Leveraging AI to Lead Change and Understand User Satisfaction on Music Streaming Platforms

This study evaluates the primary antecedents influencing user satisfaction with AI-enhanced music streaming services in the Romanian context. Drawing on the Theory of Planned Behavior (TPB), Motivation Theory, and the Technology Acceptance Model (TAM), we propose a multidimensional framework that integrates eudaimonic, utilitarian, and hedonic motivations. Our objective is to investigate how these motivations jointly shape user perceptions and attitudes, ultimately impacting satisfaction with AI-based music platforms. Using a snowball sampling approach, we collected data from 188 university students in Bucharest via an online questionnaire. We then employed Partial Least Squares Structural Equation Modelling (PLS-SEM) to test our hypotheses. The results indicate that utilitarian and hedonic motivations significantly enhance both attitudes and user satisfaction, whereas eudaimonic motivation strongly influences perceptions and attitudes but yields a negative, non-significant direct effect on satisfaction. These findings highlight the need for AI-driven music streaming platforms to address not only functional and pleasurable aspects of user engagement, but also deeper emotional or reflective needs - although acknowledging that meaningful experiences may take longer to translate into satisfaction. Practically, this research offers insight into designing personalized interfaces, fostering user connections, and developing premium service plans that align with diverse motivational orientations. Theoretically, it extends existing TPB-TAM models by incorporating multi-dimensional motivation constructs and empirically validating their influence on AI-based streaming adoption.