Measuring job performance under algorithmic management: A systematic review of platform work and digital labor

This study synthesizes 41 peer-reviewed studies from existing literature in order to redefine job performance in the digital world. It primarily maps out how performance is conceptualized and measured. It includes platform-assessed outputs that include star ratings, on-time/accept/cancel, key performance indicators (KPIs), service quality, and task time. Additionally, it also includes worker-perceived outcomes such as engagement, continuation, and well-being. Prior research highlights that algorithmic management systems function not only as measurement tools but also as governance mechanisms shaping worker behavior and opportunity (Benlian et al., 2022; Zhou et al., 2025). The findings show that platform-level mechanisms—ratings/reputation, KPI thresholds, allocation/dispatch, incentives/gamification, and sanctions/appeals—shape assessment and opportunity, often under opacity. Additionally, individual mediators such as autonomy/control, surveillance pressure/technostress, emotional labor and coping/gaming, self-efficacy, and psychological safety have been identified. They explain the divergence between measured performance and workers’ lived experience. The findings also document how recognition and bias in customer evaluations contaminate metrics and propagate inequality. These findings have been integrated in a multilevel framework (mechanisms, mediators, outcomes) and delineate boundary conditions (customer contact intensity, platform market power, safety-criticality, global vs. local markets). This research contributes to the body of knowledge by extending performance theory to the context of platforms, with metrics being treated as a means of governance rather than mere measurements.