I lose vs. I earn: Consumer perceived price fairness toward algorithmic (vs. human) price discrimination

Many companies are turning to algorithms to determine prices. However, little research has been done to investigate consumers’ perceived price fairness when price discrimination is implemented by either a human or an algorithm. The results of two experiments with 2 (price-setting agent: algorithm vs. human) × 2 (price discrimination: advantaged vs. disadvantaged) between-subjects design reveal that consumers perceive disadvantaged price discrimination as being more unfair when it is implemented by a human (vs. algorithm). Conversely, they perceive advantaged price discrimination as being more unfair when it is implemented by an algorithm (vs. human). This difference is caused by distinct attribution processes. Consumers are more likely to externalize disadvantaged price discrimination implemented by a human than an algorithm (i.e., attributing it to the unintentionality of price-setting agents), while they are more likely to internalize advantaged price discrimination implemented by a human than an algorithm (i.e., attributing it to perceived personal luck). Based on these findings, we discuss how designers and managers can design and utilize algorithms to implement price discrimination that reduces consumer perception of price unfairness. We believe that reasonable disclosure of algorithmic clues to consumers can maximize the benefits of price discrimination strategies.