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Cited by 1,050Digital Economy and Work TransformationLegal and Labor StudiesEmployment, Labor, and Gender Studies

Algorithmic management and app‐work in the gig economy: A research agenda for employment relations and HRM

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Duggan, James, Sherman, Ultan, Carbery, Ronan, McDonnell, Anthony·2020·Human Resource Management Journal
DOI 10.1111/1748-8583.12258
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Abstract

Current understanding of what constitutes work in the growing gig economy is heavily conflated, ranging from conceptualisations of independent contracting to other forms of contingent labour. This article calls for a move away from problematic aggregations by proposing a classification of gig work into three variants, all based strongly upon key technological features: app‐work, crowdwork, and capital platform work. Focusing specifically on the app‐work variant, this article's more delineated focus on the textured dimensions of this work proposes new lines of enquiry into employment relationships and human resource management. Examining the crucial role of algorithmic management, we critically discuss the impact of this novel mediation tool used by gig organisations for the nature of employment relations within app‐work, work assignment processes, and performance management. In so doing, we propose a series of research questions that can serve as a guide for future research in this increasingly important field.

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Crossref · 10.1111/1748-8583.12258

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  • App-workers reportedly crave more social interactions and networking opportunities and often attempt to compensate for lack of relatedness by creating support groups.

    “‘App-workers’, who obtain work through gig-work platforms such as Uber, reportedly crave more social interactions and networking opportunities179,185,194 and often attempt to compensate for a lack of relatedness by creating support groups that connect virtually and physically183,191,195.”

    From Understanding and shaping the future of work with self-determination theory (2022) · cites this paper as [185]

    Section 4.1 (Employment relations; page unavailable) reports that app-workers seek social interaction and networking opportunities. It does not say they create virtual or physical support groups to compensate for a lack of relatedness; the support groups it mentions are described as provided by platform organisations. (LLM, conf=0.87)

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  • The technical complexity and opacity of algorithmic systems deprives workers of ability to understand and master the system that governs their work, limiting their voice and empowerment.

    “Moreover, the technical complexity and opacity of algorithmic systems187–189 deprives workers of the ability to understand and master the system that governs their work, which limits their voice and enpowerment172,185,190.”

    From Understanding and shaping the future of work with self-determination theory (2022) · cites this paper as [185]

    Section 4.3 (Performance management; page unavailable) reports limited transparency about how work-assignment algorithms are changed. Section 4.1 (Employment relations; page unavailable) says workers in a power imbalance can struggle to have their voice heard, and section 4.2 describes constrained autonomy. The paper does not establish the claimed causal chain in which technical complexity prevents workers from understanding and mastering the system, thereby limiting their voice and empowerment. (LLM, conf=0.84)

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  • The evidence so far suggests that mostly comparative feedback provided by algorithmic management is insufficiently informative because the value of feedback is short-lived as continuously updating algorithms change what is required to perform well.

    “The evidence so far suggests that the mostly comparative feedback provided by algorithmic management is insufficiently informative because the value of the feedback is short-lived — continuously updating algorithms change what is required to perform well177,183,185.”

    From Understanding and shaping the future of work with self-determination theory (2022) · cites this paper as [185]

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  • Algorithmic management imposes irremovable boundaries that constrain workers' abilities to develop transferable competencies needed to pursue opportunities beyond precarious roles.

    “In addition, Duggan et al. (2020) argued that algorithmic management imposed irremovable boundaries that constrain workers' abilities to develop transferable competencies (e.g., job-related skills, knowledge, career networks, mentoring, etc.) which are indispensable for them to pursue opportunities beyond precarious roles.”

    From Gig work and gig workers: An integrative review and agenda for future research (2024) · cites this paper as Duggan et al. (2020)

    Section 4.1 (Employment relations; page unavailable) says training and development opportunities in most app-work are severely limited or nonexistent, and describes workers seeking skills useful for their careers and networking opportunities. It does not argue that algorithmic management imposes irremovable boundaries or establishes that workers cannot develop transferable competencies needed to move beyond precarious roles. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • Some algorithmic management platforms do not reveal all aspects of a task or penalize workers who decline jobs, severely restricting their choices.

    “Some algorithmic management platforms do not reveal all aspects of a given task (for example, not revealing the client destination before work is accepted) or penalize workers who decline jobs185, thereby severely restricting their choices.”

    From Understanding and shaping the future of work with self-determination theory (2022) · cites this paper as [185]

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Cites

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Research works printed but not linked: 3

  1. Kirven, A. (2018). Whose gig is it anyway? Technological change, workplace control and supervision, and workers' rights in the gig economy. University of Colorado Law Review, 89, 249-292. Available from http://heinonline.org/HOL/P?h=hein. journals/ucollr89&i=267
  2. Rosenblat, A., & Stark, L. (2016). Algorithmic labour and information asymmetries: A case study of Uber's drivers. Interna- tional Journal of Communication, 10, 3758-3784. https://doi.org/10.2139/ssrn.2686227 · DOI
  3. Thomas, C. (2018). Ride oversharing: Privacy regulation within the gig economy. Cardozo Arts and Entertainment Law Journal, 36, 247-276. Available from https://heinonline.org/HOL/P?h=hein.journals/caelj36&i=257

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Authors
Duggan, J., Sherman, U., Carbery, R., & McDonnell, A.
Affiliations
University College Cork (IE)
Corresponding author
Duggan, James
ORCID
Venue
Human Resource Management Journal
Publisher
Wiley
Published
2019-09-12
Volume
30
Issue
1
Pages
114-132
ISSN
  • 0954-5395 (ISSN-L)
  • 1748-8583
DOI
10.1111/1748-8583.12258
OpenAlex
W2972413052
Cited by
1,050
References
78
Citations by year
2026: 255 · 2025: 202 · 2024: 216 · 2023: 146 · 2022: 124 · 2021: 79 · 2020: 25 · 2019: 3
FWCI
116.21
Type
journal article
Language
en
Open access
Licence
onlinelibrary.wiley.com/termsAndConditions#vor (vor)
Locations
  • Human Resource Management Journal (publishedVersion)
  • Maynooth University ePrints and eTheses Archive (Maynooth University) (acceptedVersion)
  • Arrow@dit (Dublin Institute of Technology) (submittedVersion)
Funders
Irish Research Council
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 › Safety Research › Employment, Labor, and Gender Studies
Keywords
algorithmic management, gig economy, crowdwork, platform work, independent contracting, employment relations, human resource management, job assignment, performance management

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