Abstract
Purpose
Today, the use of smart technologies in healthcare systems is experiencing exponential growth, and the future of healthcare is seemingly closely intertwined with such technologies. Thus, any exploration of the factors that influence human health and healthcare systems inevitably touches upon the subject of new technologies. This study aims to design a conceptual model to investigate the elements that affect individuals' openness to accepting and using mobile healthcare applications (mHealth apps) and their reciprocal effects.
Design/methodology/approach
After a brief review of the literature, the authors identify the influential factors in the acceptance of smart technologies in healthcare systems and present a conceptual model in this regard. Next, the authors analyze the factors and variables and the extent of their impact by a structural equation modeling (SEM) approach. The statistical population of this study consists of 421 individuals including the developers, consultants and users (i.e. patients) of mHealth apps. Data analysis was done on the statistical software SPSS v.26, while SEM was carried out using the partial least squares (PLS) method on the modeling software SmartPLS.
Findings
The results indicate that user, consultant and developer preferences have a positive and significant impact on time, quality of life, managing chronic conditions and cooperation, and these constructs (system performance) finally have a positive and significant impact on the acceptance of mobile healthcare technologies.
Originality/value
This paper shows that mHealth apps can have a remarkable role in the prevention and treatment of medical conditions, and it is strongly recommended that this technology be utilized in the studied region.
Today, the use of smart technologies in healthcare systems is experiencing exponential growth, and the future of healthcare is seemingly closely intertwined with such technologies. Thus, any exploration of the factors that influence human health and healthcare systems inevitably touches upon the subject of new technologies. This study aims to design a conceptual model to investigate the elements that affect individuals' openness to accepting and using mobile healthcare applications (mHealth apps) and their reciprocal effects.
Design/methodology/approach
After a brief review of the literature, the authors identify the influential factors in the acceptance of smart technologies in healthcare systems and present a conceptual model in this regard. Next, the authors analyze the factors and variables and the extent of their impact by a structural equation modeling (SEM) approach. The statistical population of this study consists of 421 individuals including the developers, consultants and users (i.e. patients) of mHealth apps. Data analysis was done on the statistical software SPSS v.26, while SEM was carried out using the partial least squares (PLS) method on the modeling software SmartPLS.
Findings
The results indicate that user, consultant and developer preferences have a positive and significant impact on time, quality of life, managing chronic conditions and cooperation, and these constructs (system performance) finally have a positive and significant impact on the acceptance of mobile healthcare technologies.
Originality/value
This paper shows that mHealth apps can have a remarkable role in the prevention and treatment of medical conditions, and it is strongly recommended that this technology be utilized in the studied region.
| Original language | English |
|---|---|
| Pages (from-to) | 129-151 |
| Journal | American Journal of Business |
| Volume | 38 |
| Issue number | 3 |
| Early online date | 12 Jun 2023 |
| DOIs | |
| Publication status | Published - 9 Aug 2023 |
Bibliographical note
Copyright © 2023, Emerald Publishing Limited Copyright. This AAM is provided for your own personal use only. It may not be used for resale, reprinting, systematic distribution, emailing, or for any other commercial purpose without the permission of the publisher.Keywords
- Smart technologies
- Mobile health
- mHealth
- Technology acceptance
- Healthcare systems
- structural equation modeling