Responses to AI and Human Recommendations in a Joint-Consumption Context

Companies are getting an exponentially increasing number of data points regarding consumer preferences and are using that data to make recommendations to consumers. Simultaneously, a lot of consumers are moving their social interactions online and would therefore be receiving these recommendations. The extant literature covers algorithmic recommendations and joint consumption, and joint decision-making extensively. This literature, however, does not study the interaction between the recommendation context of algorithmic recommendations and the joint-consumption context. In a Prolific-based experiment, we study multiple hypotheses and find out that consumers perceive algorithmic recommendation systems as less competent, which leads to a lower purchase likelihood. The paper closes with future directions and limitations for the research.