Financial Literacy, Digital Financial Behavior, and Investment Decision-Making among University Students: An Integrated Structural Equation and Predictive Modeling Approach

Abstract

Background of study: University students increasingly manage money, payments, savings, and investments through digital platforms, making financial knowledge insufficient unless it is translated into disciplined digital financial behavior. Aims and scope of paper: This study integrates financial literacy, digital financial behavior, and investment decision-making in a causal-predictive framework, examining both structural relationships and out-of-sample prediction. Methods: A cross-sectional explanatory-predictive design is specified for university students. Partial least squares structural equation modeling (PLS-SEM) tests direct and mediated effects, while linear regression, random forest, and XGBoost models compare predictive performance. For manuscript-development purposes, the reporting format is illustrated with a synthetic dataset (N = 624), which must be replaced by verified empirical data before submission. Result: The illustrative model indicates that financial literacy positively predicts digital financial behavior and investment decision-making, while digital financial behavior shows the strongest direct association with decision quality and partially mediates the effect of literacy. XGBoost provides the best illustrative out-of-sample prediction, followed by random forest and linear regression. Conclusion: Financial literacy appears most valuable when converted into consistent digital financial practices, suggesting that higher-education financial education should combine knowledge, digital self-management, and evidence-based investment decision skills.