Beyond technology acceptance: relatedness and self-efficacy as correlates of behavioral intention toward digital health application use

Digital health application use is shaped by motivation, technology, beliefs about their health and privacy. When considered simultaneously, the constructs examined within individual theories may overlap or lose their independent contribution. This study used an exploratory cross-theoretical framework to identify which factors emerged as the most stable correlates of future behavioral intention toward digital health application use. A cross-sectional survey was conducted ( n = 156) among adults with prior or recent experience using digital health applications. Constructs derived from UTAUT2/TAM, the Health Belief Model, Privacy Calculus Theory, and Self-Determination Theory were measured using a multi-staged structural equation modeling approach. Separate measurement for each theory to examine the relationships was conducted, followed by a full integrated exploratory model. Constructs were retained in a reduced model using explicit rule-based retention criteria based on construct validity, theoretical distinctness, independent structural contributions, and parsimony. Sensitivity analyses using latent-variable and composite-score models were subsequently performed. Several constructs were associated with behavioral intention within their respective theory-specific models, but most associations were significantly reduced after simultaneous cross-theoretical adjustment. The only constructs, Relatedness and Self-Efficacy, were retained in the primary reduced model and remained independently associated with behavioral intention, with Relatedness showing the stronger association (β = 0.516, p < .001) followed by Self-Efficacy (β = 0.376, p < .001). The reduced model described 69.5% of the variance in behavioral intention; however, model fit was mixed due to an elevated RMSEA. Adding Perceived Benefit increased variance only marginally to 70.7%, with no statistically significant adjusted association (β = 0.143, p = .053). Additionally, the central pattern was reproduced in the composite-score regression with Relatedness and Self-Efficacy as the dominant correlates, and Perceived Benefit contributed only a small additional association. Future behavioral intention toward digital health application use was most consistently associated with users’ sense of relatedness and confidence in their capability to engage with health-related behaviours and technologies. The findings suggest that relational support and self-efficacy may represent more stable cross-theoretical mechanisms than technology-specific perceptions when considered in isolation. The reduced model should be interpreted as exploratory, and replication in larger, diverse, and longitudinal samples is required before to establish whether this model has predictive or causal value.