Mobile Information Systems / 2019 / Article / Tab 1

Research Article

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

Table 1

Related studies on wearable technology adoption.


[19]SmartwatchN/AQualitative study“Perceived usefulness, perceived ease of use, enabling technologies, functionality, complementary goods, continuous usage intention”
[21]Wearable deviceUTAUTSurvey“Performance expectancy, hedonic motivation, privacy concern, facilitating conditions, hedonic, trust, effort expectancy”
[6]Wearable deviceExtended TAMSurvey“Hedonic motivation, social influence, risk, functionality, perceived ease of use, visual attractiveness, perceived ease of use, brand name, perceived usefulness”
[22]Wearable deviceReference group theory, TAM, and health belief modeSurvey“Health belief, perceived convenience, health belief, perceived usefulness, perceived credibility, consumer innovativeness, perceived interpretability”
[2]Wearable devicePrivacy calculus theorySurvey“Perceived privacy risk (information sensitivity, perceived prestige, legislative protection, personal innovativeness legislative), perceived benefit (perceived informativeness, functional congruence), adoption intention, actual adoption”
[23]Smart glassesExtended TAMSurvey“Complexity, self-efficacy, usefulness, health concern, ease of use, risk, intention, external influence”
[24]Smart glassesExtended TAMSurvey“Hedonic motivation, perceived ease of use, perceived usefulness”
[25]Fitness wearableN/AFocus group“Perceived effort, utilitarian benefits, gender, physiological traits, social influence, hedonic”
[26]Wearable deviceExtended UTAUTSurvey“Facilitating conditions, social influence, trust, performance expectancy”
[27]Smart glassesTAMSurvey“Technology risk, perceived usefulness, privacy, hedonic motivation, image, perceived ease of use, norms”