Articles

Algorithms at Work: The New Contested Terrain of Control

Katherine C. Kellogg

Work and Organization Studies MIT Sloan School of Management

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Melissa A. Valentine

¹ Corresponding author.

Management Science and Engineering Stanford School of Engineering

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and

Angèle Christin

Department of Communication Stanford School of Humanities and Sciences

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Published Online:15 Jan 2020https://doi.org/10.5465/annals.2018.0174

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Abstract

The widespread implementation of algorithmic technologies in organizations prompts questions about how algorithms may reshape organizational control. We use Edwards’ (1979) perspective of “contested terrain,” wherein managers implement production technologies to maximize the value of labor and workers resist, to synthesize the interdisciplinary research on algorithms at work. We find that algorithmic control in the workplace operates through six main mechanisms, which we call the “6 Rs”—employers can use algorithms to direct workers by restricting and recommending, evaluate workers by recording and rating, and discipline workers by replacing and rewarding. We also discuss several key insights regarding algorithmic control. First, labor process theory helps to highlight potential problems with the largely positive view of algorithms at work. Second, the technical capabilities of algorithmic systems facilitate a form of rational control that is distinct from the technical and bureaucratic control used by employers for the past century. Third, employers’ use of algorithms is sparking the development of new algorithmic occupations. Finally, workers are individually and collectively resisting algorithmic control through a set of emerging tactics we call algoactivism. These insights sketch the contested terrain of algorithmic control and map critical areas for future research.

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Vol. 14, No. 1

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Received 11 September 2018

Accepted 18 November 2019

Published online 15 January 2020

Published in print 1 January 2020

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Acknowledgments

We gratefully thank Catherine Turco for her extremely helpful contributions from the beginning of this project, and J.P. Eggers and two anonymous reviewers for improving the article throughout the review process. The article has benefited greatly from comments by Matt Beane, Michael Bernstein, Beth Bechky, Samer Faraj, Arvind Karunakaran, Sarah Lebovitz, Vili Lehdonvirta, Hila Lifshitz-Assaf, Melissa Mazmanian, Wanda Orlikowski, Alex Rosenblat, Ryan Stice-Lusvardi, Emily Truelove, and Steve Vallas.

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