Understanding User Acceptance of AI-Powered Financial Advisory: A Dual-Process Model Integrating Trust, Satisfaction, and Perceived Risk

This study examines user acceptance of AI-powered financial advisory tools by applying a dual-process trust model. Systematic cues, such as performance efficacy and personalisation, are proposed to influence cognitive trust, while heuristic cues, including anthropomorphism and social influence, shape emotional trust. Additionally, AI-specific attributes trendiness, visual attractiveness, and problem-solving capability are assessed for their impact on user satisfaction, which in turn drives adoption intention and acceptance. Perceived financial risk is introduced as a moderating variable that may weaken trust formation. To test the proposed framework, data were collected from 412 individuals with experience using AI-based financial services through a structured questionnaire. Quantitative analysis was used to assess the relationships among constructs. The results confirm that both trust types significantly affect satisfaction and adoption, while perceived risk reduces trust formation. These findings provide actionable insights for financial service providers and policymakers to design trustworthy, engaging, and user-centred AI advisory systems