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Paper Type
Completed
Paper Number
1193
Description
This study examines how the use of algorithmic controls embedded in gig economy platforms impacts worker well-being and behavior. We draw on the information systems (IS) control and technostress literatures to explore how different modes of algorithmic control correspond with (positive) challenge technostressors and (negative) hindrance technostressors experienced by gig workers. We also consider the technostress outcomes, in terms of continuance intentions and workaround use. Using a survey of 621 US-based Uber drivers, we find that algorithmic input controls positively relate to hindrance technostressors, but that algorithmic behavior and output controls positively relate to challenge technostressors. The study bridges the IS control and technostress literatures by conceptualizing algorithmic control modes as work demands that put gig workers under stress. This stress can have important downstream effects on worker behavior, which can impact the overall gig economy platform in the event that workers discontinue their work or increase their workaround use.
Recommended Citation
Cram, W. Alec; Wiener, Martin; Tarafdar, Monideepa; and Benlian, Alexander, "Algorithmic Controls and their Implications for Gig Worker Well-being and Behavior" (2020). ICIS 2020 Proceedings. 1.
https://aisel.aisnet.org/icis2020/is_workplace_fow/is_workplace_fow/1
Algorithmic Controls and their Implications for Gig Worker Well-being and Behavior
This study examines how the use of algorithmic controls embedded in gig economy platforms impacts worker well-being and behavior. We draw on the information systems (IS) control and technostress literatures to explore how different modes of algorithmic control correspond with (positive) challenge technostressors and (negative) hindrance technostressors experienced by gig workers. We also consider the technostress outcomes, in terms of continuance intentions and workaround use. Using a survey of 621 US-based Uber drivers, we find that algorithmic input controls positively relate to hindrance technostressors, but that algorithmic behavior and output controls positively relate to challenge technostressors. The study bridges the IS control and technostress literatures by conceptualizing algorithmic control modes as work demands that put gig workers under stress. This stress can have important downstream effects on worker behavior, which can impact the overall gig economy platform in the event that workers discontinue their work or increase their workaround use.
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