The Role of (Dis)similarity in (Mis)predicting Others' Preferences

Kate  Barasz; Tami  Kim; Leslie K. John

Article | Journal of Marketing Research (JMR) | August 2016

The Role of (Dis)similarity in (Mis)predicting Others' Preferences

by Kate Barasz, Tami Kim and Leslie K. John

Abstract

Consumers readily indicate liking options that appear dissimilar—for example, enjoying both rustic lake vacations and chic city vacations or liking both scholarly documentary films and action-packed thrillers. However, when predicting other consumers’ tastes for the same items, people believe that a preference for one precludes enjoyment of the dissimilar other. Five studies show that people sensibly expect others to like similar products but erroneously expect others to dislike dissimilar ones (Studies 1 and 2). While people readily select dissimilar items for themselves (particularly if the dissimilar item is of higher quality than a similar one), they fail to predict this choice for others (Studies 3 and 4)—even when monetary rewards are at stake (Study 3). The tendency to infer dislike from dissimilarity is driven by a belief that others have a narrow and homogeneous range of preferences (Study 5).

Keywords: perceived similarity; prediction error; preference prediction; self-other difference; social inference; Cognition and Thinking; Perception; Forecasting and Prediction;

Format: Print

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Citation:

Barasz, Kate, Tami Kim, and Leslie K. John. "The Role of (Dis)similarity in (Mis)predicting Others' Preferences." Journal of Marketing Research (JMR) 53, no. 4 (August 2016): 597–607.

The case describes a program that CVS Health recently implemented to improve medication adherence, an important problem from a societal, public policy, and firm perspective. A test of the program, costing hundreds of thousands of dollars to implement, increased the proportion of adherent customers by 1.4 percentage points. Students are asked to quantify the system-wide economic benefit of this improvement and draw upon insights from behavioral science to examine approaches for boosting medication adherence.

Email mking@hbs.edu for a courtesy copy.

By the time Dan Doctoroff, CEO of Sidewalk Labs, began hosting a Reddit “Ask Me Anything” session in January 2018, he had only nine months remaining to convince the people of Toronto, their government representatives, and presumably his parent company Alphabet, Inc., that Sidewalk Labs’ plan to construct “the first truly 21st-century city” on the Canadian city’s waterfront was a sound one. Along with much excitement and optimism, strains of concern had emerged since Doctoroff and partners first announced their intentions for a city “built from the internet up” in Toronto’s Quayside district. As Doctoroff prepared for yet another milestone in a year of planning and community engagement, it was almost certain that of the many questions headed his way, digital privacy would be among them.

Article | Cognition | July 2019

I Know Why You Voted for Trump: (Over)inferring Motives Based on Choice

Kate Barasz, Tami Kim and Ioannis Evangelidis

People often speculate about why others make the choices they do. This paper investigates how such inferences are formed as a function of what is chosen. Specifically, when observers encounter someone else's choice (e.g., of political candidate), they use the chosen option's attribute values (e.g., a candidate's specific stance on a policy issue) to infer the importance of that attribute (e.g., the policy issue) to the decision-maker. Consequently, when a chosen option has an attribute whose value is extreme (e.g., an extreme policy stance), observers infer—sometimes incorrectly—that this attribute disproportionately motivated the decision-maker's choice. Seven studies demonstrate how observers use an attribute's value to infer its weight—the value-weight heuristic—and identify the role of perceived diagnosticity: more extreme attribute values give observers the subjective sense that they know more about a decision-maker's preferences and, in turn, increase the attribute's perceived importance. The paper explores how this heuristic can produce erroneous inferences and influence broader beliefs about decision-makers.

Keywords: self-other difference; social perception; inference-making; preferences; consumer behavior; prediction; prediction error; Decision Choices and Conditions; Perception; Behavior; Forecasting and Prediction;

Citation:

Barasz, Kate, Tami Kim, and Ioannis Evangelidis. "I Know Why You Voted for Trump: (Over)inferring Motives Based on Choice." Special Issue on The Cognitive Science of Political Thought. Cognition 188 (July 2019): 85–97. View Details

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The Role of (Dis)similarity in (Mis)predicting Others' Preferences

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