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. 2020 Feb 6;15(2):e0228457.
doi: 10.1371/journal.pone.0228457. eCollection 2020.

Do the shuffle: Exploring reasons for music listening through shuffled play

Affiliations

Do the shuffle: Exploring reasons for music listening through shuffled play

Katie Rose M Sanfilippo et al. PLoS One. .

Abstract

Adults listen to music for an average of 18 hours a week (with some people reaching more than double that). With rapidly changing technology, music collections have become overwhelmingly digital ushering in changes in listening habits, especially when it comes to listening on personal devices. By using interactive visualizations, descriptive analysis and thematic analysis, this project aims to explore why people download and listen to music and which aspects of the music listening experience are prioritized when people talk about tracks on their device. Using a newly developed data collection method, Shuffled Play, 397 participants answered open-ended and closed research questions through a short online questionnaire after shuffling their music library and playing two pieces as prompts for reflections. The findings of this study highlight that when talking about tracks on their personal devices, people prioritise characterizing them using sound and musical features and associating them with the informational context around them (artist, album, and genre) over their emotional responses to them. The results also highlight that people listen to and download music because they like it-a straightforward but important observation that is sometimes glossed over in previous research. These findings have implications for future work in understanding music, its uses and its functions in peoples' everyday lives.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. The most common reasons for downloading the tracks ordered by %.
Fig 2
Fig 2. The first ranked reasons (ordered by % of total) chosen by the 70 participants who answered why they usually listen to the track.
Fig 3
Fig 3. All codes by each level.
The number of occurrences that were coded for each level 3 code.
Fig 4
Fig 4. The number of occurrences that were coded for each level 1 code.
Fig 5
Fig 5. The number of occurrences that were coded for each level 2 code.

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