  Recommender systems assist users in decision-making, where the presentation
of recommended items and their explanations are critical factors for enhancing
the overall user experience. Although various methods for generating
explanations have been proposed, there is still room for improvement,
particularly for users who lack expertise in a specific item domain. In this
study, we introduce the novel concept of \textit{consequence-based
explanations}, a type of explanation that emphasizes the individual impact of
consuming a recommended item on the user, which makes the effect of following
recommendations clearer. We conducted an online user study to examine our
assumption about the appreciation of consequence-based explanations and their
impacts on different explanation aims in recommender systems. Our findings
highlight the importance of consequence-based explanations, which were
well-received by users and effectively improved user satisfaction in
recommender systems. These results provide valuable insights for designing
engaging explanations that can enhance the overall user experience in
decision-making.
