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Paper Details


Title
HYBRID PLS-ANN APPROACH FOR SMART HOME HEALTHCARE SERVCIE ADOPTION PREDICTION: A HOUSEHOLD CASE STUDY
Author
, A Mahmud,
Email
Abstract

The healthcare industry is undergoing a paradigm shift, with a greater focus on preventative measures. The use of smart technology in health care is giving rise to new tools for monitoring vital signs in the general population. Citizens' acceptability and willingness to pay for mobile health technology is a widely disputed topic in the literature, particularly when the services offered are preventative rather than curative. The current healthcare and support systems are facing challenges due to the tremendous growth of the elderly population across the globe. This area has quickly become the leading option for the Internet of Things (IoT) and associated technologies. The healthcare industry is undergoing a sea change as traditional systems embrace new technology to become smart healthcare ecosystems. Nevertheless, achieving its broad acceptance remains an aspirational goal. The purpose of the proposed investigation was to identify the crucial factors that influence people's intentions to use smart healthcare services. The research model was developed by using the expanded diffusion of innovation (DOI) model with several external factors. The suggested model was tested using a multi-analytical technique, namely a partial least square (PLS)-artificial neural network (ANN). The findings of the hybrid analysis revealed that the most essential aspect for people to embrace SHHS is perceived relative advantage. Other factors such as innovativeness, perceived risk, compatibility, and privacy also influenced the behavioral intention of SHHS adoption. This research delves further into the theoretical, practical as well as methodological contributions of the SHHS in Saudi Arabia. Finally, the limitations of current findings and future research paths are highlighted.

Keywords
Journal or Conference Name
International Journal on Technical and Physical Problems of Engineering
Publication Year
2024
Indexing
scopus