  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.
