An Integrated SEM-Neural Network Approach for Predicting Determinants of Adoption of Wearable Healthcare Devices

Table 1

Related studies on wearable technology adoption.

References	Technology	Theory	Method	Variables
[19]	Smartwatch	N/A	Qualitative study	“Perceived usefulness, perceived ease of use, enabling technologies, functionality, complementary goods, continuous usage intention”
[21]	Wearable device	UTAUT	Survey	“Performance expectancy, hedonic motivation, privacy concern, facilitating conditions, hedonic, trust, effort expectancy”
[6]	Wearable device	Extended TAM	Survey	“Hedonic motivation, social influence, risk, functionality, perceived ease of use, visual attractiveness, perceived ease of use, brand name, perceived usefulness”
[22]	Wearable device	Reference group theory, TAM, and health belief mode	Survey	“Health belief, perceived convenience, health belief, perceived usefulness, perceived credibility, consumer innovativeness, perceived interpretability”
[2]	Wearable device	Privacy calculus theory	Survey	“Perceived privacy risk (information sensitivity, perceived prestige, legislative protection, personal innovativeness legislative), perceived benefit (perceived informativeness, functional congruence), adoption intention, actual adoption”
[23]	Smart glasses	Extended TAM	Survey	“Complexity, self-efficacy, usefulness, health concern, ease of use, risk, intention, external influence”
[24]	Smart glasses	Extended TAM	Survey	“Hedonic motivation, perceived ease of use, perceived usefulness”
[25]	Fitness wearable	N/A	Focus group	“Perceived effort, utilitarian benefits, gender, physiological traits, social influence, hedonic”
[26]	Wearable device	Extended UTAUT	Survey	“Facilitating conditions, social influence, trust, performance expectancy”
[27]	Smart glasses	TAM	Survey	“Technology risk, perceived usefulness, privacy, hedonic motivation, image, perceived ease of use, norms”