The Two Faces of Algorithmic Management in the Gig Economy

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dc.contributor.author Bujold, Antoine
dc.contributor.author Parent-Rocheleau, Xavier
dc.date.accessioned 2023-12-26T18:46:16Z
dc.date.available 2023-12-26T18:46:16Z
dc.date.issued 2024-01-03
dc.identifier.isbn 978-0-9981331-7-1
dc.identifier.other e0500d87-aa8c-45d3-840f-4e6544153af7
dc.identifier.uri https://hdl.handle.net/10125/107016
dc.language.iso eng
dc.relation.ispartof Proceedings of the 57th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Digitalization of Work
dc.subject algorithmic management
dc.subject gig work
dc.subject job autonomy
dc.subject organization justice.
dc.subject work engagement
dc.title The Two Faces of Algorithmic Management in the Gig Economy
dc.type Conference Paper
dc.type.dcmi Text
dcterms.abstract Algorithmic management of workers is a relatively new phenomenon which impacts workers in diverse manners. The growing literature on this disruptive and technology-mediated form of management suggest that, through different mechanisms, it can result in both beneficial and harmful consequences. Aiming to examine these two faces empirically and simultaneously, time-lagged data was collected from 366 gig workers. The results show that, on the one hand, high perceived exposure to AM is associated to greater perceived procedural justice. On the other hand, workers reporting high AM exposure also perceive lower job autonomy. This has the simultaneous effect of indirectly fostering and worsening the level of gig workers' engagement.
dcterms.extent 9 pages
prism.startingpage 5258
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