Modeling music-selection behavior in everyday life: A multilevel statistical learning approach and mediation analysis of experience sampling data

  • Music listening has become a highly individualized activity with smartphones and music streaming services providing listeners with absolute freedom to listen to any kind of music in any situation. Until now, little has been written about the processes underlying the selection of music in daily life. The present study aimed to disentangle some of the complex processes among the listener, situation, and functions of music listening involved in music selection. Utilizing the experience sampling method, data were collected from 119 participants using a smartphone application. For 10 consecutive days, participants received 14 prompts using stratified-random sampling throughout the day and reported on their music-listening behavior. Statistical learning procedures on multilevel regression models and multilevel structural equation modeling were used to determine the most important predictors and analyze mediation processes between person, situation, functions of listening, and music selection. Results revealed that the features of music selected in daily life were predominantly determined by situational characteristics, whereas consistent individual differences were of minor importance. Functions of music listening were found to act as a mediator between characteristics of the situation and music-selection behavior. We further observed several significant random effects, which indicated that individuals differed in how situational variables affected their music selection behavior. Our findings suggest a need to shift the focus of music-listening research from individual differences to situational influences, including potential person-situation interactions.

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Author:Fabian Greb, Jochen Steffens, Wolff Schlotz
Pubmed Id:
Parent Title (English):Frontiers in psychology
Publisher:Frontiers Research Foundation
Place of publication:Lausanne
Contributor(s):Egon L. van den Broek
Document Type:Article
Year of Completion:2019
Date of first Publication:2019/03/19
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2020/02/03
Tag:experience sampling method; functions of music listening; machine learning; music-listening behavior; music-selection behavior; user behavior analysis
Issue:Art. 390
Page Number:20
First Page:1
Last Page:20
Copyright © 2019 Greb, Steffens and Schlotz. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Institutes:Psychologie und Sportwissenschaften / Psychologie
Angeschlossene und kooperierende Institutionen / MPI für empirische Ästhetik
Dewey Decimal Classification:1 Philosophie und Psychologie / 15 Psychologie / 150 Psychologie
Licence (German):License LogoCreative Commons - Namensnennung 4.0