The Impact of Product Uniqueness in AI Recommendation Systems on User Acceptance Willingness

Product uniqueness in AI recommendation systems significantly impacts consumer perceptions and behaviors. However, existing research on how product uniqueness in AI recommendations influences consumer acceptance is relatively scarce. Grounded in self-determination theory and empowerment theory, this study adopts a field and between-subjects experimental design to explore the internal mechanisms and boundary conditions of how product uniqueness in AI recommendations affects consumer acceptance. The findings reveal that: (1) the higher the product uniqueness recommended by AI, the higher the consumer's acceptance of the recommendation; (2) the need for uniqueness moderates the relationship between product uniqueness and consumer acceptance of AI recommendations; (3) perceived expertise plays a moderated mediation role in the relationship between product uniqueness and consumer acceptance of AI recommendations. These findings offer valuable insights into the fields of AI and marketing, while also assisting companies and e-commerce platforms in effectively utilizing AI recommendations to enhance recommendation efficiency and strengthen marketing capabilities.