Exploring users’ algorithmic knowledge and reflexivity in a music streaming context: A critical realist approach

Abstract

Digital platforms such as Spotify have specific characteristics and properties that influence, to some extent, how the platform is used. However, users develop their own interpretations of these properties as well as unique ways to engage with the platform. This study applies a critical realist framework to explore how reflexivity modes are practiced in the context of Spotify as an example of algorithmic recommendation systems. From this perspective, reflexivity is a person’s capacity to reflect on their contexts, data, previous experiences, and knowledge, among other elements, before deciding how to act. Findings from interviews with Spotify users suggest that participants practice multiple reflexivity modes when interpreting Spotify’s recommendations and deciding what to listen to. These modes depend on each participant’s concerns and algorithmic knowledge.