Determining the Factors Influencing College Students’ Intention to Use Mobile Health Applications: An Integrated SEM-ANN Approach

This study integrates the Technology Acceptance Model and Innovation Resistance Theory to deeply explore the key factors influencing college students’ intention to use mobile health applications. A random sample of 879 college students participated in a questionnaire survey, collecting self-reports on eight constructs: perceived value barriers, perceived complexity, perceived risk, resistance, perceived usefulness, perceived ease of use, attitude, and usage intention. This research used a hybrid method of Structural Equation Modeling-Artificial Neural Network to reveal complex and non-linear relationships between the predictors and usage intention. Results indicate that attitude is the strongest predictor of usage intention, followed by resistance. Perceived value barriers, perceived risk, and perceived complexity have no significant direct effect on attitude but have a significant positive impact on resistance. Perceived ease of use does not significantly influence resistance. Resistance serves as a complementary partial mediator in the impact of perceived usefulness on attitude and fully mediates the effect of perceived value barriers, perceived risk, and perceived complexity on attitude. Furthermore, through multilayer perceptron analysis, attitude is identified as the most crucial predictor (normalized importance 100%), followed by perceived usefulness (52.0%), resistance (28.2%), perceived ease of use (18.8%), perceived risk (14.0%), perceived complexity (9.4%), and perceived value barriers (9.1%). Finally, this study presents theoretical and practical implications for investigating the factors influencing college students’ intention to use mobile health applications.