Playing with Fire – How Personalized Pricing triggers Perceived Price Unfairness
(A2024-117735)

AUTHORS

Martin Ohlwein, International School of Management; Pascal Bruno, International School of Management

ABSTRACT

Personalized pricing is a type of algorithmic pricing based on data analytics which allows to automatically generate individualized prices in realtime. While it enables companies to skim consumer surplus, it often triggers reactance on the part of consumers who perceive this pricing as unfair. This study analyzes the impact of personalized pricing on price fairness and uncovers underlying cognitive and emotional mediators. The results reveal that personalized pricing reduces price fairness, regardless of whether customers personally benefit from this approach or not, since it violates social norms. This negative impact is grounded in a cognitive factor – suspicion – and emotional factors – negative moral emotions and lack of gratitude. The findings imply that a company should consider the opportunity costs of personalized pricing that arise from customers perceiving this pricing to be unfair, thus potentially negatively impacting the relationship between the customer and the brand.