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In the intersection of technology and music listening, understanding user experiences is paramount. This research employed the Experience Sampling Method via the smartphone app MuPsych to continuously capture real-time data on individuals' music listening behaviors and related emotional responses. Over a span of two weeks, participants from Germany were prompted to report on various factors as they engaged in music listening, resulting in a rich dataset. Results indicate that Spotify Premium was the most frequently used music application, with personal playlists being the preferred listening format. To unravel the intricacies of these responses and their determinants, linear mixed-effects model analysis was utilized. Among the critical findings, the Perceived Usefulness and Perceived Ease of Use, two of the central constructs of the Technology Acceptance Model, emerged as significant predictors for the valence and enjoyment of the music experienced by users during the onset of music listening sessions. This highlights the imperative role of user-friendly interfaces in enhancing positive emotional states even before fully engaging with the music, underscoring the need for designers and developers of music-related apps to prioritise usability and useful functions.
Introduction. Music streaming services have changed how music is played and perceived, but also how it is managed by individuals. Voice interfaces to such services are becoming increasingly com-mon, for example through voice assistants on mobile and smart devices, and have the poten-tial to further change personal music management by introducing new beneficial features and new challenges.
Method. To explore the implications of voice assistants for personal music listening and management we surveyed 248 participants online and in a lab setting to investigate (a) in which situa-tions people use voice assistants to play music, (b) how the situations compare to established activities common during non-voice assistant music listening, and (c) what kinds of com-mands they use.
Analysis. We categorised 653 situations of voice assistant use, which reflect differences to non-voice assistant music listening, and established 11 command types, which mostly reflect finding or refinding activities but also indicate keeping and organisation activities.
Results. Voice assistants have some benefits for music listening and personal music management, but also a notable lack of support for traditional personal information management activities, like browsing, that are common when managing music.
Conclusion. Having characterised the use of voice assistants to play music, we consider their role in per-sonal music management and make suggestions for improved design and future research.