Distributions Distract: How Distributions on Attribute Filters and Other Tools Affect Consumer Judgments

Abstract 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.