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


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Authors

  • Anne Mareike Flaswinkel Department of Business Administration and Economics, Bielefeld University, Universitaetsstrasse 25, 33615 Bielefeld, Germany
  • Reinhold Decker Department of Business Administration and Economics, Bielefeld University, Universitaetsstrasse 25, 33615 Bielefeld, Germany

DOI:

https://doi.org/10.32479/irmm.15673

Keywords:

Audio Streaming Platform, Recommendation. User Choice, Listening Intention, Algorithms, Recommendation Systems

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.

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Published

2024-03-19

How to Cite

Flaswinkel, A. M., & Decker, R. (2024). Comparing Algorithm-Based and Friend-Based Recommendations on Audio Streaming Platforms. International Review of Management and Marketing, 14(2), 7–12. https://doi.org/10.32479/irmm.15673

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Articles