Sie befinden Sich nicht im Netzwerk der Universität Paderborn. Der Zugriff auf elektronische Ressourcen ist gegebenenfalls nur via VPN oder Shibboleth (DFN-AAI) möglich. mehr Informationen...
Gig worker organizing: toward an adapted Attraction-Selection-Attrition framework
Ist Teil von
Journal of managerial psychology, 2023-01, Vol.38 (1), p.47-59
Ort / Verlag
Bradford: Emerald Publishing Limited
Erscheinungsjahr
2023
Quelle
Applied Social Sciences Index & Abstracts (ASSIA)
Beschreibungen/Notizen
PurposeAs conventional practices of working continue to be modified in the gig economy, more theoretical work examining the experiences of gig workers is needed. Relying on person-based fit and levels of analysis literature, this paper proposes an adaptation to the traditional Attraction-Selection-Attrition (ASA) framework to the gig economy.Design/methodology/approachDrawing on the ASA framework, this conceptual paper explores how gig workers join, leave and could be retained by gig employers.FindingsThe authors recognize an intermediary “organizing” phase within the ASA framework for gig workers. Using examples of appwork and crowdwork, the authors show that workers tend to self-organize through third-party websites to help gig work become economically sustainable, avoid being exploited and enhance gig workers' sense of community and identity.Practical implicationsThe practical implications of this research lie in gig employers understanding how workers experience gig employment and in helping employers be successful in attracting, selecting and retaining quality workers and thereby lowering permanent attrition.Originality/valueThe authors propose a novel adaptation to the conventional ASA framework to include organizing as a phase in gig worker employment. This research defines gig attraction and attrition at the individual-level, selection at the individual- and task-levels based in person-job (PJ)-fit and the various aspects of gig organizing as encompassing fit with one's job, organization, and environmental (i.e., PJ-, PO-, PE-fit) at the individual-, task-, and network-levels.