Examining Trust and Willingness to Accept AI Recommendation Systems

This paper proposes and tests a conceptual model that identifies the antecedents of trust in AI, which could in turn lead to users’ willingness to accept AI recommendation systems. An online survey was conducted in the context of stock market investment. Responses came from 313 participants with prior investment experiences. Data were analyzed using partial least squares structural equation modeling. Results indicate that attitude towards AI and perceived AI accuracy were positively related to users’ trust in AI. Users’ AI anxiety was negatively related to trust in AI. Furthermore, users’ trust in AI was positively related to their willingness to accept AI recommendation systems. The paper extends previous works by explicating the role of users’ trust in AI and suggests that the uptake of AI systems can be promoted by fostering favorable attitudes, greater perceived AI accuracy, and lowering AI anxiety.