation testing through regression analysis, and bootstrapping techniques to examine both direct effects and boundary conditions. Findings The results demonstrate that ATE serves as a significant hindrance stressor that increases perceived procedural unfairness, which in turn diminishes work experience of decent work. Additionally, the study finds that relative deprivation further strengthens these relationships, emphasizing the value of social comparison around algorithmic processes. Originality/value These insights contribute to the recent literature on gig work, directing platform enterprises to leverage algorithm tools while safeguarding decent work for app-workers.