Algorithmic Surveillance and Workers’ Compliance Intentions: The Role of Trust, Privacy Concerns, and Fairness in Online Crowdwork

How do workers decide to comply with, alter, or resist algorithmic surveillance? We argue that decontextualization is a key, yet overlooked, mechanism that shapes workers’ responses to algorithmic surveillance. Research has widely critiqued algorithmic surveillance, focusing on diminished worker control and agency. However, the control-resistance mechanisms related to algorithmic surveillance are undertheorized and underexplored. We draw on socio-technical systems theory and micro-level legitimacy to examine mechanisms of surveillance and resistance in online crowdwork. Our findings, based on three-wave data from 435 European online crowdworkers, show that perceived algorithmic surveillance undermines trust and fairness, while increasing privacy concerns, which in turn inform workers’ intentions to comply, alter, or resist algorithmic surveillance. Perceived decontextualization moderates these relationships, exacerbating the adverse effects on trust and fairness while mitigating the effects on privacy concerns. These outcomes extend the view that individual outcomes are shaped by social and technical factors only by demonstrating that perceived decontextualization and micro-level legitimacy judgments—that is, trust, privacy concerns, and fairness—are important socio-technical mechanisms that also impact workers’ compliance. By highlighting the overlooked role of decontextualization in shaping resistance and compliance, this study challenges dominant control-centric narratives and offers a new lens on algorithmic governance.