Consumer Preference Analysis of Financial Technology Application Services in a Developing Economy: A Maximum Difference Scaling Approach

Situated in a developing economy context, this study explores what matters most to users when they engage with financial technology (Fintech) applications. Rather than assuming a uniform set of needs, we used Maximum Difference (MaxDiff) scaling and Random Utility Theory (RUT) to unpack how people rank nine attributes including security, ease of use, service charges, and platform type. The results show that users place the greatest emphasis on security, affordability, and usability, while attributes like regulatory backing and years of operation matter less. Interestingly, these preferences vary across demographic groups, such as age, gender, and education. The findings offer practical insight into how fintech service providers can tailor their platforms to support better‐informed consumer decisions, particularly by improving simplicity, safety, and cost fairness. The application of RUT and MaxDiff provides a robust and scalable framework for understanding user priorities, providing a basis for replication in other Fintech contexts.