TY - JOUR
T1 - Understanding consumer acceptance of healthcare wearable devices
T2 - An integrated model of UTAUT and TTF
AU - Wang, Hailiang
AU - Tao, Da
AU - Yu, Na
AU - Qu, Xingda
N1 - Publisher Copyright:
© 2020 Elsevier B.V.
PY - 2020/7
Y1 - 2020/7
N2 - Background: Healthcare wearable devices (HWDs) enable continuous monitoring of consumers’ health signals and have great potential to improve the efficiency and quality of healthcare. However, factors influencing consumer acceptance of HWDs are not well understood. Moreover, extant studies seem to fail to consider whether an HWD has appropriate functions to fit the requirements of consumers’ healthcare activities. Objectives: The objective of this study was to develop and empirically test a model by integrating the Unified Theory of Acceptance and Usage of Technology (UTAUT) and Task-Technology Fit (TTF) models to understand how consumers accept HWDs. Methods: A self-administered questionnaire was designed based on validated measurement scales. Data from 406 valid samples were analyzed using partial least squares structural equation modeling. Results: The results indicated that performance expectancy, effort expectancy, facilitating conditions, social influence, and task-technology fit positively affected consumers’ behavioral intention to use HWDs, and together accounted for 68.0 % of its variance. Both task and technology characteristics were significant determinants of task-technology fit and exerted impacts on behavioral intention through the mediating roles of task-technology fit and effort expectancy. Conclusions: The key findings showed that consumer acceptance of HWDs was affected by both users’ perceptions (i.e., performance expectancy, effort expectancy, social influence and facilitating conditions) and the task-technology fit. The theoretical and practical implications and contributions were provided for future researchers and practitioners to increase consumers’ use of HWDs in their healthcare activities.
AB - Background: Healthcare wearable devices (HWDs) enable continuous monitoring of consumers’ health signals and have great potential to improve the efficiency and quality of healthcare. However, factors influencing consumer acceptance of HWDs are not well understood. Moreover, extant studies seem to fail to consider whether an HWD has appropriate functions to fit the requirements of consumers’ healthcare activities. Objectives: The objective of this study was to develop and empirically test a model by integrating the Unified Theory of Acceptance and Usage of Technology (UTAUT) and Task-Technology Fit (TTF) models to understand how consumers accept HWDs. Methods: A self-administered questionnaire was designed based on validated measurement scales. Data from 406 valid samples were analyzed using partial least squares structural equation modeling. Results: The results indicated that performance expectancy, effort expectancy, facilitating conditions, social influence, and task-technology fit positively affected consumers’ behavioral intention to use HWDs, and together accounted for 68.0 % of its variance. Both task and technology characteristics were significant determinants of task-technology fit and exerted impacts on behavioral intention through the mediating roles of task-technology fit and effort expectancy. Conclusions: The key findings showed that consumer acceptance of HWDs was affected by both users’ perceptions (i.e., performance expectancy, effort expectancy, social influence and facilitating conditions) and the task-technology fit. The theoretical and practical implications and contributions were provided for future researchers and practitioners to increase consumers’ use of HWDs in their healthcare activities.
KW - Healthcare wearable device
KW - Task technology fit
KW - Technology acceptance
KW - UTAUT
UR - http://www.scopus.com/inward/record.url?scp=85084175939&partnerID=8YFLogxK
U2 - 10.1016/j.ijmedinf.2020.104156
DO - 10.1016/j.ijmedinf.2020.104156
M3 - Journal article
C2 - 32387819
AN - SCOPUS:85084175939
SN - 1386-5056
VL - 139
JO - International Journal of Medical Informatics
JF - International Journal of Medical Informatics
M1 - 104156
ER -