Can Consumers Learn Price Dispersion? Evidence for Dispersion Spillover Across Categories
André, Quentin, Nicholas Reinholtz, and Bart de Langhe (resubmitted for second round at the Journal of Consumer Research)
- Dispersion knowledge (the perception of the variability of a numerical distribution) is a key antecedent of many judgments and decisions, both mundane (e.g., “should I search for a better price?”) and consequential (e.g., “how much should I have in my emergency fund?”). But how does this knowledge develop from experience? Across seven studies, we document a bias in the formation of this dispersion knowledge: Consumers ascribe more variance to a distribution when it was learned in a high (vs. low) variance environment. We show that this “dispersion spillover” has downstream consequences on judgments of price attractiveness, and on consumers’ decision to search for better options.
Restricted-Use Funds and Budgeting Decisions
André, Quentin, Nicholas Reinholtz, and John G. Lynch Jr. (second round at the Journal of Consumer Research)
- How do consumers budget when a part of their income is denominated in a category-restricted resource (e.g., food stamps)? Building on the mental accounting and categorization literature, we hypothesized that people endowed with a category-restricted resource would be averse to spending their unrestricted money on products of this category. To test this hypothesis, I developed a multi-round, interactive budgeting simulation. We find that people endowed with a food-restricted resource end up budgeting less on food than people who received an equivalent amount in unrestricted money.
Slop(p)y Causal Inferences: How Market Trends Foster an Illusory Sense of Learning and Understanding
André, Quentin and Bart de Langhe (three studies collected, first draft in preparation)
- We demonstrate that the outcome bias (people’s tendency to infer the quality of a decision from its outcome) can exacerbate illusory correlations. To do so, I designed and programmed a simulation in which people make repeated investment decisions, and are asked to learn the characteristics that are associated with higher returns. Unbeknownst to them, the payoffs are independent of their choices, and are manipulated to be increasing (vs. decreasing or flat) over time. We show that this manipulation of slope significantly increases people’s confidence in how much they have learnt, and in their ability to predict future outcomes.
No Evidence for Loss Aversion Disappearance and Reversal in Walasek and Stewart (2015)
André, Quentin and Bart de Langhe (under review at the Journal of Experimental Psychology: General)
- In an influential article published in the Journal of Experimental Psychology: General, Walasek and Stewart (2015) have proposed that decision by sampling is the origin of loss aversion. They report that people are loss averse, loss neutral, or loss seeking, depending on the distribution of gains and losses that they have encountered. We rely on multiple methods (an analytical proof, simulated choices, and a re-analysis of the original data) to show that diminishing sensitivity to gains and losses alone can cause the apparent changes in loss aversion. After accounting for diminishing sensitivity, we do not find evidence for the notion that decision by sampling drives loss aversion. We discuss general implications for empirical examinations of loss aversion.
Healthy Through Presence or Absence, Nature or Science? A Framework for Understanding Front-Of-Package Food Claims
- We propose that food products claim to be healthy in one of four ways: because they have removed negative properties (e.g., "low fat"), added positive properties (e.g., "high antioxydants), preserved positive properties (e.g., "unprocessed") or not added negative properties (e.g., "No additives"). We show that while consumers make different inferences from those different types of claim, foods bearing different types of claims do not differ in their nutritional properties.
Consumer Choice and Autonomy in the Age of Artificial Intelligence and Big Data
- We explore the opportunities and challenges that the development of automation and artificial intelligence pose to consumers. They can, on the one hand, contribute to consumer well-being by making choices easier, more practical, and more efficient. On the other hand, they can also undermine consumers' sense of autonomy, the absence of which is detrimental to well-being. Drawing from diverse perspective, we explore the relevance and importance of autonomy to consumers, and identify open research questions in this domain.