Fair Dealings with Algorithms? Analyzing the Perceived Procedural Fairness of Managerial Algorithms and their Impacts on Gig-Workers

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dc.contributor.authorJabagi, Nura
dc.contributor.authorCroteau, Anne-Marie
dc.contributor.authorAudebrand, Luc
dc.contributor.authorMarsan, Josianne
dc.date.accessioned2023-12-26T18:43:21Z
dc.date.available2023-12-26T18:43:21Z
dc.date.issued2024-01-03
dc.identifier.doihttps://doi.org/10.24251/HICSS.2024.505
dc.identifier.isbn978-0-9981331-7-1
dc.identifier.other3abed532-d1ee-4cc6-a96f-dd2581e7c3d0
dc.identifier.urihttps://hdl.handle.net/10125/106889
dc.language.isoeng
dc.relation.ispartofProceedings of the 57th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectCrowdsourcing and Digital Workforce in the Gig Economy
dc.subjectalgorithmic fairness
dc.subjectalgorithmic management
dc.subjectgig economy
dc.subjectjob satisfaction
dc.subjectplatform work
dc.titleFair Dealings with Algorithms? Analyzing the Perceived Procedural Fairness of Managerial Algorithms and their Impacts on Gig-Workers
dc.typeConference Paper
dc.type.dcmiText
dcterms.abstractThis study examines how gig-workers perceive the fairness of managerial algorithms on gig-work platforms using Organizational Justice Theory. Through a survey of 435 Uber drivers, we find that the perceived fairness of algorithmic decisions (both matching and performance evaluation decisions) is positively and significantly related to job satisfaction and perceived organizational support (POS). We also find that certain indicators of perceived algorithmic fairness are unique to the type of decision made and whether it is perceived to require mechanical or human skills. In answering calls to study the impacts of algorithmic fairness in real-world settings, we find that managerial algorithms play a key role in shaping gig-workers’ attitudes as technological artefacts and as organizational agents. Recommendations are provided to enhance perceived algorithmic fairness to address challenges in the gig-economy, like high turnover, by increasing satisfaction and POS.
dcterms.extent10 pages
prism.startingpage4194

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