Investigating engagement and burnout of gig-workers in the age of algorithms

An empirical study in digital labor platforms

Bibliographic Data

ID21596293
AuthorsNastaran Hajiheydari (0000-0003-3663-5254, University of Sheffield), Mohammad Soltani Delgosha (0000-0003-1718-8110, University of Birmingham)
Year2024
Volume37
Issue7
Pages2489-2522
Publication date2024-12-03
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueInformation Technology and People (JOURNAL)
Journal identifiersISSN: 0959-3845 • E-ISSN: 1758-5813
PublisherEmerald (PUBLISHER)
DOI10.1108/itp-11-2022-0873
OpenAlexW4392237683
LanguageEN
Citations received17
References cited105

Purpose Digital labor platforms (DLPs) are transforming the nature of the work for an increasing number of workers, especially through extensively employing automated algorithms for performing managerial functions. In this novel working setting – characterized by algorithmic governance, and automatic matching, rewarding and punishing mechanisms – gig-workers play an essential role in providing on-demand services for final customers. Since gig-workers’ continued participation is crucial for sustainable service delivery in platform contexts, this study aims to identify and examine the antecedents of their working outcomes, including burnout and engagement. Design/methodology/approach We suggested a theoretical framework, grounded in the job demands-resources heuristic model to investigate how the interplay of job demands and resources, resulting from working in DLPs, explains gig-workers’ engagement and burnout. We further empirically tested the proposed model to understand how DLPs' working conditions, in particular their algorithmic management, impact gig-working outcomes. Findings Our findings indicate that job resources – algorithmic compensation, work autonomy and information sharing– have significant positive effects on gig-workers’ engagement. Furthermore, our results demonstrate that job insecurity, unsupportive algorithmic interaction (UAI) and algorithmic injustice significantly contribute to gig-workers’ burnout. Notably, we found that job resources substantially, but differently, moderate the relationship between job demands and gig-workers’ burnout. Originality/value This study contributes a theoretically accurate and empirically grounded understanding of two clusters of conditions – job demands and resources– as a result of algorithmic management practice in DLPs. We developed nuanced insights into how such conditions are evaluated by gig-workers and shape their engagement or burnout in DLP emerging work settings. We further uncovered that in gig-working context, resources do not similarly buffer against the negative effects of job demands

Algorithm · Burnout · Empirical research · Statistics · Clinical Psychology · Computer Science · Digital Economy and Work Transformation · Employment and Welfare Studies · Mathematics · Psychology · Sharing Economy and Platforms

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