Telemedicine has emerged as a vital component of healthcare delivery, particularly in Saudi Arabia, where its adoption was significantly accelerated by the COVID-19 pandemic. Despite widespread adoption, understanding the factors driving continued use of telemedicine applications remains critical to ensuring their long-term sustainability. This study examines the effects of confirmation (CF), perceived risk (PR), perceived usefulness (PU), and social influence (SINF) on users’ intention to continue using the Sehhaty telemedicine application in Saudi Arabia. A quantitative research design was employed, utilizing stratified random sampling to ensure representativeness across population subgroups. Data were collected from 397 telemedicine users and analyzed using SPSS for descriptive analysis and SmartPLS for Partial Least Squares Structural Equation Modelling (PLS-SEM). The findings indicate that confirmation and social influence have significant positive effects on continuance intention, with social influence emerging as the strongest predictor. Perceived usefulness also exhibits a positive, albeit weaker, significant effect on intention, whereas perceived risk does not significantly influence continued use. The model explains a substantial proportion of variance in continuance intention, highlighting the robustness of the proposed framework. These results underscore the importance of social and contextual factors in sustaining telemedicine use within government-led digital health ecosystems and offer practical implications for healthcare policymakers and platform developers seeking to enhance long-term user engagement with national telemedicine services.
Anawade, P. A., Sharma, D., & Gahane, S. (2024). A comprehensive review on exploring the impact of telemedicine on healthcare accessibility. Cureus, 16(3), e55996.
Alamir, I. A. (2025). Extended Technology Acceptance Model (TAM) for Evaluating Digital Banking Adoption in Saudi Arabia. Scientific Journal of King Faisal University, Humanities & Management Sciences, 26(1).
AlFawaz, I., & Alrasheed, A. (2023). Experiences with telemedicine among family medicine residents at King Saud University Medical City during the COVID-19 pandemic: A cross-sectional study. BMC Medical Education, 23. [https://doi.org/10.1186/s12909-023-04295-0](https://doi.org/10.1186/s12909-023-04295-0)
Al-Haimi, B., Ali, F., & Hujainah, F. (2024). Digital transformation in healthcare: Impact on organizations' strategies, future landscape, and required skills. In Navigating the Intersection of Business, Sustainability and Technology (pp. 61–74). Singapore: Springer Nature.
Al-Samarraie, H., Ghazal, S., Alzahrani, A. I., & Moody, L. (2020). Telemedicine in Middle Eastern countries: Progress, barriers, and policy recommendations. International Journal of Medical Informatics, 141, 104232.
Alsadhan, I., Alyusuf, E. Y., Shaltoot, O., Alruwashid, S., Alhamad, M., Ekhzaimy, A. A., Fouda, M. A., & Sulimani, R. A. (2022). Adoption of telemedicine care for osteoporotic patients during the COVID-19 pandemic: Experience from a tertiary care center in Saudi Arabia. Archives of Osteoporosis, 18.
Bakshi, S., & Tandon, U. (2021). Understanding barriers of telemedicine adoption: A study in North India. Systems Research and Behavioral Science, 39(1), 128–142. [https://doi.org/10.1002/sres.2774](https://doi.org/10.1002/sres.2774)
Baradwan, S., & Al-Hanawi, M. (2023). Perceived knowledge, attitudes, and barriers toward the adoption of telemedicine services in the Kingdom of Saudi Arabia: Cross-sectional study. JMIR Formative Research, 7(1), e46446.
Baptista, G., & Oliveira, T. (2015). Understanding mobile banking: The unified theory of acceptance and use of technology combined with cultural moderators. Computers in Human Behavior, 50, 418–430.
Bashir, M. S., Lalithabai, D. S., AlOtaiby, S., & Abu-Shaheen, A. (2023). Health care professionals’ knowledge and attitudes toward telemedicine. Frontiers in Public Health, 11. https://doi.org/10.3389/fpubh.2023.957681.
Binyamin, S. S., & Hoque, M. R. (2020). Understanding the drivers of wearable health monitoring technology: An extension of the unified theory of acceptance and use of technology. Sustainability, 12(22), 9605.
Chandra Chanda, R., Vafaei-Zadeh, A., Hanifah, H., Nikbin, D., & Chang, R. (2025). Modeling telemedicine continuance usage: Findings from PLS-SEM and fsQCA. Journal of Health Organization and Management.
Gallardo, M. O., Dela Torre, J., & Ebardo, R. (2024). The role of initial trust in the behavioral intention to use telemedicine among Filipino older adults. Gerontology and Geriatric Medicine, 10, 23337214231222110.
Gupta, A., Yousaf, A., & Mishra, A. (2020). How pre-adoption expectancies shape post-adoption continuance intentions: An extended expectation-confirmation model. International Journal of Information Management, 52, 102094.https://doi.org/10.1016/j.ijinfomgt.2020.102094.
Harst, L., Lantzsch, H., & Scheibe, M. (2019). Theories predicting end-user acceptance of telemedicine use: Systematic review. Journal of Medical Internet Research, 21(5), e13117. https://doi.org/10.2196/13117.
Hsieh, H. L., Lai, J. M., Chuang, B. K., & Tsai, C. H. (2022). Determinants of telehealth continuance intention: A multi-perspective framework. Healthcare, 10(10), 2038.
Kamal, S. A., Shafiq, M., & Kakria, P. (2020). Investigating acceptance of telemedicine services through an extended technology acceptance model (TAM). Technology in Society, 60, 101212.
Kissi, J., Dai, B., Ou, W.-H., Agbi, F. A., & Obeng, E. K. (2020). An empirical study of intrinsic determinants influencing telemedicine services adoption by healthcare professionals. International Journal of Scientific Research in Science and Technology, 90–103.
Li, J., Ma, Q., Chan, A. H. S., & Man, S. S. (2019). Health monitoring through wearable technologies for older adults: Smart wearables acceptance model. Applied Ergonomics, 75, 162–169. [https://doi.org/10.1016/j.apergo.2018.10.006](https://doi.org/10.1016/j.apergo.2018.10.006)
Liu, K., & Tao, D. (2022). The roles of trust, personalization, loss of privacy, and anthropomorphism in public acceptance of smart healthcare services. Computers in Human Behavior, 127, 107026.
Mackwood, M., Butcher, R., Vaclavik, D., Alford-Teaster, J. A., Curtis, K. M., Lowry, M., & Tosteson, A. N. (2022). Adoption of telemedicine in a rural US cancer center amid the COVID-19 pandemic: Qualitative study. JMIR Cancer, 8(3), e33768.
Mao, L., Mohan, G., & Normand, C. (2023). Use of information communication technologies by older people and telemedicine adoption during COVID-19: A longitudinal study. Journal of the American Medical Informatics Association, 30, 2012–2020.
Mirabootalebi, N., Meidani, Z., Akbari, H., Rangraz Jeddi, F., Tagharrobi, Z., Swoboda, W., & Holl, F. (2024). Design and psychometric evaluation of nurses’ mobile health device acceptance scale (NMHDA-scale): Application of the expectation-confirmation theory. JMIR Human Factors, 11, e55324.
Mohiuddin, S., Thorakkattil, S., Abushoumi, F., Nemr, H., Jabbour, R., & Al-Ghamdi, F. (2021). Implementation of pharmacist-led tele medication management clinic in ambulatory care settings: A patient-centered care model in COVID-19 era. Exploratory Research in Clinical and Social Pharmacy, 4. [https://doi.org/10.1016/j.rcsop.2021.100083](https://doi.org/10.1016/j.rcsop.2021.100083)
Porat-Packer, T., Green, G., Sharon, C., & Tesler, R. (2025). Factors influencing telemedicine adoption among healthcare professionals in geriatric medical centers: A Technology Acceptance Model approach. Behavioral Sciences, 15(10), 1367. [https://doi.org/10.3390/bs15101367](https://doi.org/10.3390/bs15101367)
Van, H., Pham, L., Williamson, S., Huong, V., Hoa, P., & Trang, P. (2020). Impact of perceived risk on mobile banking usage intentions: Trust as a mediator and a moderator. International Journal of Business and Emerging Markets, 12(1), 94. [https://doi.org/10.1504/IJBEM.2020.106202](https://doi.org/10.1504/IJBEM.2020.106202)
Wijaya, L., Ng, K. C., & Sihombing, P. R. (2023). Assessing determinants of the telemedicine applications continuance usage intention with TTF theory. In Proceedings of the 8th International Conference on Business and Industrial Research (ICBIR) (pp. 256–261). IEEE.
Wu, T., & Ho, C. (2023). Barriers to telemedicine adoption during the COVID-19 pandemic in Taiwan: Comparison of perceived risks by socioeconomic status correlates. International Journal of Environmental Research and Public Health, 20(4), 3504. [https://doi.org/10.3390/ijerph20043504](https://doi.org/10.3390/ijerph20043504)
Zahidah, S., & Al-haimi, B. (2024). Telemedicine: Transforming healthcare accessibility and quality with sustainable technological advancement. Navigating the Intersection of Business, Sustainability and Technology, 137–149.
Alenzi, S., Al-Haimi, B., Jusoh, N. M., Zakaria, N. H., & Alruwaili, K. (2026). What Drives Continued Use of Telemedicine in Saudi Arabia? A Model of Confirmation, Usefulness, Social Influence, and Risk. International Journal of Academic Research in Business and Social Sciences, 16(3), 28–42.
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