DYNAMIC PRICING ALGORITHMS AND CONSUMER FAIRNESS PERCEPTIONS: EXPERIMENTAL EVIDENCE FROM RIDE-HAILING APPS

Abstract

Dynamic pricing — when prices change in real time based on supply and demand — has become a staple of digital marketplaces, and once you have it, other things suddenly seem possible. It’s integral to the way ride-hailing works now through apps like Uber and Lyft. While such algorithms to maximize efficiency and profit, make worries concerning customer fairness perceptions, trust, and acceptance. Current research shows that there is an ambivalence among customers towards algorithmic pricing which encounters both acceptance of the system as efficient and the denial accordingly considering it to be exploitative. This research empirically assesses customer perceptions of fairness in dynamic pricing situations using mixed method experiments. Leveraging survey experiments on 2,000 ride-hailing consumers and in-depth interviews with 40 individuals, we investigate how three transparency mechanisms (surge frequency, algorithmic framing, transparency) shape consumer fairness evaluations. Our quantitative results show that the transparency of algorithmic operation decreases negative perceptions of fairness, but our qualitative data also provide insights into how consumers perceive concerns about distributive justice (cf. Fehr & Schwarze, 1999). Results indicate that despite consumer acceptance of dynamic pricing in principle, opaque implementation generates distrust. Findings contribute to the emerging literature on algorithmic governance and consumers’ behavior, lending a theoretical support as well as policy implications. Policymakers and platforms will need to weigh such efficiency gains against desirability of fairness disclosure, in order to maintain consumers’ confidence.

Keywords: dynamic pricing, fairness perceptions, ride-hailing apps, algorithmic governance, consumer trust, behavioral experiments