Modeling IoT adoption in elderly healthcare: A hybrid TAM-UTAUT approach
Abstract
The growing elderly population in developing countries, such as Iran, underscores the urgent need to improve healthcare services. In this context, the Internet of Things (IoT) serves as an innovative solution that has the potential to greatly enhance elderly care. This study aims to investigate the factors influencing the adoption of IoT in elderly healthcare in Iran and proposes a hybrid model to address this issue. The proposed model combines two widely recognized frameworks, the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT), while incorporating technical, individual, social, and health-related dimensions. This quantitative research was conducted by distributing questionnaires among various age groups in Iran. Data were analyzed using SPSS and SmartPLS software, and the model was evaluated for its measurement and structural components. The study found that perceived advantages, social influences, and technical facilitating conditions significantly affected the adoption of IoT, consistent with earlier research. Conversely, perceived risk, personal innovativeness, and ease of use were found to be insignificant. These findings can help shape effective strategies for encouraging the adoption of healthcare technologies for the elderly in Iran and similar contexts. This can lead to better management of the aging population crisis and improved care quality in developing countries. • Proposes a hybrid TAM–UTAUT model for IoT adoption in elderly healthcare • Identifies perceived benefits and necessity as key drivers of performance expectancy • Shows that facilitating conditions and social support reduce complexity and anxiety • Shows that cultural and social conditions may reshape adoption factor priorities
Fields of Study & Subjects
Technology Use by Older Adults
Context-Aware Activity Recognition Systems
IoT and Edge/Fog Computing
Publication details
- Journal / source
- Acta Psychologica
- Publisher
- Elsevier BV
- Volume
- 264
- Pages
- 106542–106542
- Publication date
- 27 February 2026
- Language
- EN