Dissertation Project: (Not) Managing Metrics: Algorithmic Labor Control and Resistance in the Gig Economy

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How do algorithmic metrics (e.g., ratings, rankings, and reviews) function as technologies of control and resistance in gig workers’ everyday work practices? This is the over-arching question that guides my dissertation project. Drawing insights from Science and Technology Studies (STS), critical data studies, and the sociology of work, this project examines the role of algorithmic metrics in transforming the service labor processes in the gig economy. In particular, I conceputalize metrics as socio-technical artifacts and practices that classify, commensurate, and discipline workers’ differences at a distance through the production and distribution of numbers. Using a multi-case study approach, I examine how digitally-enabled service workers encounter, interpret, and manage algorithmic performance metrics as part of their daily work in the contexts of (1) ride-hailing platforms, (2) TaskRabbit, and (3) platform cooperatives. This qualitative project explores the following sub-questions:

  1. How do platforms use metrics to manage their distributed workforces?
  2. How do workers interpret and manage metrics as part of their daily work practices?
  3. How do metrics affect the interactions between platform owners, workers, and consumers?
  4. How do workers’ interpretation and uses of metrics vary depending on platform-specific socio-technical features and labor processes?
  5. What are the alternative models of labor management in the gig economy, if any?

Dissertation Committee: Lee Humphreys (Chair), Brooke Erin Duffy, Tarleton Gillespie, and Malte Ziewitz

Ngai Keung Chan
Ph.D. Candidate in Communication

I’m a doctoral candidate in the Department of Communication at Cornell University. Currently, I study how algorithms and performance metrics transform and shape labor control and resistance in the digitally-enabled gig economy.