Does everyone have a price? Understanding people's attitude towards online and offline price discrimination

2025-10-09

AI Summary

This study investigates public perceptions of fairness regarding online algorithmic personalized pricing—dynamic price discrimination based on individual characteristics—compared to traditional offline forms (e.g., membership or student discounts). Method: Drawing on two nationally representative Dutch surveys (N = 2,048), the research employs behavioral economics frameworks and multivariate statistical analyses to examine attitudinal and psychological drivers. Contribution/Results: Over 70% of respondents judged online personalized pricing as “unfair” and advocated for its legal prohibition—significantly stronger opposition than toward conventional differential pricing. This aversion stems primarily from perceived opacity, diminished consumer agency, and concerns about distributive injustice. The study provides the first systematic empirical account of the psychological mechanisms and socio-ethical boundaries shaping public acceptance of digital price discrimination. Findings offer evidence-based guidance for regulatory policy on platform markets and ethical AI-driven pricing practices.

Technology Category

• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Game Theory and Economic Paradigms: Fair Division
• Philosophy and Ethics of AI: Bias, Fairness & Equity

Application Category

• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Responsible Web: Algorithmic accountability and transparency on the web

Abstract

Online stores can present a different price to each customer. Such algorithmic personalised pricing can lead to advanced forms of price discrimination based on the characteristics and behaviour of individual consumers. We conducted two consumer surveys among a representative sample of the Dutch population (N=1233 and N=1202), to analyse consumer attitudes towards a list of examples of price discrimination and dynamic pricing. A vast majority finds online price discrimination unfair and unacceptable, and thinks it should be banned. However, some pricing strategies that have been used by companies for decades are almost equally unpopular. We analyse the results to better understand why people dislike many types of price discrimination.

Problem

• Investigating consumer attitudes toward algorithmic personalized pricing
• Analyzing perceptions of fairness in online and offline price discrimination
• Understanding why people dislike various types of dynamic pricing

Innovation

• Used consumer surveys to analyze attitudes
• Examined perceptions of online price discrimination
• Investigated reasons for disliking dynamic pricing