Algorithms at Work: The New Contested Terrain of Control

Kellogg, Katherine C.; Valentine, Melissa A.; Christin, Angéle

doi:10.5465/annals.2018.0174

Cited by 2,755Digital Economy and Work TransformationLegal and Labor StudiesInformation Systems Theories and Implementation

Algorithms at Work: The New Contested Terrain of Control

Kellogg, Katherine C., Valentine, Melissa A., Christin, Angéle·2020·Academy of Management Annals

DOI 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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For advertisers, algorithmic ad delivery creates uncertainty about the targets they reach and reduces their control over the output of their actions.
“For advertisers, algorithmic ad delivery creates uncertainty about the targets they reach and more generally reduces their control over the output of their actions (Kellogg et al., 2020).”
From How online advertising targets consumers: The uses of categories and algorithmic tools by audience planners (2024) · cites this paper as Kellogg et al., 2020
Algorithmic systems are socially constructed and embedded in existing power dynamics, and are neither created nor function outside the biases rooted in the organizational cultures within which they are implemented.
“Understanding that algorithmic systems are socially constructed and embedded in existing power dynamics also means these systems are neither created nor function outside the biases rooted in the organizational cultures within which they are implemented (Kellogg et al., 2020).”
From Algorithmic management in a work context (2021) · cites this paper as Kellogg et al., 2020
The use of advanced algorithms creates new opportunities for managers to exercise control over the workforce.
“However, the use of advanced algorithms cre-ates new opportunities for managers to exercise control over the workforce (Kellogg et al., 2020; Shapiro, 2018).”
From Algorithmic management in a work context (2021) · cites this paper as Kellogg et al., 2020
Digital labor platforms manage gig workers via algorithmic management.
“Different from traditional staffing agencies, digital labor platforms manage gig workers via algorithmic management (Kellogg et al., 2020;McDonnell et al., 2021).”
From Gig work and gig workers: An integrative review and agenda for future research (2024) · cites this paper as Kellogg et al., 2020
Gig workers' customer evaluations serve as surrogates for their performance ratings and affect the likelihood of their future orders.
“Indeed, gig workers' customer evaluations not only serve as surrogates for their performance ratings but also affect the likelihood of their future orders (Kellogg et al., 2020).”
From Gig work and gig workers: An integrative review and agenda for future research (2024) · cites this paper as Kellogg et al., 2020

Management scholars have provided very little insight into AI during the last two decades.
“Management scholars have therefore provided very little insight into AI during the last two decades (Kellogg, Valentine, & Christin, 2020;Lindebaum, Vesa, & den Hond, 2020).”
From Artificial Intelligence and Management: The Automation–Augmentation Paradox (2021) · cites this paper as Kellogg, Valentine, & Christin, 2020
Whenever key actors of an organization negotiate who is authorized to modify the AI system and to what extent modification is to be undertaken, dimensions of power and control are on the table.
“Whenever the key actors of an organization negotiate who is authorized (4.1 & 4.3) to modify the AI system and to what extent modification is to be undertaken, dimensions of power and control are on the table (Kellogg et al. 2020;Ren 2019;Vallas and Hill 2012).”
From Keeping the organization in the loop: a socio-technical extension of human-centered artificial intelligence (2023) · cites this paper as Kellogg et al. 2020
Algorithms are currently able to perform six management functions: monitoring, goal setting, performance management, scheduling, compensation, and job termination.
“First, we identify six management functions that algorithms are currently able to perform: monitoring, goal setting, performance management, scheduling, compensation, and job termination.”
From Algorithms as work designers: How algorithmic management influences the design of jobs (2022) · cites this paper as Kellogg et al., 2019
Algorithmic management is a technological phenomenon that is not contingent on the type of organization and can be found in a large array of work contexts.
“However, recent reviews and conceptualizations (Kellogg et al., 2019;Mateescu & Nguyen, 2019a;Schafheitle et al., 2020) consider algorithmic management as a technological phenomenon that is not contingent on the type of organization and can be found in a large array of work contexts.”
From Algorithms as work designers: How algorithmic management influences the design of jobs (2022) · cites this paper as Kellogg et al., 2019
Algorithmic control of platforms is used to control the behaviour of users on platforms to achieve the platform owners' corporate objectives.
“Algorithmic control of platforms is used to control the behaviour of users on platforms to achieve the platform owners’ corporate objectives”
From Content Creators Between Platform Control and User Autonomy: The Role of Algorithms and Revenue Sharing (2023) · cites this paper as Kellogg et al. 2020
Other studies have found that content creators are 'leveraging algorithms' and 'working around the algorithm' by constantly uploading, A/B testing, and diversifying their incomes.
“Other studies have found that content creators are ‘leveraging algorithms’ (Kellogg et al. 2020, p. 391) and ‘working around the algorithm’ (Curchod et al. 2020, p. 17) by constantly uploading (Arriagada and Ibáñez 2020), A/B testing (Cotter 2019), and diversifying their incomes (Cutolo and Kenney 2021).”
From Content Creators Between Platform Control and User Autonomy: The Role of Algorithms and Revenue Sharing (2023) · cites this paper as Kellogg et al. 2020
Content creators focus on content and topics that have delivered good key figures in the past, similar to algorithmic rewarding.
“Thus, they focus on content and topics that have delivered good key figures in the past, similar to algorithmic rewarding (Kellogg et al. 2020).”
From Content Creators Between Platform Control and User Autonomy: The Role of Algorithms and Revenue Sharing (2023) · cites this paper as Kellogg et al. 2020
Previous literature has identified A/B testing, algorithmic gossip, and leveraging algorithms as coping mechanisms.
“Previous literature has already identified A/B testing (Cotter 2019), algorithmic gossip (Bishop 2019), and leveraging algorithms (Kellogg et al. 2020) as coping mechanisms.”
From Content Creators Between Platform Control and User Autonomy: The Role of Algorithms and Revenue Sharing (2023) · cites this paper as Kellogg et al. 2020
Empirical research shows that freelance platform workers respond to algorithmic control by gaming and at times sabotaging algorithms to (re)gain control.
“Empirical research shows that freelance platform workers respond to algorithmic control by gaming and at times, sabotaging algorithms to (re)gain control (Gandini, 2019;Kellogg et al., 2020;Newlands, 2020;Veen et al., 2020).”
From Algorithmic human resource management: Synthesizing developments and cross-disciplinary insights on digital HRM (2021) · cites this paper as Kellogg et al., 2020
The contested-terrain dynamic that the algorithmic management literature describes was deployed in a firm with no specialist function to contest it.
“This is the contested-terrain dynamic that the algorithmic management literature describes, deployed in a firm with no specialist function to contest it [21].”
From Borrowed Competence: When AI-Driven HR Builds Startups’ People Capability and When It Hollows It Out (2026) · cites this paper as [21]
Algorithmic management might perpetuate societal biases and reproduce discriminatory practices at work, whether the discrimination is built into the algorithms, management's use of the algorithm, or customer ratings of workers.
“Algorithmic management might perpetuate societal biases and reproduce discriminatory practices at work, whether the discrimination is built into the algorithms, or management's use of the algorithm, or as a result of customer's rating of workers (Noble 2018;Benjamin 2019;Kellogg et al. 2020;Akter et al. 2021;Zajko 2021;Heinrichs 2022).”
From Artificial intelligence and work: a critical review of recent research from the social sciences (2024) · cites this paper as Kellogg et al. 2020

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Authors: Kellogg, K. C., Valentine, M. A., & Christin, A.
Affiliations: Massachusetts Institute of Technology (US)
Stanford University (US)
Department of Communication Stanford School of Humanities and Sciences
ORCID: Kellogg, Katherine C.
Valentine, Melissa A.
Venue: Academy of Management Annals
Publisher: Academy of Management
Published: 2020-01
Volume: 14
Issue: 1
Pages: 366-410
ISSN: 1941-6067 (ISSN-L)
1941-6520
DOI: 10.5465/annals.2018.0174
OpenAlex: W2990520658
Cited by: 2,732
References: 284
Citations by year: 2026: 1145 · 2025: 477 · 2024: 350 · 2023: 288 · 2022: 247 · 2021: 175 · 2020: 48 · 2019: 2
FWCI: 237.04
Type: journal article
Language: en
Open access: closed
Locations: Academy of Management Annals (publishedVersion)
Topics: Social Sciences › Sociology and Political Science › Digital Economy and Work Transformation
Social Sciences › Business, Management and Accounting › Industrial relations › Legal and Labor Studies
Social Sciences › Sociology and Political Science › Information Systems Theories and Implementation
Keywords: algorithmic control, contested terrain, labour process theory, rational control, employee resistance, worker evaluation, worker discipline

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Algorithms at Work: The New Contested Terrain of Control

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