We began by defining the research around two connected questions: why users consider fund or robo-advisory investment, and what makes them hesitate. The project looked at fund investment not only as a financial product, but as an experience shaped by confidence, perceived effort, expected return, service trust, and the clarity of information provided in the app.
This framing helped us identify the core tension in the service experience. Users wanted easier investment management and more diversified ways to invest, but they also faced barriers such as difficulty choosing products, concerns about returns, service fees, and uncertainty around how robo-advisory technology actually works.
We conducted an online survey with people in their 20s to 50s who had used, or were willing to use, fund and robo-advisory investment services. The survey explored existing investment behavior, financial app usage patterns, expectations for robo-advisory services, barriers to adoption, and willingness to use the proposed service concept.
The results showed that users expected robo-advisory services to help them respond to market changes, receive personalized investment recommendations, and compensate for limited investment knowledge. At the same time, technology trust, fees, and difficulty understanding financial terms remained important barriers that shaped whether users would consider using the service.
We synthesized the survey results to identify patterns across investment motivation, service hesitation, app visit behavior, and engagement preferences. The analysis showed that users visited financial apps mainly to check asset or investment status and complete financial actions, while regular missions and rewards could also support repeated visits when they felt personally useful.
The research also revealed that gamification needed to be handled carefully in an investment context. Users showed stronger preference for rewards, quizzes, missions, and personal progress than for competition or social comparison. This suggested that engagement features should reinforce learning, self-checking, and confidence rather than making investment feel like a game of ranking or performance display.
We translated the research findings into UX directions for a robo-advisory investment experience that could feel easier to enter and safer to evaluate. The recommendations focused on clarifying expected value, reducing uncertainty around technology and fees, and helping users understand investment options through more transparent explanations of risk, return, and service logic.
The final direction emphasized a staged experience that supports different levels of investment confidence. Rather than presenting robo-advisory investing as a fully automated solution, the service needed to help users understand what the system is doing, why a recommendation is being made, and how each action connects to their own investment goals.
