Contextual Price Effects: How Other Prices Shape Consumer Decisions
(A2026-133344)

AUTHORS

GILLES LAURENT, ESSEC Business School; Arnaud Monnier, Nova School of Business and Economics - Universidade Nova de Lisboa; Laurence Ashworth, Smith School of Business, Queen’s University; Diego Aparicio, IESE; Rajesh Bagchi, Virginia Tech; Yonatan Vanunu, Coller School of Management and Sagol School of Neuroscience, Tel Aviv University/Booth School of Business, University of Chicago

ABSTRACT

These four papers have a common focus: when consumer decisions about a posted price also depend on other prices. Ashworth et al. analyze the impact of another price that is now irrelevant because it is no longer available. Aparicio et al. examine the consequences of retailers’ algorithmic pricing strategies on price sensitivity. Bagchi and colleagues investigate the effect of the position in which a price is presented among other prices in an assortment. Vanunu et al. reveal how consumers may prefer larger packs even when their unit price is larger than for smaller formats. In “Can Irrelevant Prices Matter?” Laurence Ashworth (with Nicole Robitaille and Zheng Xu) examines the effect of lower, but completely irrelevant, price comparisons on consumers’ purchase intentions (e.g., a lower price available only with the purchase of a much more expensive item). Three experiments find that irrelevant price comparisons reduce purchase intentions and that these effects cannot be explained by changes in reference prices or expectations about future prices. Instead, the authors find that consumers avoid purchasing to protect self-competence perceptions. In “Algorithmic Pricing and Price Variability,” Diego Aparicio (with Dan Eckles, Madhav Kumar, and Catherine Tucker) shows that algorithmic pricing induces an unintended behavioral “boomerang effect”: by exposing consumers to frequent price changes, firms inadvertently make consumers more attentive sensitive to price and more responsive to small fluctuations. As a result, some of the theoretical profit gains from algorithmic pricing may be offset by induced shifts in demand elasticity. This has direct implications for firms adopting AI-driven pricing systems and for policymakers. In “Out of Order: When Random Price Presentation Enhances Product Assortment Evaluations,” Rajesh Bagchi (presenting work with Jiaqi Cao, Julio Sevilla, Mathew Isaac) shows that random (vs. monotonically ordered) price displays can enhance consumers’ holistic evaluations of the prices in an assortment and increase overall purchase incidence. This effect occurs as random ordering expands consumers’ perceived price range via increased price entropy, heightening consumers’ confidence that they have encountered a sufficiently representative set of options to make a purchase. The effect holds when consumers pick price presentation order, and despite their tendency to select ascending order. In “The Quantity Discount Fallacy,” Yonatan Vanunu (with Grace Zhang, and Oleg Urminsky), through six-pre-registered studies, provides strong evidence of a preference for larger packages—even when their unit price is higher and clearly communicated, and even when convenience in bulk buying is controlled for. This tendency likely stems from a generalized belief that larger packages offer better value for money. Accordingly, the effect is modulated by the proportion of products in the store that contradict this belief.