Moreau, F., Viotto, J., & Wikström, P. (2026). Consumer satisfaction with new content discovery through algorithmic and human recommendations. Applied Economics Letters, 1–5. https://doi.org/10.1080/13504851.2026.2655917
Abstract. We contribute to the literature on algorithm preference/aversion and salience effect which suggests that, on the one hand, users may prefer human recommendations to AI counterparts due to an algorithm aversion, and, on the other hand, that algorithmic recommendations outperform human curation in capturing users’ attention because of the salience effect. This research utilizes a unique dataset that tracks the daily consumption of 9,778 random premium subscribers of a major European music streaming platform, who discovered 4,136 distinct new songs via algorithmic recommendations or human-curated playlists. Using the number of repeat organic streams as an indirect behavioural measure of preference matching and a control for the salience effect, we demonstrate that algorithmic recommendations assist consumers in finding items that suit their preferences. Conversely, human recommendations perform better when introducing songs from artists unfamiliar to the user.