Knowledge that Transforms

Firms and other entities provide category-level product attribute information via attribute filters and other tools to aid consumers in filtering, evaluating, comparing, and choosing products. This research examines how displaying this information as a range with or without the distribution of values systematically affects judgments involving attribute value comparisons. Specifically, distributions draw attention away from attribute values, reducing the importance of attribute value differences. With this reduced importance, consumers are less sensitive to attribute value differences; thus, consumers evaluate individual products more positively as they seem more similar to the best available option. Likewise, wider bands of attribute values are selected when filtering product options, as the minimum and maximum values chosen seem less different. Reduced sensitivity to differences also has implications for choices involving tradeoffs between attributes. Importantly, the presence of a distribution itself is the primary driver of this effect, more so than distribution type, as the effect is largely independent of the type of distribution displayed (e.g., normal, bimodal, skewed, uniform). Across six main and seven supplemental experiments, this research highlights a novel consideration for how consumers filter options, form preferences, and choose products. These findings have practical implications and highlight important topics for future research.

With the rise of machine learning and “big data,” many large yet spurious relationships between variables are discovered, leveraged by marketing communications, and publicized in the media. Thus, consumers are increasingly exposed to many large-magnitude relationships between variables that do not signal causal effects. This exposure may carry a substantial cost. Seven studies demonstrate that the magnitudes of relationships between variables can distort consumers’ judgments about whether those relationships reflect causal effects. Specifically, consumers often use a magnitude heuristic: consumers infer that relationships with larger perceived magnitudes are more likely to reflect causal effects, even when this is not true (and even when relationships’ correlations are held constant). In many situations, relying on the magnitude heuristic will distort causality judgments, such as when large-magnitude relationships between variables are spurious, or when normatively extraneous factors (e.g., reference points) distort perceptions of magnitudes. Moreover, magnitude-distorted (mis)perceptions of causality, in turn, distort consumers’ purchase and consumption decisions. Since consumers often encounter spurious relationships with large magnitudes in the health domain and in other consequential domains, the magnitude heuristic is likely to lead to biases in some of consumers’ most important decisions.

Contemporary developments in material duplication promise product alternatives that are physically and sensorially indistinguishable from incumbent offerings. When fully realized, such duplicate offerings should obsolete the incumbents as a consequence of wider availability and lower monetary and social costs. Disruption will be impeded, however, if consumers favor incumbent products on the basis of non-material qualities. The authors show that the influence of such qualities depends on both the product category and characteristics of the consumer. In particular, when a creator is central to the product and when the consumer is inclined toward extraordinary beliefs, the influence of origin looms especially large. By contrasting origin and substance, the present research exposes dualistic thinking in consumers’ product evaluations, enriches prior research on authenticity and extraordinary beliefs, and contributes to the stubborn problem of technology adoption.