Prediction of the intention to use a smartwatch : a comparative approach using machine learning and partial least squares structural equation modeling

Elnagar, A; Alnazzawi, N; Afyouni, I; Shahin, I; Bou Nassif, A; Salloum, S

Abstract

This study makes use of a cohesive yet innovative research model to identify the determinants of the adoption of smart watches using constructs from the Technology Acceptance Model (TAM) and constructs of smartwatches, including effectiveness, content richness, and personal innovativeness. The chief objective of the study was to encourage the use of smartwatches for medical purposes so that the role of doctors can be made more effective and to facilitate access to patient records. Our conceptual framework highlights the association of TAM constructs (i.e., perceived usefulness and perceived ease of use) with the content richness, the construct of user satisfaction, and innovativeness. To measure the effectiveness of the smartwatch, an external factor based on the flow theory was added, which emphasizes the control over the smartwatch and the degree of involvement. The study employs data from 385 respondents involved in the field of medicine, such as doctors, patients, and nurses. The data were gathered through a survey and used for evaluation of the research model using partial least squares structural equation modeling (PLS-SEM) and machine learning (ML) models. The significance and performance of factors impacting THE adoption of smartwatches were also identified using Importance-Performance Map Analysis (IPMA). User satisfaction is the most important predictor of intention to adopt a medical smartwatch according to the ML and IPMA analyses. The fitting of the structural equation model to the sample showed a high dependence of user satisfaction on perceived usefulness and perceived ease of use. Furthermore, two critical factors, innovativeness and content richness, are demonstrated to enhance perceived usefulness. However, one should consider that perceived usefulness or behavioral intention could not be determined based on perceived ease of use. In general, the findings suggest that smartwatch usage could become critically important in the medical field as a mediator that allows doctors, patients, and other users to access essential information.

Citation

Elnagar, A., Alnazzawi, N., Afyouni, I., Shahin, I., Bou Nassif, A., & Salloum, S. (2022). Prediction of the intention to use a smartwatch : a comparative approach using machine learning and partial least squares structural equation modeling. Informatics in Medicine Unlocked, 29, 100913. https://doi.org/10.1016/j.imu.2022.100913

• Journal Article Type: Article
• Acceptance Date: Mar 9, 2022
• Online Publication Date: Mar 11, 2022
• Publication Date: Mar 12, 2022
• Deposit Date: Mar 29, 2022
• Publicly Available Date: Mar 29, 2022
• Journal: Informatics in Medicine Unlocked
• Print ISSN: 2352-9148
• Publisher: Elsevier
• Volume: 29
• Pages: 100913
• DOI: https://doi.org/10.1016/j.imu.2022.100913
• Publisher URL: https://doi.org/10.1016/j.imu.2022.100913
• Related Public URLs: https://www.sciencedirect.com/journal/informatics-in-medicine-unlocked