In the era of big data, the utilization of algorithms for dynamic pricing has become prevalent. However, concerns have been raised about the potential negative impact of these practices on consumers' fairness perceptions. Using attribution theory as the underlying framework, we explore how AI disclosure moderates the relationship between AI pricing type (unified/personalized dynamic pricing) and fairness perceptions (procedural/distributive fairness) and how this moderation effect is further moderated by the perceived typicality of AI pricing. An online scenario-based experiment was carried out with 145 participants. The results reveal that personalized dynamic pricing elicits lower fairness perceptions than unified dynamic pricing. Furthermore, we observe a significant moderated moderation effect, indicating that the negative impact of personalized dynamic pricing can be mitigated by AI disclosure for consumers who perceive AI pricing as typical. These findings contribute to AI pricing literature and the development of fairer platform designs.
Peng, Xiao; Peng, Xixian; and Xu, David (Jingjun), "Algorithmic Pricing and Fairness: A Moderated Moderation Model of AI Disclosure and Typicality of AI Pricing" (2023). PACIS 2023 Proceedings. 116.
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