Paper Number
ICIS2025-1083
Paper Type
Complete
Abstract
Algorithmic rewards on gig platforms are expected to motivate workers to sustain their episodic loyalty and stay on the platform. However, when not properly designed, algorithmic rewards can have negative effects on workers' continuance intention on the platform. We build upon self-determination theory’s propositions about two fundamental psychological needs – autonomy and competence – and investigate the dual impact of algorithmic rewards on episodic loyalty. In a 2×2×2 online experiment, two types of bonuses (cash and priority) and the reward achievability were manipulated. We find that cash and priority bonuses with a reasonable target have positive effects on workers’ psychological needs. Hard-to-achieve target, however, renders these bonuses ineffective and frustrate workers’ needs. These needs are important drivers of episodic loyalty. This paper contributes to the discourse on algorithmic reward effects, an important component of algorithmic control but usually understudied in the literature, and informs the better design of algorithmic rewards.
Recommended Citation
Huang, Xuemei and Nguyen, Long The, "Beyond Reach: Algorithmic Rewards’ Effects on Gig Workers’ Episodic Loyalty" (2025). ICIS 2025 Proceedings. 3.
https://aisel.aisnet.org/icis2025/hti/hti/3
Beyond Reach: Algorithmic Rewards’ Effects on Gig Workers’ Episodic Loyalty
Algorithmic rewards on gig platforms are expected to motivate workers to sustain their episodic loyalty and stay on the platform. However, when not properly designed, algorithmic rewards can have negative effects on workers' continuance intention on the platform. We build upon self-determination theory’s propositions about two fundamental psychological needs – autonomy and competence – and investigate the dual impact of algorithmic rewards on episodic loyalty. In a 2×2×2 online experiment, two types of bonuses (cash and priority) and the reward achievability were manipulated. We find that cash and priority bonuses with a reasonable target have positive effects on workers’ psychological needs. Hard-to-achieve target, however, renders these bonuses ineffective and frustrate workers’ needs. These needs are important drivers of episodic loyalty. This paper contributes to the discourse on algorithmic reward effects, an important component of algorithmic control but usually understudied in the literature, and informs the better design of algorithmic rewards.
When commenting on articles, please be friendly, welcoming, respectful and abide by the AIS eLibrary Discussion Thread Code of Conduct posted here.
Comments
15-Interaction