Algorithms of (un)fairness – Is personalized pricing fair game or foul play?

Abstract

Personalized pricing is a type of algorithmic pricing based on data analytics which allows price-setters to automatically generate individualized prices in real-time. While this price-setting technique enables companies to skim consumer surplus, it may often trigger reactance on the part of consumers who perceive such 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 perceived price fairness, regardless of whether customers personally benefit from this approach or not, since the individualized prices violate customers’ social norms. This negative impact is rooted in a cognitive factor – suspicion – and emotional factors – negative moral emotions and lack of gratitude. The findings imply that a company needs to consider the opportunity costs of personalized pricing. These costs arise from customers perceiving this pricing as unfair, thus potentially negatively impacting the relationship between the customer and the brand.