We compared the effectiveness of quantitatively providing the result (profit or loss)that users can achieve through investment advice provided by robo-advisor based on attribute framing theory. The main factors considered while evaluating them were the transparency of the system and the understandability and acceptance of investment advisory information. Researchers in related fields emphasise the importance of improving transparency in artificial intelligence decision-making algorithms and information asymmetry in financial advice to increase customers’ acceptance of robo-advisor. The financial information provided by robo-advisor canbe difficult for customers to understand, which negatively impacts their willingness to use the system. Positive framing, which takes into account the user experience by providing clear meanings and concise sentence composition, contributes to effective message design. The results suggest that providing a positive expectation for advice acceptance (i.e. quantitative representation) has a positive impact on improving transparency and mitigating information asymmetry in decision-making systems.