Antecedents of Intention to Adopt Mobile Health (mHealth) Application and Its Impact on Intention to Recommend: An Evidence from Indonesian Customers

Table 1

Summary of studies with mHealth adoption models.

Author (year)	Theory	Dependent variable	Findings
Diño and de Guzman [110]	UTAUT and HBM	Behavioural intention for telehealth use	UTAUT constructs (especially EE) are significant influences, while gender shows no moderating effect.
Deng et al. [111]	Extended TAM, trust and perceived risk	Adoption of mHealth services	Trust, PU, and PEOU positively correlate with adoption, while privacy and performance risks negatively correlate with trust and intention to adopt.
Meng et al. [112]	Trust transfer model	mHealth service use intention	Trust in mHealth services and trust in offline health services affect intention to use positively.
Gong et al. [113]	Extended valence and trust	Adoption of OHCS	Subjective norms, trust in providers, and perceived benefit have a positive effect, while offline habits negatively affect.
Zhang et al. [63]	UTAUT	Intention to use diabetes management applications	PE and social influence are the most important determinants.
Ramírez-Correa et al. [114]	TPB and TAM	Adoption of telemedicine during COVID-19 pandemic	TPB provides a significant explanatory power.

TAM: technology acceptance model; OHCS: online health consultation service; UTAUT: unified theory of acceptance and use of technology; TPB: the theory of planned behaviour; HBM: health belief model; EE: effort expectancy; PU: perceived usefulness; PEOU: perceived ease of use.