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

Abstract

The advancement in wireless sensor and information technology has offered enormous healthcare opportunities for wearable healthcare devices and has changed the way of health monitoring. Despite the importance of this technology, limited studies have paid attention for predicting individuals’ influential factors for adoption of wearable healthcare devices. The proposed research aimed at determining the key factors which impact an individual's intention for adopting wearable healthcare devices. The extended technology acceptance model with several external variables was incorporated to propose the research model. A multi-analytical approach, structural equation modelling-neural network, was considered for testing the proposed model. The results obtained from the structural equation modelling showed that the initial trust is considered as the most determinant and influencing factor in the decision of wearable health device adoption followed by health interest, consumer innovativeness, and so on. Moreover, the results obtained from the structural equation modelling applied as an input to the neural network indicated that the perceived ease of use is one of the predictors that are significant for adoption of wearable health devices by consumers. The proposed study explains the wearable health device implementation along with test adoption model, and their outcome will help providers in the manufacturing unit for increasing actual users’ continuous adoption intention and potential users’ intention to use wearable devices.

A Multi-Analytical Approach to Predict the Determinants of Cloud Computing Adoption in Higher Education Institutions

“…It has also been noted that neural networks surpass traditional compensatory models such as multiple, discriminant, and logistic regression analyses [118,119]. However, despite the fact that neural network has been implemented in studies in different fields such as economics [120], customer loyalty [121], wearable healthcare devices [122], and consumer choice [119,123], few studies have focused on its information systems applications [124]. Hence, the present study will first utilize PLS-SEM to determine the constructs that have strong relationships with the adoption of CC in HEIs, and then implement the non-compensatory neural network model for foreseeing the adoption of CC in HEIs according to the critical adoption variables.…”

Understanding the barriers and factors associated with consumer adoption of wearable technology devices in managing personal health

“…Further, additional variables could be added to the research model. For example, Asadi, Abdullah, Safaei, and Nazir (2019) found that initial trust had the most significant effect. They analyzed their results with SEM.…”

M-health Services Equipped with Public-Sector Community Health Centers in China: Investigating Adoption using UTAUT and Channel Expansion Theory

“…The adoption of e-health and m-health services have been investigated using various theoretical approached including stimulus-organism-response theory [14], protection motivation theory [15], the theory of planned behavior, and value attitude behavioral model [16], trust transfer model [17], technology acceptance model [18][19][20][21][22][23][24][25][26], and the uni ed theory of acceptance and use of technology theory [27][28][29][30][31][32][33][34]. The uni ed theory of acceptance and use of technology (UTAUT) argues that the effort and performance expectancies with social in uences and facilitating conditions having a direct effect on the behavioral intention to use new technology.…”