Acceptance of mobile health applications by the general public: the roles of technology acceptance model constructs, health-app-specific factors, and socio-demographic moderators.
Acceptance of mobile health applications by the general public: the roles of technology acceptance model constructs, health-app-specific factors, and socio-demographic moderators.
Zhenzhen Xie,C. Or,Xu-Chun Ye
2025 · DOI: 10.1016/j.apergo.2025.104655
Applied Ergonomics · 0 Citations
TLDR
The acceptance model showed strong empirical fit and the potential to support health app development and deployment and examined the moderating effects of selected socio-demographic characteristics on intention to use.
Abstract
BACKGROUND
Despite the transformative potential of health applications (apps) to improve the general public's health and healthcare knowledge, their acceptance remains a barrier to success. Although research on health app acceptance has expanded, most studies have focused on classical factors, such as those in the technology acceptance model (TAM), giving less attention to health-app-specific factors, such as technological characteristics and operational details.OBJECTIVES
To expand our knowledge of the factors influencing health app acceptance, particularly among the general public, this study aimed to (i) develop and test a model of acceptance (measured by intention to use) that incorporates TAM constructs and health-app-specific factors and (ii) examine the moderating effects of selected socio-demographic characteristics on intention to use.METHODS
We formulated an acceptance model by expanding the TAM to include nine additional constructs and tested it using a cross-sectional survey of 600 adults from the general public, stratified by age, gender, and district of residence. Partial least squares structural equation modeling was applied to test the structural model. Multigroup analyses examined whether age, gender, education level, and household income moderated the relationships between model factors and acceptance.RESULTS
The model accounted for 55 % of the variance in acceptance, which was positively influenced by perceived usefulness, mHealth literacy, smartphone screen size, and healthcare providers' attitudes and negatively affected by smartphone memory consumption. Perceived ease of use, favorable features, and quality of information explained 58 % of the variance in perceived usefulness. Regarding moderation, older individuals showed less concern about perceived usefulness, whereas men and individuals with higher education levels exhibited less concern about smartphone screen size.CONCLUSION
Our acceptance model showed strong empirical fit and the potential to support health app development and deployment. To achieve acceptance among the general public, app developers should enhance app usability, ensure high-quality information, include desirable app features, and meet user needs. Usability testing and stakeholder-centered design approaches are essential to achieve these goals.