Understanding acceptance of eHealthcare by IoT natives and IoT immigrants: An integrated model of UTAUT, perceived risk, and financial cost

Wissal Ben Arfi, Imed Ben Nasr, Tatiana Khvatova () and Younes Ben Zaied ()
Additional contact information
Wissal Ben Arfi: EDC - EDC Paris Business School
Imed Ben Nasr: Excelia Group | La Rochelle Business School
Tatiana Khvatova: EM - EMLyon Business School
Younes Ben Zaied: EDC - EDC Paris Business School

Post-Print from HAL

Abstract: The Internet of Things (IoT) is a modern disruptive technological approach that connects devices and people in a smart way at any time and at any place. The development of IoT is forecast to generate high economic value, improve efficiency of enterprises' operational processes, and benefit the personal and professional lives of its end users. This new model of human–technology interaction is under-researched, especially with regard to eHealth. The current study aims to close this research gap by investigating IoT adoption in eHealthcare from the customer perspective and by including financial cost in the extended Unified Theory of Acceptance and Use of Technology (UTAUT) framework. The model is validated based on data collected from a randomly selected sample of 268 potential users of IoT-based healthcare devices in France. Structural modeling reveals that the cost of using IoT in eHealthcare is the key barrier to IoT adoption. Age is a significant mediator of customers' intention to use IoT in eHealthcare and inspires the formulation of two new categories: IoT natives and IoT immigrants. The findings have practical application for IoT developers, policymakers, and potentially for marketers.

Keywords: IoT adoption; eHealthcare; Extended UTAUT model; Generation gap; IoT native; IoT immigrant (search for similar items in EconPapers)
Date: 2021-02-01
Note: View the original document on HAL open archive server: https://hal.science/hal-03188218
References: Add references at CitEc
Citations: View citations in EconPapers (18)

Published in Technological Forecasting and Social Change, 2021, 163, ⟨10.1016/j.techfore.2020.120437⟩

Downloads: (external link)
https://hal.science/hal-03188218/document (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-03188218

DOI: 10.1016/j.techfore.2020.120437

Access Statistics for this paper

More papers in Post-Print from HAL
Bibliographic data for series maintained by CCSD ().