Bart de Langhe

The Anchor Contraction Effect in International Marketing Research

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 persons 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.

Productivity Metrics and Consumers’ Misunderstanding of Time Savings

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).

Fooled by Heteroscedastic Randomness: Local Consistency Breeds Extremity in Price-Based Quality Inferences

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.

Bang for the Buck: Gain-Loss Ratio as a Driver of Judgment and Choice

Prominent decision-making theories propose that individuals (should) evaluate alternatives by combining gains and losses in an additive way. Instead, we suggest that individuals seek to maximize the rate of exchange between positive and negative outcomes and thus combine gains and losses in a multiplicative way. Sensitivity to gain-loss ratio provides an alternative account for several existing findings and implies a number of novel predictions. It implies greater sensitivity to losses and risk aversion when expected value is positive, but greater sensitivity to gains and risk seeking when expected value is negative. It also implies more extreme preferences when expected value is positive than when expected value is negative. These predictions are independent of decreasing marginal sensitivity, loss aversion, and probability weighting—three key properties of prospect theory. Five new experiments and reanalyses of two recently published studies support these predictions. This paper was accepted by Yuval Ro...

Circle of Incompetence: Sense of Understanding as an Improper Guide to Investment Risk

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.

The Marketing Manager as an Intuitive Statistician

Business decisions are increasingly based on data and statistical analyses. Managerial intuition plays an important role at various stages of the analytics process. It is thus important to understand how managers intuitively think about data and statistics. This article reviews a wide range of empirical results from almost a century of research on intuitive statistics. The results support four key insights: (1) Variance is not intuitive; (2) Perfect correlation is the intuitive reference point; (3) People conflate correlation with slope; and (4) Nonlinear functions and interaction effects are not intuitive. These insights have implications for the development, implementation, and evaluation of statistical models in marketing and beyond. I provide several such examples and offer suggestions for future research.