How Platform Workers Contest Algorithmic Management: Theorizing the Dynamics of Algoactivistic Practices

Algorithmic management (AM) has become a defining feature of online labor platforms. Workers contest AM through a range of practices—a phenomenon we refer to as worker algoactivism. Existing debates portray algoactivistic practices as uniformly accessible and primarily reactive resistance directed at AM systems. Our study paints a more complex picture. Drawing on the case of Uber and using a computer-assisted, grounded-theory approach, we show how platforms’ AM reconfigurations and worker algoactivism co-evolve over time. We also show that workers’ ability to engage in different forms of algoactivism—self-optimizing, distancing, and confronting—depends on their capacity to mobilize resources, including algorithmic literacy, labor market alternatives, and collective voice. Beyond their resource requirements, the three forms differ in their logics, targets, and durability. Our findings have implications for platform providers and policymakers. Platform providers should anticipate that AM reconfigurations will fuel recurring dynamics of worker algoactivism and should not interpret worker inaction as evidence of compliance or consent. Policymakers should recognize that regulation of AM systems requires not only system transparency but also interventions that strengthen workers’ access to key resources. Doing so can reduce resource-based inequalities in the ability to contest AM and foster fairer, more sustainable platform work.

• 저널Information Systems Research
• 토픽Digital Economy and Work Transformation · Sociology and Political Science
• DOI10.1287/isre.2024.0927
• 저자Jennifer Jiang, Martin Wiener, Alexander Benlian, Martin Adam, Magnus Mähring