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Prime Suspect: Mechanisms of Labor Control at Amazon's Warehouses

Vallas, Steven P., Johnston, Hannah, Mommadova, Yana·2022·Work and Occupations
DOI 10.1177/07308884221106922
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Abstract

What mechanisms has Amazon deployed in its effort to control the labor of its warehouse employees? This question holds both practical and theoretical interest, given Amazon's prominent position in the economy and the wider importance of the logistics sector for consumer capitalism. This paper, part of a broader mixed-methods study of Amazon's workplace regime, uses a small national sample of interviews with Amazon warehouse workers (N = 46) to identify the mechanisms of labor control the company invokes. In keeping with accounts propounded by activists and journalists, we find evidence of highly coercive labor controls, chiefly in the form of what we call techno-economic despotism (which applies algorithmic technology to a precariously employed workforce). Yet many workers also experience forms of labor control that rely not on coercion but on the generation of consent. We identify three such mechanisms of hegemonic labor control - normative, relational, and governmental – that Amazon uses to foster workers’ consent. The efficacy of Amazon's workplace regime stems largely from its ability to deploy a multiplicity of labor controls that resonate with different groups holding distinct positions in the labor process. Given shifts in the social and economic conditions that bear on the company's regime, cracks have begun to appear in Amazon's armor, potentially reducing the traction its labor control mechanisms have gained with segments of its employees.

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Metadata

Authors
Vallas, S. P., Johnston, H., & Mommadova, Y.
Affiliations
  • Department of Sociology and Anthropology, Northeastern University, Boston, Massachusetts, USA
  • School of Human Resources Management, York University, Toronto, Ontario, Canada
ORCID
Vallas, Steven P.
Venue
Work and Occupations
Publisher
SAGE Publications
Published
2022-06-19
Volume
49
Issue
4
Pages
421-456
ISSN
  • 0730-8884
  • 1552-8464
DOI
10.1177/07308884221106922
Cited by
73
References
2
Language
en
Licence
journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)

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