NEW YORK, NY – Amid ongoing debates about gender-based pricing disparities, known as "pink taxes," new research from Columbia Business School Professor Vicki Morwitz, reveals that consumers often feel exploited by pricing variations based on demographics such as age and gender. However, they exhibit greater tolerance when prices are set by algorithms. This research suggests that algorithmic pricing may offer a more accepted alternative to traditional human-set pricing.
The paper, titled “Demographic Pricing in the Digital Age: Assessing Fairness Perceptions in Algorithmic versus Human-Based Price Discrimination,” co-authored by Morwitz, the Bruce Greenwald Professor of Business in the Marketing Division, University of Southern California’s Professor Nofar Duani, and University of Colorado’s Professor Alixandra Barasch, finds that algorithms are perceived as rules-based and impersonal. This perception helps mitigate feelings of discrimination, leading consumers to view algorithmically determined prices as fairer than those set by humans. The study involved participants recruited from online platforms like Amazon Mechanical Turk and Prolific, with sample sizes ranging from 276 to 469 across four studies. Participants evaluated scenarios related to purchasing insurance policies, live show tickets, or retail and hotel services, with prices varying based on personal characteristics or time factors. Participants were informed that a human sales manager or an algorithm determined the prices in some conditions. Researchers measured purchase intentions, perceptions of fairness, and feelings of being judged or exploited. Results showed that participants perceived algorithmic pricing pricing to be more fair and had greater purchase intentions when it involved demographic factors. For instance, in Study 1, participants were more likely to purchase insurance when algorithms set the prices based on demographics, perceiving both the prices and the process as fairer. Study 2 reinforced this finding, demonstrating an increased willingness to buy items with algorithmically set prices based on demographics, although this preference diminished for time-based pricing. Study 3 introduced a prosocial goal—where prices aimed to benefit a community or cause—which softened the preference for algorithmic pricing, yet participants still favored algorithms in neutral contexts.
Professor Morwitz’s research presents a shift from traditional views on managing consumer reactions to pricing, suggesting that algorithms can help mitigate backlash, especially regarding sensitive demographic pricing. However, as trust in algorithmic decisions grows, oversight becomes crucial. With algorithmic pricing likely to persist, regulators and companies must collaboratively explore how data-driven pricing models can promote fairness in an increasingly volatile market.
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