Digital advertising platforms have emerged as a widely utilized data source in consumer research, yet the interpretation of such data remains a source of confusion for many researchers. This paper aims to address this issue by offering a comprehensive and accessible review of four prominent data collection methods proposed in the marketing literature: “informal studies,” “multiple-ad studies without holdout,” “single-ad studies with holdout,” and “multiple-ad studies with holdout.” By outlining the strengths and limitations of each method, we aim to enhance understanding regarding the inferences that can and cannot be drawn from the collected data. Furthermore, we present seven recommendations to effectively leverage these tools for programmatic consumer research. These recommendations provide guidance on how to use these tools to obtain causal and non-causal evidence for the effects of marketing interventions, and the associated psychological processes, in a digital environment regulated by targeting algorithms. We also give recommendations for how to describe the testing tools and the data they generate, and urge platforms to be more transparent on how these tools work.

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Diversification—investing in imperfectly correlated assets—reduces volatility without sacrificing expected returns. While the expected return of a diversified portfolio is the weighted average return of its constituent parts, the variance of the portfolio is less than the weighted average variance of its constituent parts. Our results suggest that very few people have correct statistical intuitions about the effects of diversification. The average person in our data sees no benefit of diversification in terms of reducing portfolio volatility. Many people, especially those low in financial literacy, believe diversification actually increases the volatility of a portfolio. These people seem to believe that the unpredictability of individual assets compounds when aggregated together. Additionally, most people believe diversification increases the expected return of a portfolio. Many of these people correctly link diversification with the concept of risk reduction, but seem to understand risk reduction to mean greater returns on average. We show that these beliefs can lead people to construct investment portfolios that mismatch investors’ risk preferences. Further, these beliefs may help explain why many investors are underdiversified. 

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Loss aversion—the idea that losses loom larger than equivalent gains—is one of the most important ideas in Behavioral Economics. In an influential article published in the Journal of Experimental Psychology: General, Walasek and Stewart (2015) test an implication of decision by sampling theory: Loss aversion can disappear, and even reverse, depending on the distribution of gains and losses people have encountered. In this manuscript, we show that the pattern of results reported in Walasek and Stewart (2015) should not be taken as evidence that loss aversion can disappear and reverse, or that decision by sampling is the origin of loss aversion. It emerges because the estimates of loss aversion are computed on different lotteries in different conditions. In other words, the experimental paradigm violates measurement invariance, and is thus invalid. We show that analyzing only the subset of lotteries that are common across conditions eliminates the pattern of results. We note that other recently published articles use similar experimental designs, and we discuss general implications for empirical examinations of utility functions. 

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Consumers incorrectly rely on their sense of understanding of what a company does to evaluate investment risk. In three correlational studies, greater sense of understanding was associated with lower risk ratings (Study 1) and with prediction distributions of future stock performance that had lower standard deviations and higher means (Studies 2 and 3). In all studies, sense of understanding was unassociated with objective risk measures. Risk perceptions increased when the authors degraded sense of understanding by presenting company information in an unstructured versus structured format (Study 4). Sense of understanding also influenced downstream investment decisions. In a portfolio construction task, both novices and seasoned investors allocated more money to hard-to-understand companies for a risk-tolerant client relative to a risk-averse one (Study 5). Study 3 ruled out an alternative explanation based on familiarity. The results may explain both the enduring popularity and common misinterpretation of the “invest in what you know” philosophy. 

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The marketplace is replete with productivity metrics that put units of output in the numerator and one unit of time in the denominator (e.g., megabits per second [Mbps] to measure download speed). In this article, three studies examine how productivity metrics influence consumer decision making. Many consumers have incorrect intuitions about the impact of productivity increases on time savings: they do not sufficiently realize that productivity increases at the high end of the productivity range (e.g., from 40 to 50 Mbps) imply smaller time savings than productivity increases at the low end of the productivity range (e.g., from 10 to 20 Mbps). Consequently, the availability of productivity metrics increases willingness to pay for products and services that offer higher productivity levels. This tendency is smaller when consumers receive additional information about time savings through product experience or through metrics that are linearly related to time savings. Consumers’ intuitions about time savings are also more accurate when they estimate time savings than when they rank them. Estimates are based less on absolute than on proportional changes in productivity (and proportional changes correspond more with actual time savings).

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This research documents a substantial disconnect between the objective quality information that online user ratings actually convey and the extent to which consumers trust them as indicators of objective quality. Analyses of a data set covering 1272 products across 120 vertically differentiated product categories reveal that average user ratings (1) lack convergence with Consumer Reports scores, the most commonly used measure of objective quality in the consumer behavior literature, (2) are often based on insufficient sample sizes which limits their informativeness, (3) do not predict resale prices in the used-product marketplace, and (4) are higher for more expensive products and premium brands, controlling for Consumer Reports scores. However, when forming quality inferences and purchase intentions, consumers heavily weight the average rating compared to other cues for quality like price and the number of ratings. They also fail to moderate their reliance on the average user rating as a function of sample size sufficiency. Consumers’ trust in the average user rating as a cue for objective quality appears to be based on an “illusion of validity.”

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In some product categories, low-priced brands are consistently of low quality, but high-priced brands can be anything from terrible to excellent. In other product categories, high-priced brands are consistently of high quality, but quality of low-priced brands varies widely. Three experiments demonstrate that such heteroscedasticity leads to more extreme price-based quality predictions. This finding suggests that quality inferences do not only stem from what consumers have learned about the average level of quality at different price points through exemplar memory or rule abstraction. Instead, quality predictions are also based on learning about the covariation between price and quality. That is, consumers inappropriately conflate the conditional mean of quality with the predictability of quality. We discuss implications for theories of quantitative cue learning and selective information processing, for pricing strategies and luxury branding, and for our understanding of the emergence and persistence of erroneous beliefs and stereotypes beyond the consumer realm.

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In an increasingly globalized marketplace, it is common for marketing researchers to collect data from respondents who are not native speakers of the language in which the questions are formulated. Examples include online customer ratings and internal marketing initiatives in multinational corporations. This raises the issue of whether providing responses on rating scales in a person's native versus second language exerts a systematic influence on the responses obtained. This article documents the anchor contraction effect (ACE), the systematic tendency to report more intense emotions when answering questions using rating scales in a nonnative language than in the native language. Nine studies (1) establish ACE, test the underlying process, and rule out alternative explanations; (2) examine the generalizability of ACE across a range of situations, measures, and response scale formats; and (3) explore managerially relevant and easily implementable corrective techniques.

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