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Factors Influencing Patient Adoption of the IoT for E-Health Management Systems (e-HMS) Using the UTAUT Model: A High Order SEM-ANN Approach

Factors Influencing Patient Adoption of the IoT for E-Health Management Systems (e-HMS) Using the UTAUT Model: A High Order SEM-ANN Approach

Manish Dadhich, Kamal Kant Hiran, Shalendra Singh Rao, Renu Sharma
Copyright: © 2022 |Volume: 13 |Issue: 1 |Pages: 18
ISSN: 1941-6237|EISSN: 1941-6245|EISBN13: 9781683180647|DOI: 10.4018/IJACI.300798
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MLA

Dadhich, Manish, et al. "Factors Influencing Patient Adoption of the IoT for E-Health Management Systems (e-HMS) Using the UTAUT Model: A High Order SEM-ANN Approach." IJACI vol.13, no.1 2022: pp.1-18. http://doi.org/10.4018/IJACI.300798

APA

Dadhich, M., Hiran, K. K., Rao, S. S., & Sharma, R. (2022). Factors Influencing Patient Adoption of the IoT for E-Health Management Systems (e-HMS) Using the UTAUT Model: A High Order SEM-ANN Approach. International Journal of Ambient Computing and Intelligence (IJACI), 13(1), 1-18. http://doi.org/10.4018/IJACI.300798

Chicago

Dadhich, Manish, et al. "Factors Influencing Patient Adoption of the IoT for E-Health Management Systems (e-HMS) Using the UTAUT Model: A High Order SEM-ANN Approach," International Journal of Ambient Computing and Intelligence (IJACI) 13, no.1: 1-18. http://doi.org/10.4018/IJACI.300798

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Abstract

This study examines factors influencing patients' adoption of the IoT for e-Health Management System (e-HMS). A conceptual framework is built by applying the Unified Theory of Acceptance and Use of Technology (UTAUT) model to achieve the research objectives. The constructs viz. performance expectations, perceived healthcare threat, facilitation, and trust were employed to analyze the behaviour intention of healthcare IoT devices in the medical context. A questionnaire was prepared and circulated to 210 IoT healthcare users. Further, a synergy of Smart-PLS and ANN was used to determine factors influencing adopting the IoT for e-HMS. The study yields a novel insight that would render vital benefits to users and service providers. The outcomes of this research can assist IoT inventors, medicinal specialists, and vendors to enhance the optimization of e-HMS. The results make some new reporting facts to improve IoT 4.0 practices in the e-healthcare system and structural model assessment.

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