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Paper Type
ERF
Description
Platforms increasingly rely on AI algorithms to execute automated task allocation, monitoring, and performance evaluation of workers in the gig economy. Platforms also expect AI algorithms can prompt gig workers to provide proactive services to customers for a better reputation. However, it remains unclear how algorithmic management impacts the proactive service behavior of gig workers. Based on self-determination theory, this emergent research forum paper proposes a double-edged sword effect of algorithmic management due to conflicting work motives: algorithmic management can increase gig workers' proactive service behavior via autonomy motivation, whereas it can also inhibit proactive service behavior via controlled motivation. A three-wave longitudinal survey will be conducted to test the posited double-edged sword effect. This study has the potential to contribute to the algorithmic management literature in IS field by uncovering the "black box" between algorithm management and proactive service behavior of gig workers from the perspective of work motivation.
Paper Number
1345
Recommended Citation
Hu, Peng, "A Double-edged Sword? Algorithmic Management and Workers' Proactive Service" (2023). AMCIS 2023 Proceedings. 3.
https://aisel.aisnet.org/amcis2023/sig_cnow/sig_cnow/3
A Double-edged Sword? Algorithmic Management and Workers' Proactive Service
Platforms increasingly rely on AI algorithms to execute automated task allocation, monitoring, and performance evaluation of workers in the gig economy. Platforms also expect AI algorithms can prompt gig workers to provide proactive services to customers for a better reputation. However, it remains unclear how algorithmic management impacts the proactive service behavior of gig workers. Based on self-determination theory, this emergent research forum paper proposes a double-edged sword effect of algorithmic management due to conflicting work motives: algorithmic management can increase gig workers' proactive service behavior via autonomy motivation, whereas it can also inhibit proactive service behavior via controlled motivation. A three-wave longitudinal survey will be conducted to test the posited double-edged sword effect. This study has the potential to contribute to the algorithmic management literature in IS field by uncovering the "black box" between algorithm management and proactive service behavior of gig workers from the perspective of work motivation.
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