Rating-based summary statistics are ubiquitous in e-commerce, and often are crucial components in personalized recommendation mechanisms. Especially visual rating summarizations have been identiied as important means to explain, why an item is presented or proposed to an user. Largely left unexplored, however, is the issue to what extent the descriptives of these rating summary statistics inluence decision making of the online consumer. Therefore, we conducted a series of two conjoint experiments to explore how diferent summarizations of rating distributions (i.e., in the form of number of ratings, mean, variance, skewness, bimodality, or origin of the ratings) impact users' decision making. In a irst study with over 200 participants, we identiied that users are primarily guided by the mean and the number of ratings, and - to lesser degree - by the variance and origin of a rating. When probing the maximizing behavioral tendencies of our participants, other sensitivities regarding the summary of rating distributions became apparent. We thus instrumented a follow-up eye-tracking study to explore in more detail, how the choices of participants vary in terms of their decision making strategies. This second round with over 40 additional participants supported our hypothesis that users, who usually experience higher decision diiculty, follow compensatory decision strategies, and focus more on the decisions they make. We conclude by outlining how the results of these studies can guide algorithm development, and counterbalance presumable biases in implicit user feedback.

Original languageEnglish
Title of host publicationProceedings of the 24th International Conference on Intelligent User Interfaces
EditorsWai-Tat Fu, Shimei Pan
Place of PublicationNew York, NY, USA
PublisherAssociation for Computing Machinery (ACM)
Number of pages12
ISBN (Electronic)978-1-4503-6272-6
Publication statusPublished - 2019
Event24th ACM International Conference on Intelligent User Interfaces, IUI 2019 - Marina del Ray, United States
Duration: 17 Mar 201920 Mar 2019


Conference24th ACM International Conference on Intelligent User Interfaces, IUI 2019
CountryUnited States
CityMarina del Ray

    Research areas

  • Conjoint analysis, Explanations, Maximizers, Recommender systems, Satisicers, User studies

ID: 53936769