The DOI matches its Crossref record.
Humans as a Service: The Promise and Perils of Work in the Gig Economy
Abstract
The rise of the gig economy is disrupting business models across the globe. Platforms’ digital work intermediation has had a profound impact on traditional conceptions of the employment relationship. The completion of ‘tasks’, ‘gigs’, or ‘rides’ in the (digital) crowd fundamentally challenges our understanding of work in modern labour markets: gone are the stable employment relationships between firms and workers, replaced by a world in which everybody can be ‘their own boss’ and enjoy the rewards—and face the risks—of independent businesses. Is this the future of work? What are the benefits and challenges of crowdsourced work? How can we protect consumers and workers without stifling innovation? Humans as a Service provides a detailed account of the growth and operation of gig-economy platforms, and develops a blueprint for solutions to the problems facing on-demand workers, platforms, and their customers. Following a brief introduction to the growth and operation of on-demand platforms across the world, the book scrutinizes competing narratives about ‘gig’ work. Drawing on a wide range of case studies, it explores how claims of ‘disruptive innovation’ and ‘micro-entrepreneurship’ often obscure the realities of precarious work under strict algorithmic surveillance, and the return to a business model that has existed for centuries. Humans as a Service shows how employment law can address many of these problems: gigs, tasks, and rides are work—and should be regulated as such. A concluding chapter demonstrates the broader benefits of a level playing field for consumers, taxpayers, and innovative entrepreneurs.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Duggan, J., Sherman, U., Carbery, R., & McDonnell, A. (2020) · Human Resource Management Journal · doi:10.1111/1748-8583.12258 · Cited by 1,050
Köchling, A., & Wehner, M. C. (2020) · Business Research · doi:10.1007/s40685-020-00134-w · Cited by 511
Uber happy? Work and well-being in the ‘Gig Economy’
Berger, T., Frey, C. B., Levin, G., & Danda, S. R. (2019) · Economic Policy · doi:10.1093/epolic/eiz007 · Cited by 267
Meijerink, J., Boons, M., Keegan, A., & Marler, J. (2021) · The International Journal of Human Resource Management · doi:10.1080/09585192.2021.1925326 · Cited by 267
Tubaro, P., Casilli, A. A., & Coville, M. (2020) · Big Data & Society · doi:10.1177/2053951720919776 · Cited by 262
Regulate, replicate, and resist – the conjunctural geographies of platform urbanism
Graham, M. (2020) · Urban Geography · doi:10.1080/02723638.2020.1717028 · Cited by 252
The ethical debate about the gig economy: A review and critical analysis
Tan, Z. M., Aggarwal, N., Cowls, J., Morley, J., Taddeo, M., & Floridi, L. (2021) · Technology in Society · doi:10.1016/j.techsoc.2021.101594 · Cited by 225
Good models borrow, great models steal: intellectual property rights and generative AI
Chesterman, S. (2025) · Policy and Society · doi:10.1093/polsoc/puae006 · Cited by 156
Artificial intelligence and work: a critical review of recent research from the social sciences
Deranty, J.-P., & Corbin, T. (2024) · AI & SOCIETY · doi:10.1007/s00146-022-01496-x · Cited by 118
“Recent research in political science and labour law focuses on these aspects (Prassl 2018).”
Kuhn, K. M., Meijerink, J., & Keegan, A. (2021) · Research in Personnel and Human Resources Management · doi:10.1108/s0742-730120210000039001 · Cited by 93
Webster, N. A., & Zhang, Q. (2020) · NORA - Nordic Journal of Feminist and Gender Research · doi:10.1080/08038740.2020.1714725 · Cited by 71
Offline contexts of online jobs: Platform drivers, decent work, and informality in Lagos, Nigeria
Cieslik, K., Banya, R., & Vira, B. (2022) · Development Policy Review · doi:10.1111/dpr.12595 · Cited by 62
Ebert, I., Wildhaber, I., & Adams-Prassl, J. (2021) · Big Data & Society · doi:10.1177/20539517211013051 · Cited by 56
Self-citationDiscrimination in the gig economy: the experiences of Black online English teachers
Curran, N. M. (2023) · Language and Education · doi:10.1080/09500782.2021.1981928 · Cited by 41
Metadata
- Authors
- Prassl, J.
- Publisher
- Oxford University PressOxford
- Published
- 2018-04-19
- OpenAlex
- W2801943901
- Cited by
- 441
- References
- 0
- FWCI
- 41.15
- Type
- book
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.