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Study of the Continuous Intention to use Artificial Intelligence Based Internet of Medical Things (IoMT) During Concurrent Diffusion. The Influence Diffusion of Innovation Factors Has as Determinants of Continuous Intention to Use Ai-Based IoMT

Aldhaen, Fatema S.F.A.
Publication Date
2022
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Creative Commons License
The University of Bradford theses are licenced under a Creative Commons Licence.
Peer-Reviewed
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Accepted for publication
Institution
University of Bradford
Department
Faculty of Management, Law and Social Sciences
Awarded
2022
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Abstract
This research was about the continuous intention of healthcare professionals to use internet of medical things (IoMT) embedded with artificial intelligence (AI). IoMT and AI are evolving innovations and diffusing at the same time. It was not known in what way the two complex technologies diffusing concurrently could influence continuous intention to use IoMT. In addition, behavioural aspects namely motivation and training to use IoMT have been argued to intervene in the relationship between an AI based IoMT and continuous intention to use IoMT. Diffusion of Innovation theory was applied to explain the relationship between diffusion factors that aid the diffusion of AI based IoMT and continuous intention to use IoMT. The five factors relative advantage, compatibility, complexity, observability and trialability were chosen as determinants of continuous intention to use IoMT using DoI theory. Self-determination theory and theory of planned behaviour were used to introduce the interventions in the relationship between diffusion factors and continuous intention to use IoMT. UTAUT was used to explain the influence of the moderators artificial intelligence awareness, novelty seeking behaviour and age of healthcare professionals. The central issue investigated was the determinants of continuous intention of healthcare professionals to use IoMT with behavioural attributes of motivation and training conceived as mediators of the relationship between diffusion factors and continuous intention to use IoMT in the presence of moderators. Quantitative research methodology was used to test the research model developed to understand the relationship between the five diffusion of innovation theory factors and continuous intention to use IoMT when AI based IoMT is still diffusing. The concurrent diffusion of two new technologies was investigated using a research model that was developed for studying the healthcare professionals and their intention. The research was conducted in Bahrain in the healthcare sector. A sample of 354 healthcare professionals participated in the research. Structural equation modelling was used to analyse the data and test the hypothesis. The research showed that healthcare professionals will continue to use concurrently diffusing technologies depending on the relative advantage, complexity and compatibility of the innovations that diffuse. In addition, the results show that healthcare professionals will be motivated by the compatibility of AI-based IoMT if they have to continuously use IoMT. Furthermore, training enables both the organization and the healthcare professionals to overcome dilemma in case they have to continue to use an innovation during its diffusion or when new innovation surface in the market. Finally, artificial intelligence awareness is able to moderate the relationship between relative advantage, complexity and training to use IoMT. Thus, this research contributes to the discipline of behavioural intention of healthcare professionals in determining the influence of an artificial intelligence based IoMT on continuous intention to use IoMT when artificial intelligence embedded in IoMT diffuses concurrently with IoMT. Where IoMT diffusion factors can be used as a determine of continuous intention to use IoMT, artificial intelligence could be understood as a moderator of the relationship between diffusion factors and training to use IoMT, thus demonstrating the combined diffusion of the two technologies diffusing concurrently.
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Type
Thesis
Qualification name
PhD
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