Decision Making of Maximizers and Satisficers Based on Collaborative Explanations

TL;DR

This study investigates how different summaries of rating distributions influence online consumer decision making, revealing that users mainly focus on the mean and number of ratings, with behavioral differences linked to decision difficulty.

Contribution

It provides new insights into how various rating distribution descriptors affect consumer choices and highlights behavioral differences among maximizers and satisficers.

Findings

Users prioritize mean and number of ratings in decision making.

Variance and origin of ratings have a lesser impact.

Participants with high decision difficulty show different sensitivities.

Abstract

Rating-based summary statistics are ubiquitous in e-commerce, and often are crucial components in personalized recommendation mechanisms. Largely left unexplored, however, is the issue to what extent the descriptives of rating distributions influence the decision making of online consumers. We conducted a conjoint experiment to explore how different summarizations of rating distributions (i.e., in the form of the number of ratings, mean, variance, skewness or the origin of the ratings) impact users' decision making. Results from over 200 participants indicate that users are primarily guided by the mean and the number of ratings and to a lesser degree by the variance, and the origin of a rating. We also looked into the maximizing behavioral tendencies of our participants, and found that in particular participants scoring high on the Decision Difficulty subscale displayed other sensitivities regarding the way in which rating distributions were summarized than others.