Comparing Algorithm-Based and Friend-Based Recommendations on Audio Streaming Platforms

Abstract

With the rise of audio streaming platforms (ASPs), users face the challenge of navigating a large amount of audio content. Companies are increasingly employing algorithms to provide personalized recommendations to their customers; however, word-of-mouth research has demonstrated in numerous studies the crucial role of friend-based recommendations, particularly in the realm of experience goods. Considering the experiential factor in ASPs, existing insights into recommendations raise the question of which recommendation source holds a greater advantage in the realm of ASPs. This study deals with recommendation sources in the field of ASPs and examines in particular the effects of algorithm-based suggestions on users' listening intentions. Using a quantitative research approach, we investigate users' attitudes toward recommended content and compare the intentions to listen to suggested content in cases of algorithmic and friend-based recommendations. Our results provide valuable insights for companies planning to provide helpful recommendations to ASP users and increase their listening intentions for recommended content.