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Everything We Do, Everything We Press: Data-Driven Remote Performance Management in a Mobile Workplace

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Bakewell, Lyndsey L., Vasileiou, Konstantina, Long, Kiel S., Atkinson, Mark, Rice, Helen, Barreto, Manuela, Barnett, Julie, Wilson, Michael +2 more·2018·Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems
DOI 10.1145/3173574.3173945
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Abstract

This paper examines how data-driven performance monitoring technologies affect the work of telecommunications field engineers. As a mobile workforce, this occupational group rely on an array of smartphone applications to plan, manage and report on their jobs, and to liaise remotely with managers and colleagues. These technologies intend to help field engineers be more productive and have greater control over their work; however they also gather data related to the quantity and effectiveness of their labor. We conducted a qualitative study examining engineers' experiences of these systems. Our findings suggest they simultaneously enhance worker autonomy, support co-ordination with and monitoring of colleagues, but promote anxieties around productivity and the interpretation of data by management. We discuss the implications of data-driven performance management technologies on worker agency, and examine the consequences of such systems in an era of quantified workplaces.

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  • Use of top performers' scores as an ever-increasing standard of performance is likely to result in perpetually higher expectations and work intensification.

    “According to Bakewell et al. (2018) and Guendelsberger (2019), the use of top performers' scores as the ever-increasing standard of performance is likely to result in perpetually higher expectations and work intensification.”

    From Algorithms as work designers: How algorithmic management influences the design of jobs (2022) · cites this paper as Bakewell et al., 2018

    In “Being Monitored and Monitoring Others” (page not provided), the paper reports that engineers compared their performance with colleagues and that the apps encouraged competition. In “Flexibility, Accountability, and Intensification of Work” (page not provided), it describes intensified work. It does not say that top performers’ scores became an ever-rising standard or that expectations rose perpetually. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • Evidence shows advantages of providing employees with a staff application to modify algorithmic scheduling or task assignment decisions, to pick a better task assignment or to swap shifts with coworkers without penalties.

    “Evidence shows the advantages of providing employees with a staff application to modify algorithmic scheduling or task assignment decisions, to pick a better task assignment or to swap shifts with coworkers without penalties (Bakewell et al., 2018;Williams, 2017).”

    From Algorithms as work designers: How algorithmic management influences the design of jobs (2022) · cites this paper as Bakewell et al., 2018

    In “Independence and (not) Being in Control” (page unavailable), engineers viewed the ability to reorder assigned jobs through the Your Jobs app positively. But they could not change which jobs were assigned or move jobs between morning and afternoon; the section also reports concern that poor scheduling decisions would be held against them. The paper does not establish an ability to choose a better assignment or swap shifts with coworkers without penalties. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

  • Algorithmic 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 Bakewell et al., 2018

  • Algorithmic performance management is positively associated with workload.

    “Workload (+)”

    From Algorithms as work designers: How algorithmic management influences the design of jobs (2022) · cites this paper as Bakewell et al. (2018)

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Cited by

Sorted by citations

Authors
Bakewell, L. L., Vasileiou, K., Long, K. S., Atkinson, M., Rice, H., Barreto, M., Barnett, J., Wilson, M., Lawson, S., & Vines, J.
Affiliations
  • Loughborough University (GB)
  • University of Bath (GB)
  • Northumbria University (GB)
  • University of Exeter (GB)
  • Independent Researcher, Newcastle upon Tyne, United Kingdom
ORCID
Venue
Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems
Publisher
ACM
Published
2018-04-21
Pages
1-14
DOI
10.1145/3173574.3173945
OpenAlex
W2796424995
Cited by
23
References
3
Citations by year
2026: 3 · 2025: 1 · 2024: 6 · 2023: 4 · 2021: 5 · 2020: 1 · 2019: 3
FWCI
6.86
Type
conference paper
Language
en
Open access
Licence
Locations
  • CHI Conference on Human Factors in Computing Systems (publishedVersion, cc-by)
  • Northumbria Research Link (Northumbria University) (submittedVersion)
  • Open Research Exeter (University of Exeter) (submittedVersion)
  • DMU Open Research Archive (De Montfort University) (submittedVersion)
  • Loughborough University Institutional Repository (Loughborough University) (submittedVersion, cc-by-nc-nd)
Funders
ESRC (ES/M003558/1)
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
mobile workforce, mobile application, employee autonomy, worker agency

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