Suchergebnisse

Abstract
In de Langhe, Fernbach, and Lichtenstein (2016), we argue that consumers trust average user ratings as indicators of objective product performance much more than they should. This simple idea has provoked passionate commentaries from eminent researchers across three subdisciplines of marketing: experimental consumer research, modeling, and qualitative consumer research. Simonson challenges the premise of our research, asking whether objective performance even matters. We think it does and explain why in our response. Winer and Fader argue that our results are neither insightful nor important. We believe that their reaction is due to a fundamental misunderstanding of our goals, and we show that their criticisms do not hold up to scrutiny. Finally, Kozinets points out how narrow a slice of consumer experience our article covers. We agree, and build on his observations to reflect on some big-picture issues about the nature of research and the interaction between the subdisciplines.

Abstract
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."

AbstractPrice knowledge is a key antecedent of many consumer judgments and decisions. This article examines consumers' ability to form accurate beliefs about the minimum, the maximum, and the overall variability of prices for multiple product categories. Eight experiments provide evidence for a novel phenomenon we call dispersion spillover: Consumers tend to overestimate price dispersion in a category after encountering another category in which prices are more dispersed (vs. equally or less dispersed). Our experiments show that this dispersion spillover is consequential: It influences the likelihood that consumers will search for (and find) better prices and offers, and how much consumers bid in auctions. Finally, we disentangle two cognitive processes that might underlie dispersion spillover. Our results suggest that judgments of dispersion are not only based on specific prices stored in memory and that dispersion spillover does not simply reflect the inappropriate activation of prices from other categories. Instead, it appears that consumers also form "intuitive statistics" of dispersion: Summary representations that encode the dispersion of prices in the environment but that are insufficiently category specific.

Abstract
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 article 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.

Abstract
Companies can create value by differentiating their products and services along quantitative attributes. Existing research suggests that consumers' tendency to rely on relatively effortless and affect-based processes reduces their sensitivity to the scope of quantitative attributes and that this explains why increments along quantitative attributes often have diminishing marginal value. The current article sheds new light on how "system 1" processes moderate the effect of quantitative product attributes on subjective value. Seven studies provide evidence that system 1 processes can produce diminishing marginal value, but also increasing marginal value, or any combination of the two, depending on the composition of the choice set. This is because system 1 processes facilitate ordinal comparisons (e.g., 256 GB is more than 128 GB, which is more than 64 GB) while system 2 processes, which are relatively more effortful and calculation based, facilitate cardinal comparisons (e.g., the difference between 256 and 128 GB is twice as large as between 128 and 64 GB).