Analysis of willingness to use health management APP for female college students: application of UTAUT model based on Fogg theory - Wang, Lanying; Zhang, Yinying; Li, Zhihong; Pang, Xinyu; Zhang, Yuanyuan; Zou, Mingming (Frontiers in Psychology, 2024)
Introduction As the development process of medical industry informatization has entered the stage of smart healthcare, health management applications (apps) have played an important role in improving people’s health and preventing diseases, especially among female college students. Methods This study combines the UTAUT model and the Fogg behavioral model (FBM) as a theoretical framework to investigate the factors affecting female college students’ willingness to use health management apps. A survey was conducted with 624 female college students regarding their usage of AI health management mobile applications. Results The analysis reveals that social influence (β = 0.497, p < 0.001), performance expectancy (β = 0.268, p < 0.001), effort expectancy (β = 0.359, p < 0.001), and facilitating conditions (β = 0.603, p < 0.001) positively predict attitude; social influence (β = 0.36, p < 0.001) and effort expectancy (β = 0.183, p < 0.001) positively predict perceived risk, while facilitating conditions negatively predict perceived risk (β = −0.108, p < 0.01). Additionally, performance expectancy (β = 0.231, p < 0.001), effort expectancy (β = 0.285, p < 0.001), facilitating conditions (β = 0.25, p < 0.01), and attitude (β = 0.291, p < 0.05) positively predict an individual’s intention to use such applications, which in turn affects actual behavior (β = 0.804, p < 0.001). Discussion This study develops a comprehensive theoretical framework to explore the psychological and social factors influencing female college students’ utilization of health management applications. The findings underscore the significant roles of social influence, performance expectancy, effort expectancy, and facilitating conditions in shaping user attitudes and intentions. These insights offer valuable guidance for formulating effective interventions to enhance the adoption of these applications.