Rethinking Platform Capitalism: Mapping the Algorithmic Logics and Labor Control Models of Uber Eats and Fantuan

• In Event: Labor and Labor Movements Section Refereed Roundtables
  In Refereed Roundtable (60 minute): Roundtable 10: Tech Labor, Platform Capitalism, and Algorithmic Labor Control 2

Abstract

This article examines how platform companies structure labor control in the food-delivery sector by comparing two major apps operating in the U.S. and Canada: Uber Eats and Fantuan. While existing research on algorithmic management emphasizes the emergence of “algorithmic despotism”—opaque, data-driven systems that shape worker behavior through surveillance, nudges, and individualized pay schemes—there is a need to map out the variations in platform control. Drawing on a year-long ethnography that combined app walkthroughs, participant observation, and semi-structured interviews with 34 delivery workers in the Greater Toronto/Hamilton Area, we analyze the key organizational “nodes” of each platform, from onboarding and order dissemination to pay structures and worker surveillance. Our comparative analysis demonstrates that labor control under platform capitalism does not operate as a single, monolithic system. Uber Eats deploys a “dispersive-extractive” model that extracts profit through algorithmic opaqueness and performance metrics throughout the driver's workflow. Fantuan, by contrast, operates through a “value-laden” model in which ethnocultural norms and human managerial interventions work alongside algorithmic systems to enforce hierarchy, efficiency, and compliance. By mapping these distinct regimes, this article develops a comparative framework for understanding the variations within algorithmic management and calls for more research across ethnically diverse platform markets.

Authors

• Tommy Wu, McMaster University

• Junyi Zhang, McMaster University

• Zachary WeiJian Gan, University of Toronto

• Ayra Thomas, University of Toronto