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

Decision Making of Maximizers and Satisficers Based on Collaborative Explanations