Self-avatar representation matters: Deciphering user immersion in VR games through Steam reviews

Abstract

This study critically examines the influence of self-avatars on user immersion in VR games by analyzing user reviews from Steam's top 100 VR games. Utilizing the BERT algorithm for text classification and detailed manual coding on avatar representations, the research addresses the effects of presence, perspective, visual features, and interactivity of avatars on immersion. Although the Mann-Whitney U test results were non-significant, effect size analyses revealed practical implications of avatar characteristics on user immersion. Notably, the study identifies key trends in avatar design within popular VR games, such as the predominance of first-person perspectives and the relative importance of hand representations over facial features. These findings suggest a need for a shift in focus in avatar research towards more user-relevant features. This innovative approach, using user-generated content, marks a significant departure from traditional experimental methods. It offers a richer, more ecologically valid understanding of user experiences in VR. The study's insights have significant implications for future avatar design and research.