Table of Contents Author Guidelines Submit a Manuscript
Mobile Information Systems
Volume 2019, Article ID 8026042, 9 pages
https://doi.org/10.1155/2019/8026042
Research Article

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

1Faculty of Computer Science and Information Technology, University Putra Malaysia, Seri Kembangan, Malaysia
2School of Computing Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bahru, Malaysia
3Department of Computer Science, University of Swabi, Pakistan

Correspondence should be addressed to Shahla Asadi; moc.liamg@3002alhahs.idasa

Received 16 November 2018; Accepted 27 January 2019; Published 14 February 2019

Guest Editor: Raquel Lacuesta

Copyright © 2019 Shahla Asadi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.