A smart place to work? Big data systems, labour, control and modern retail stores
Open accessAbstract
The modern retail store is a complex coded assemblage and data‐intensive environment, its operations and management mediated by a number of interlinked big data systems. This paper draws on an ethnography of a retail store in Ireland to examine how these systems modulate the functioning of the store and working practices of employees. It was found that retail work involves a continual movement between a governance regime of control reliant on big data systems which seek to regulate and harnesses formal labour and automation into enterprise planning, and a disciplinary regime that deals with the symbolic, interactive labour that workers perform and act as a reserve mode of governmentality if control fails. This continual movement is caused by new systems of control being open to vertical and horizontal fissures. While retail functions as a coded assemblage of control, systems are too brittle to sustain the governmentality desired.
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Descriptive algorithms are likely to multiply the metrics available to quantify employees' actions, emotions, performance, behaviors, attitudes, physical state, and customer-related information such as satisfaction and traffic.
“Descriptive algorithms are likely to reinvent the world of performance management (PM) in multiplying the metrics available to precisely quantify various facets of employees' actions, emotions, performance, behaviors, attitudes and physical state (Angrave et al., 2016;Tambe et al., 2019), but also customer-related information such as satisfaction and traffic (Evans & Kitchin, 2018;Levy & Barocas,”
From Algorithms as work designers: How algorithmic management influences the design of jobs (2022) · cites this paper as Evans & Kitchin, 2018
The Conclusion (page unavailable) says customer satisfaction is delegated to big-data and algorithmic processing. The section “Non-coded activities and symbolic labour” (page unavailable) describes a running count of customers passing through the self-scan area, supporting the traffic example. The paper does not establish that descriptive algorithms will multiply metrics that precisely quantify customer satisfaction and traffic. (LLM, conf=0.84)
Experimental — accuracy not yet measuredBecause data collected through monitoring devices is often used to assess performance, workers sometimes tend to work for data, focusing more on monitored tasks and eluding unmonitored ones, which can hamper task variety.
“Because data collected through monitoring devices is often used to assess their performance, workers sometimes tend to”
From Algorithms as work designers: How algorithmic management influences the design of jobs (2022) · cites this paper as Evans & Kitchin, 2018
The ‘Working for data’ section says PDA-directed work is measured through system data and leaves little room for customer interaction. ‘Non-coded activities and symbolic labour’ says customer service is not captured in performance metrics, but it does not show workers choosing to evade unmonitored tasks; it says pickers must assist customers when asked. The claimed reduction in task variety is therefore supported only indirectly. (LLM, conf=0.86)
Experimental — accuracy not yet measuredCompanies can use algorithmic systems to regulate the supply of workers by implementing scheduling decisions and automatically sending the schedule to workers.
“Companies can also use algorithmic systems to regulate the supply of workers by implementing the scheduling decisions and automatically sending the schedule to workers (Evans & Kitchin, 2018;Griesbach et al., 2019).”
From Algorithms as work designers: How algorithmic management influences the design of jobs (2022) · cites this paper as Evans & Kitchin, 2018
The Introduction says algorithms inform workplace decisions and constrain how workers interact with systems; the Conclusion says code and data increasingly automate and regulate the management of work (page numbers unavailable). But the passages about scheduling concern deliveries, not workers’ shifts. Our copy does not support the specific claim that systems regulate worker supply by making scheduling decisions and automatically sending schedules to workers. (LLM, conf=0.82)
Experimental — accuracy not yet measuredAlgorithmic performance management is positively associated with job insecurity.
“Job insecurity (+)”
From Algorithms as work designers: How algorithmic management influences the design of jobs (2022) · cites this paper as Evans and Kitchin (2018)
The Introduction (page unavailable) links algorithm-governed work to precarious employment, which supports a general connection with insecurity. It does not establish a positive association specifically between algorithmic performance management and job insecurity; its discussion of performance monitoring addresses control of work rather than that outcome. (LLM, conf=0.78)
Experimental — accuracy not yet measuredAlgorithmic metrics can play a central role in the performance management process of traditional workers.
“Some studies have described how these metrics can play a central role (if not the entire role) in the performance management process of both traditional workers (Bakewell et al., 2018;Dhir & Chhabra, 2019;Evans & Kitchin, 2018;Kirven, 2018;Levy & Barocas, 2018) and gig workers (Gerber & Krzywdzinski, 2019).”
From Algorithms as work designers: How algorithmic management influences the design of jobs (2022) · cites this paper as Evans & Kitchin, 2018
Unreasonable monitoring systems foster the datafication of work, or the 'working for data' phenomenon, viewed as the orientation of workers' efforts and energy towards monitored or quantified aspects of the job, which are presumably not the most meaningful, complex or valued ones.
“Unreasonable monitoring systems foster the datafication of work, or the”
From Algorithms as work designers: How algorithmic management influences the design of jobs (2022) · cites this paper as Evans & Kitchin, 2018
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Cited by
Sorted by citations
Algorithms as work designers: How algorithmic management influences the design of jobs
Parent-Rocheleau, X., & Parker, S. K. (2022) · Human Resource Management Review · doi:10.1016/j.hrmr.2021.100838 · Cited by 512 · see citing works
Quantifying human resource management: a literature review
Coron, C. (2022) · Personnel Review · doi:10.1108/pr-05-2020-0322 · Cited by 40 · see citing works
Terry, E., Marks, A., Dakessian, A., & Christopoulos, D. (2022) · Work, Employment and Society · doi:10.1177/0950017020979504 · Cited by 23 · see citing works
- Authors
- Evans, L., & Kitchin, R.
- Affiliations
- Swansea University (GB)
- National University of Ireland, Maynooth (IE)
- Corresponding author
- Evans, Leighton
- Venue
- New Technology, Work and Employment
- Publisher
- Wiley
- Published
- 2018-03
- Volume
- 33
- Issue
- 1
- Pages
- 44-57
- ISSN
- 0268-1072 (ISSN-L)
- 1468-005X
- OpenAlex
- W2785665207
- Cited by
- 61
- References
- 2
- Citations by year
- 2026: 4 · 2025: 11 · 2024: 11 · 2023: 9 · 2022: 9 · 2021: 7 · 2020: 3 · 2019: 4 · 2018: 3
- FWCI
- 30.45
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- New Technology Work and Employment (publishedVersion)
- Maynooth University ePrints and eTheses Archive (Maynooth University) (acceptedVersion)
- OSF Preprints (OSF Preprints) (submittedVersion, cc-by)
- Funders
- European Research Council (ERC‐2012‐AdG‐323636)
- Topics
- Social Sciences › Business, Management and Accounting › Industrial relations › Legal and Labor Studies
- Social Sciences › Sociology and Political Science › Digital Economy and Work Transformation
- Social Sciences › Safety Research › Employment, Labor, and Gender Studies
- Keywords
- big data systems, retail stores, retail work, labor control, governmentality, disciplinary regime, automation, assemblage theory
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