Edinburgh Napier University

  Music streaming platforms (MSPs) employ recommender systems to assist listeners with navigating extensive libraries (Petridis et al., 2022), discovery of new content and establishing new audiences (Mehrotra et al., 2020). There are concerns that algorithmic based recommendation (ABR) systems may produce “filter bubbles”, reducing diversity of recommendations (Anderson et al., 2020; Roth et al., 2020). Studies investigating user responses towards ABRs provide evidence of “algorithmic aversion” and “algorithmic appreciation” (Yeomans et al., 2019; Araujo, 2020). MSPs may also publish editorially created playlists that provide listeners with human-curated recommendations which are more diverse than ABRs (Spotify, 2018; Villermet et al., 2021). Listeners may also receive word-of-mouth recommendations, using knowledge of each other’s music preferences as well as reflexive self-performance (Hagen & Lüders, 2016). Customer behaviour studies have shown that trust in ABRs plays a part in both volume and value of sales (Gorgoglione, Panniello & Tuzhilin, 2011). We aim to investigate a) how listeners’ receptivity may vary based on the perceived source of music recommendations and b) listeners’ awareness of how recommendation source may influence receptivity. We believe this research will improve the understanding of the role of ABRs on listener behaviour and therefore support musicians in developing sustainable revenue streams.