Serendipity is one of beyond-accuracy objectives for recommender systems (RSs), which aims to achieve both relevance and unexpectedness of recommendations, so as to potentially address the ``filter bubble'' issue of traditional accuracy-oriented RSs. However, so far most of the serendipity-oriented studies have focused on developing algorithms to consider various types of item features or user characteristics, but are largely based on their own assumptions. Few have stood from users' perspective to identify the effects of these features on users' perceptions of the serendipity of the recommendation. Therefore, in this paper, we have analyzed their effects with two user survey datasets. These are the Movielens Serendipity dataset of 467 users' responses to a retrospective survey of their perceptions of the recommended movie's serendipity, and the Taobao Serendipity dataset of 11,383 users' perceptions of the serendipity of a recommendation received at a mobile e-commerce platform. In both datasets, we have analyzed the correlations between users' serendipity perceptions and various types of item features (i.e., item-driven such as popularity, profile-driven such as in-profile diversity, and interaction-driven including category-level and item-level features), as well as the influence of several user characteristics (including the Big-Five personality traits and curiosity). The results disclose both domain-independent and domain-specific observations, which may be constructive in enhancing current serendipity- oriented recommender systems by better utilizing item features and user data.