Determinants of intention to use medical smartwatch-based dual-stage SEM-ANN analysis

Almarzouqi, A ORCID: https://orcid.org/0000-0003-3370-1818, Aburayya, A ORCID: https://orcid.org/0000-0002-1428-0547 and Salloum, S ORCID: https://orcid.org/0000-0002-6073-3981 2022, 'Determinants of intention to use medical smartwatch-based dual-stage SEM-ANN analysis' , Informatics in Medicine Unlocked, 28 , p. 100859.

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Abstract

The current study is based on an integrated research model developed by combining constructs from the Technology Acceptance Model (TAM) and other features affecting smartwatch effectiveness, such as content richness and user satisfaction (SAT). TAM is used to locate factors influencing the adoption of the smartwatch (ASW). Most importantly, the current study focuses on factors influencing smartwatch acceptance and use in the medical area, facilitating and enhancing the effective role of doctors and patients. The present study's conceptual framework examines the close association between two-term TAM variables of perceived ease of use (PEU) and perceived usefulness (PU) and the constructs of user satisfaction and content richness. It also incorporates the flow theory (EXP) to measure the effectiveness of the smartwatch. The study also uses the flow theory to assess involvement and control over ASW. The study used a sample of 489 respondents from the medical field, including doctors, nurses, and patients. The study employed a hybrid analysis method combining Structural Equation Modeling (SEM) and an Artificial Neural Network (ANN) based on deep learning. The study also used Importance-Performance Map Analysis (IPMA) to determine the relevance and performance of the variables influencing ASW. Based on the ANN and IPMA analyses, user satisfaction is the most crucial predictor of intention to use a medical smartwatch. Applying the structural equation model to the sample shows that SAT, PU, PEU, and EXP significantly influence intention to use a medical smartwatch. The study also revealed that content richness is an important factor that enhances users' PU. The current study could enable healthcare provider practitioners and decision-makers to identify factors for prioritisation and to strategise their policies accordingly. Methodologically, this study indicates that a “deep ANN architecture” can determine the non-linear associations between variables in the theoretical model. Overall, the study finds that smartwatches are in high demand in the medical field and are useful in information transmission between doctors and their patients.

Item Type: Article
Schools: Schools > School of Computing, Science and Engineering
Journal or Publication Title: Informatics in Medicine Unlocked
Publisher: Elsevier
ISSN: 2352-9148
Related URLs:
Depositing User: USIR Admin
Date Deposited: 14 Mar 2022 10:46
Last Modified: 14 Mar 2022 10:46
URI: http://usir.salford.ac.uk/id/eprint/63376

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