Algorithmic management (AM) has become a defining feature of online labor platforms (OLPs), profoundly shaping platform workers’ control over their working conditions. Prior research has documented diverse forms of worker resistance to AM—or algoactivism—yet existing studies rest on two problematic assumptions. First, they often assume that algoactivistic practices are uniformly accessible and arise directly from workers’ perceptions of structural constraints. Second, they assume that such practices are primarily reactive forms of resistance broadly targeted at the OLP’s AM system. These assumptions obscure heterogeneity in workers’ motivations and resources, as well as variation in how algoactivistic practices unfold. This study develops a more nuanced understanding of platform workers’ algoactivism by tracing how corresponding practices emerge through situated, reflective, and resource-dependent processes. Drawing on the contested terrain lens from labor process theory, we conceptualize the interplay between OLPs and workers as an ongoing struggle over control of working conditions. We examine this struggle in the context of Uber, a widely recognized extreme case of AM. Using a computer-assisted grounded theory approach that integrates topic modeling with qualitative coding procedures across multiple data sources, we develop a process-theoretical model of how platform workers contest AM. Our model centers on three recurring dynamics—reassessing terrain, exploring opportunities for contestation, and contesting terrain through algoactivistic practices—and yields two core theoretical contributions. First, we show that worker algoactivism depends on continual terrain reassessments and uneven capacities to engage in three forms of resourcing: algorithm, market, and voice resourcing. Second, we theorize algoactivism as a heterogeneous and multi-arena phenomenon comprising self-optimizing, distancing, and confronting practices that vary in their logics, targets, and durability. Together, these contributions advance a more dynamic and agentic understanding of worker algoactivism and provide actionable insights for the design and governance of platform-mediated work.

Martin holds the Chair of Business Information Systems, esp. Business Engineering at TU Dresden, Germany. He is also an Affiliated Researcher at FAU Erlangen-Nürnberg (Germany) and the Stockholm School of Economics Institute for Research (Sweden). Before joining TU Dresden, he was an Associate Professor at Bentley University, USA. His research focuses on algorithmic control and management, digital transformation processes, human-AI collaboration, and value creation through open data. His work has been published in leading Information Systems journals, including MIS Quarterly (MISQ), Information Systems Research (ISR), and the Journal of Management Information Systems (JMIS). He currently serves as Senior Editor for the Information Systems Journal(ISJ) and as Associate Editor for Business & Information Systems Engineering (BISE) and ISR.

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