CONVENIENCE TRUMPS ALGORITHMS: PREDICTING CONTINUED INTENTION TO USE MUSIC STREAMING SERVICES

Resumen

This paper develops and examines constructs for predicting continued intention to use music streaming services and combines theoretical approaches from technology acceptance studies, collecting/sensemaking in music streaming services and algorithmic culture/individuation. This theoretical framework is chosen as each approach points to constructs that capture at least part of the use-value of these services, yet that have not been examined in combination.

The theoretical model suggests that convenience value, monetary value, will to archive, algorithmic value and age predict continued intention to use music streaming services (age negatively). The empirical basis of the study consists of interviews with 26 users of streaming services and an online survey (N=793) with respondents who pay for music streaming services. The survey items were subjected to a principal component analysis, resulting in the five foreseen factors. Items that loaded on each factor were summed and averaged and used in the subsequent hierarchical regression analyses.

The results show that monetary value is the strongest predictor followed by convenience value and will to archive. As expected, age is negatively associated with continued intention to use music streaming services. Whereas algorithmic value correlates significantly with all other constructs, it does not predict continued intention to use. The qualitative interviews help explain the results. People who invest efforts in organizing their own music libraries and playlists create added value to their own service-experience. Interviews also provide accounts of how personalized recommendations are considered important by the most avid music-listeners, yet that casual listeners pay little attention to recommendations.