Algorithmic surveillance and workers’ compliance

The role of trust, privacy concerns, and fairness in online crowdwork

Bibliographic Data

ID9890959
AuthorsWard Van Zoonen (0000-0002-8531-8784, University of Jyväskylä, corresponding author), Monika E von Bonsdorff (0000-0002-4689-2126, University of Jyväskylä, corresponding author), Beatrice I J M Van Der Heijden (0000-0001-8672-5368, Radboud University, The Netherlands, corresponding author)
Year2025
Publication date2025-10-16
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueHuman Relations (JOURNAL)
Journal identifiersISSN: 0018-7267 • E-ISSN: 1741-282X
PublisherSAGE Publications (PUBLISHER • US)
DOI10.1177/00187267251379698
OpenAlexW4415267809
LanguageEN
References cited103

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

Legitimacy · Mechanism (biology · Narrative · Resistance (ecology · Social media · Through-the-lens metering · Variety (cybernetics · Digital Economy and Work Transformation · Ethics and Social Impacts of AI · Privacy, Security, and Data Protection

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Open DOIOpen Access