Navigating Patients’ Intentions in Smart Health: Insights from Novel Factors

Abstract

This research aims to evaluate the extent to which a specific set of factors affect patients’ intention to adopt and use smart health applications. These factors, derived from the unified theory of acceptance and use of technology (UTAUT), include technology anxiety (TA), perceived reliability (PR), inertia (Int) and privacy concern (PC). Data were collected from 259 participants via a cross-sectional survey. The researchers employed the partial least squares (PLS) method for data analysis using SmartPLS, a statistical tool based on structural equation modelling (SEM). The findings indicate that performance expectancy, facilitating conditions, Int and PR significantly influence patients’ intentions to adopt smart health services. However, no significant relationships were found between effort expectancy, social influence, PC, TA and the intention to adopt smart health services. This study contributes to the literature by integrating additional factors (TA, PR, Int and PC) into the UTAUT framework. Limitations of the study include the cross-sectional design, which may not capture changes over time, and the reliance on self-reported data, which may be subject to biases. Future research should consider longitudinal studies to examine the evolving nature of technology adoption and explore additional contextual factors that might influence patient intentions. This research provides practical insights for healthcare practitioners aiming to enhance the adoption of smart health applications.