Decision Making of Maximizers and Satisficers Based on Collaborative Explanations
This addresses the problem of understanding consumer behavior in e-commerce for personalized recommendations, but it is incremental as it explores an underexplored aspect without major breakthroughs.
The study investigated how different summary statistics of rating distributions influence online consumer decision-making, finding that users primarily rely on the mean and number of ratings, with variance and rating origin having lesser effects, based on data from over 200 participants.
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.