Title: Visual Distortion Bias in Consumer Choices
Author: Tao Lu, Alex Wang, May Yuan and Xiaoquan (Michael) Zhang
Existing research on word-of-mouth considers various descriptive statistics of rating distributions, such as the mean, variance, skewness, kurtosis, and even entropy and the Herfindahl-Hirschman Index. But real-world consumer decisions are often derived from visual perceptions about displayed rating distributions in the form of histograms. In this study, we argue that such distribution charts may inadvertently lead to a consumer-choice bias that we call the Visual Distortion Bias (VDB). We propose that consumers have a tendency to be misled by salient features of distributions in visual decision-making. In an illustrative model, we derive a measure of the VDB. In a series of experiments, we identify the VDB's significant impact on consumer choices. We show that with the VDB, consumers may make choices that violate widely accepted decision rules. In our experiments, subjects are observed to prefer products with lower average ratings. They violate widely accepted modeling assumptions, such as branch independence and first-order stochastic dominance.