BACKGROUND
In line with the National Transformation Programme 2020 (NTP), a component of the Saudi Vision 2030, the Ministry of Health (MOH) of Saudi Arabia has taken the initiative to improve healthcare provision through the introduction of mobile technology. It is anticipated that mhealth services will be more and more in demand, given the growing proportion of Internet users in Saudi Arabia as well as the global epidemic. Moreover, the Saudi government has pledged ongoing backing for e-health projects geared towards expanding the extent of technology acceptance among all concerned groups [1]. Under these circumstances, the determinants of mHealth acceptance within developing regions are receiving fresh attention owing to the latest healthcare developments. Health ICT research has embraced the UTAUT model because it is widely applicable to investigations of technology acceptance.
OBJECTIVE
To gain insight into the determinants of mHealth acceptance among patients in Saudi Arabia, a modified version of the UTAUT model is implemented in this study. Incorporating eight theoretical frameworks of individual acceptance, this model has been employed in healthcare settings since it was first developed and has been confirmed to be suitable for shedding light on technology acceptance within a healthcare context.
METHODS
Google Forms was used to develop an online questionnaire during the second semester (September) of the academic year 2019. Data were reported from 320 patients who had used mHealth applications developed by the MOH of Saudi Arabia. The questionnaire was distributed via different social media sites.
RESULTS
According to the results of this study, performance expectancy (PE) construct estimated the behavioural intention (BI) construct (β = 0.163, p < 0.05) positively, which supported H1. Further, effort expectancy (PE) predicted BI (β = 0.236, p < 0.05) positively, which supported H2. On the other hand, social influence (SI) failed to predict BI (β = -0.071 p > 0.05) significantly, thus rejecting H3. Facilitating conditions (FC), however, predicted BI (β = 0.511 p < 0.05) positively, which supported H4. Finally, system quality (SQ) predicted BI (β = 0. 0.367 p < 0.05) positively, which supported H5; and finally, trust (Tr) positively predicted behavioural intent (β = 0. 0.278 p < 0.05), thus providing support for H6.
CONCLUSIONS
The study has made valuable contributions by exploring and identifying the critical factors that affect mHealth acceptance in Saudi Arabia and identifying the major challenges when implementing mHealth. The UTAUT model identified a selection of factors that influenced the uptake of mHealth services. Performance expectancy, effort expectancy, facilitating conditions, system quality and trust in the ICT system were all positively influential. However, social factors did not appear to affect the behavioural intention to use mHealth.