The DOI matches its Crossref record; 298 of the 298 references it cites were found in a registry or an index.
Algorithms at Work: The New Contested Terrain of Control
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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In the citing paper
“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
In the citing paper
“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
In the citing paper
“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
In the citing paper
“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
In the citing paper
“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
In the citing paper
“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
In the citing paper
“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
In the citing paper
“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
In the citing paper
“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
In the citing paper
“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
In the citing paper
“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
In the citing paper
“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
In the citing paper
“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
In the citing paper
“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
In the citing paper
“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]
In the citing paper
“The lack of transparency in algorithmic decision-making has been associated with dynamics of control, power, and surveillance (Kellogg et al., 2020).”
From Why do users trust algorithms? A review and conceptualization of initial trust and trust over time (2022) · cites this paper as Kellogg et al., 2020
In the citing paper
“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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“…we develop five propositions in our discussion section that relate to current work on HRM on digital work platforms (Duggan et al., 2020; Kellogg et al., 2020; Leicht-Deobald et al., 2019; Meijerink & Keegan, 2019) and the broader stream of research on HRM from a worker perspective…”
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Metadata
- 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
- 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
- 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
- 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 › Business, Management and Accounting › Management Information Systems › 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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