Histogram Distortion Bias in Consumer Choices - Lu, Tao; Yuan, May; Wang, Chong (Alex); Zhang, Xiaoquan (Michael) (Management Science, 2022)
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 assessment of 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 histogram distortion bias (HDB). We propose that salient features of distributions in visual decision making may mislead consumers and result in inferior decision making. In an illustrative model, we derive a measure of the HDB. We show that with the HDB, consumers may make choices that violate well-accepted decision rules. In a series of experiments, subjects are observed to prefer products with a higher HDB despite a lower average rating. They could also violate widely accepted modeling assumptions, such as branch independence and first-order stochastic dominance. This paper was accepted by Chris Forman, information systems. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72131001 and 92146003] and the Research Grants Council, University Grants Committee [GRF 14500521, GRF 14501320, GRF 14503818, and TRS:T31-604/18-N]. Supplemental Material: Data and the online appendices are available at https://doi.org/10.1287/mnsc.2022.4306 .