Revisiting the Digital Jukebox in Daily Life: Applying Mood Management Theory to Algorithmically Curated Music Streaming Environments

Experimental evidence has profoundly contributed to our understanding of Mood Man-agement Theory (MMT) in the context of music. Extant research, however, lacks insights into everyday mood regulation through music listening, especially on music streaming services where selections can be guided by algorithmic recommendations. Hence, we tested MMT in a naturalistic setting by combining experience sampling with logged music streaming data, while accounting for algorithmic curation as a boundary condition to users’ music choices. In a pre-registered study utilizing T = 6,864 surveys from N = 144 listeners, results showed that mood, music selection, and algorithmic curation varied substantially from situation to situation. How-ever, we found no effects between mood and music choices that would confirm MMT’s selection hypotheses, yet in part, small congruent effects between mood and music. Algorithmic curation did not establish novel MMT-related patterns. Our findings suggest re-specifying MMT and related media use theories for daily life.