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

Kate  Barasz; Tami  Kim; Ioannis  Evangelidis

Article | Cognition | July 2019

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

by Kate Barasz, Tami Kim and Ioannis Evangelidis

Abstract

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;

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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.

From “Chick Beer” to “Dryer sheets for Men,” identity-based labeling is frequently deployed to appeal to people who hold the targeted identity. However, five studies demonstrate that identity appeals can backfire, alienating the very individuals they aim to attract. We begin by demonstrating backlash against identity appeals in the field during the 2016 presidential election (Study 1) and in the lab (Study 2). This (in)effectiveness of identity appeals is driven by categorization threat—feeling unwillingly reduced to a single identity—which is induced when a) the identity deployed is that of a typically marginalized group (Studies 3-4) and b) the appeal evokes a stereotype about that identity (Study 5). Ironically, identity appeals often drive identity-holders away from options they would have preferred in the absence of that appeal.

Article | Journal of Consumer Research

Why Am I Seeing this Ad? The Effect of Ad Transparency on Ad Effectiveness

Tami Kim, Kate Barasz and Leslie K. John

Given the increasingly specific ways marketers can target ads, many consumers and regulators are demanding ad transparency: disclosure of how consumers’ personal information was used to generate ads. We investigate how and why ad transparency impacts ad effectiveness. Drawing on literature about offline norms of information-sharing, we posit that ad transparency backfires when it exposes marketing practices that violate norms about “information flows”—consumers’ beliefs about how their information ought to move between parties. Study 1 inductively shows that consumers deem information flows acceptable (or not) based on whether their personal information was: 1) obtained within versus outside of the website on which the ad appears and 2) stated by the consumer versus inferred by the firm (the latter of each pair being less acceptable). Studies 2 and 3 show that revealing unacceptable information flows reduces ad effectiveness, which is driven by increasing consumers’ relative concern for their privacy over desire for the personalization that such targeting affords. Study 4 shows the moderating role of platform trust: when consumers trust a platform, revealing acceptable information flows increases ad effectiveness. Studies 5a and 5b, conducted in the field with a loyalty program website (i.e., a trusted platform), demonstrate this benefit of transparency.

Keywords: Online Advertising; Customization and Personalization; Information; Trust; Performance Effectiveness;

Citation:

Kim, Tami, Kate Barasz, and Leslie K. John. "Why Am I Seeing this Ad? The Effect of Ad Transparency on Ad Effectiveness." Journal of Consumer Research 45, no. 5 (February 2019): 906–932. View Details

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Over the last decade, healthcare in many parts of the world has shifted toward a more patient-centric, consumeristic model, marked by an emphasis on choice and a proliferation of typical consumer-facing information (e.g., price and quality data). However, while the "patients as consumers" perspective is an apt one, there are crucial differences between healthcare and typical consumer domains that warrant special consideration by policymakers and researchers alike. This article discusses some of these differences and explores the challenges that consumers (a.k.a. patients) face when making tradeoffs between price and quality.

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

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