How Do Features of Recommendation Algorithms Impede User Acceptance? From The Perspective of Human Basic Needs

With the prevalence of recommendation algorithms, it is critical to understand factors that would impede users’ acceptance. Drawing upon self-determination theory and fairness heuristic theory, we investigate how the features of recommendation algorithms (i.e., homogeneity, interference, and non-transparency, “HINT”) affect users’ intention to use, usage frequency, and evaluation of these algorithms. Specifically, we propose that human basic needs (pursuit of procedural fairness/privacy protection, autonomy, and novelty/diversity) would mediate the relationship between HINT of recommendation algorithms and users’ perceptions and behaviors. In addition, users’ tolerance for uncertainty, constraints, and homogeneity fatigue would weaken such effects. The current research has the potential to make theoretical contributions and provide managerial guidelines.