STUDENTS' READINESS AND PERCEIVED BENEFITS OF DIGITAL HEALTH TECHNOLOGIES FOR MENTAL WELL-BEING: AN INNOVATION AND ADOPTION PERSPECTIVE

The adoption of digital mental health technologies depends on multiple technological and psychological factors influencing users’ behavioral intentions. This research integrates technology acceptance models (TAM/UTAUT) with trust and perceived product intelligence to examine students’ readiness to use digital mental health technologies. A quantitative survey was conducted among university students (N = 206), and the proposed model was tested using Partial Least Squares Structural Equation Modeling (PLS-SEM) in SmartPLS 4. The findings indicate that perceived usefulness, trust, self-innovative tendencies, and perceived product intelligence significantly predict behavioral intention to use digital mental health technologies, while perceived ease of use, social influence, and perceived behavioral control show no significant direct effects. Perceived product intelligence demonstrates both a direct association with trust and an indirect effect on behavioral intention through trust. The model exhibits strong predictive power, explaining a substantial proportion of variance in behavioral intention (R² = 0.694). Multi-group analysis reveals no significant differences between EU and non-EU students, although these results are interpreted cautiously due to measurement invariance considerations. Objective: Participants eligible for inclusion were university students aged 18 years or older, currently enrolled in a higher education program, with sufficient English proficiency to complete the survey, regardless of prior use of digital mental health technologies, which aligns with the defined research object. Methods: A quantitative, cross-sectional explanatory design within a deductive research paradigm was adopted, with hypotheses formulated a priori based on established technology acceptance and trust theories and subsequently tested using empirical data. Results: The results indicate that the proposed model demonstrates strong predictive power, explaining a substantial proportion of variance in students’ behavioral intention to use digital mental health technologies (R² = 0.694). Perceived usefulness, trust, perceived product intelligence, and personal innovativeness were found to be significant positive predictors of behavioral intention, whereas perceived ease of use, social influence, and perceived behavioral control did not show statistically significant effects. Furthermore, perceived product intelligence exhibited a significant indirect effect on behavioral intention through trust, supporting a mediation mechanism. Multi-group analysis revealed no significant differences in the relationship between trust and behavioral intention between EU and non-EU students