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AI vs. Human Recommendations: Consumer Behavior in the Music Streaming Context
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Orientador(es)
Resumo(s)
As music streaming services increasingly rely on AI algorithms to deliver more personalized
recommendations for users, it becomes important to understand how these compare to human
suggestions. This thesis explores user preferences through two experimental studies and
discovers that participants are more likely to follow and report higher satisfaction levels with
recommendations from friends than from AI. Interestingly, this preference is not driven by trust
or perceived personalization, suggesting other factors may play a more significant role. Social
context did not moderate user responses, although it did influence the perception of usefulness,
which emerged as a key mediator in group settings, with users perceiving human
recommendations as more useful than AI. These insights help fill a gap in the literature on AI
versus human recommendation sources and consumer behavior in experiential contexts like
music. The findings also provide practical guidance for streaming platforms, emphasizing the
importance of designing experiences that feel more human in order to foster engagement and
long-term user satisfaction.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Driven Marketing, specialization in Digital Marketing and Analytics
Palavras-chave
Artificial Intelligence Music Streaming Services AI Personalization AI Trust Recommendation Systems