Understanding International Students Adoption Intention toward Korean-Made Healthcare Sensor Technologies for Their Parents and Elderly Relatives Using the UTAUT Model
Abstract
This study investigates the factors influencing international students’ intention to adopt Korean sensor-based healthcare devices for their elderly family members. Drawing on the Unified Theory of Acceptance and Use of Technology (UTAUT), the research examines how performance expectancy, effort expectancy, social influence, and facilitating conditions affect trust and, in turn, behavioral intention. Survey data were collected from 120 international students in Korea and analyzed using partial least squares structural equation modeling. The measurement model demonstrated strong reliability and validity for all constructs. Structural model results showed that facilitating conditions had a significant positive effect on trust (β = 0.431, t = 3.729), while effort expectancy, performance expectancy, and social influence were not significant predictors. Trust, however, exhibited a strong positive effect on behavioral intention (β = 0.536, t = 6.217), underscoring its central role in adoption decisions. These findings suggest that practical support, available resources, and compatible infrastructure are more critical than perceived ease of use, usefulness, or social pressure in building trust and encouraging cross-border adoption of Korean healthcare sensor technologies for elderly care.
References
- Al-rawashdeh, M., Keikhosrokiani, P., Belaton, B., Alawida, M., & Zwiri, A. (2022). IoT Adoption and Application for Smart Healthcare: A Systematic Review. Sensors, 22(14), 5377.
- Alvi, I. (2021). College students' reception of social networking tools for learning in India: An extended UTAUT model. Smart Learning Environments, 8(1), 19.
- Arfi, W. Ben, Nasr, I. Ben, Kondrateva, G., & Hikkerova, L. (2021). The role of trust in intention to use the IoT in eHealth: Application of the modified UTAUT in a consumer context. Technological Forecasting and Social Change, 167, 120688.
- Ben Arfi, W., Belanger, F., & Triki, A. (2021). Understanding acceptance of e-health services: A privacy calculus perspective. Information & Management, 58(2), 103414.
- Bertolazzi, A., Quaglia, V., & Bongelli, R. (2024). Barriers and facilitators to health technology adoption by older adults with chronic diseases: an integrative systematic review. BMC Public Health, 24(1), 506.
- Christian, M., Gularso, K., Utomo, P., Yulita, H., Wibowo, S., Sunarno, S., & Melati, R. (2023). Generation YZ's E-Healthcare Use Factors Distribution in COVID-19's Third Year: A UTAUT Modeling. Journal of Distribution Science, 21(7), 117-129.
- DongA Science. (2025). South Korea's digital medical device market surges with rising production and exports. Dong-A Science News.
- Fortune Business Insights. (2024). South Korea medical devices market size, share & industry analysis, 2025-2032.
- Frontiers in Public Health. (2022). The behavioral intention to adopt mobile health services: An extended UTAUT model. Frontiers in Public Health, 10, 1020474.
- Garavand, A., Moosavi, A., & Naderi, N. (2019). Factors influencing the adoption of health information technologies: A systematic review. Electronic Physician, 11(2), 6762-6773.
- Jo, T. H., Ma, J. H., & Cha, S. H. (2021). Elderly perception on the internet of things-based integrated smart-home system. Sensors, 21(4), 1284.
- Jena, R. K. (2020). Measuring the impact of business management students' attitude towards adoption of technological innovation. Technology in Society, 63, 101395.
- Kazanskiy, N. L., Khonina, S. N., & Butt, M. A. (2024). A review on flexible wearables - Recent developments in non-invasive continuous health monitoring. Sensors and Actuators A: Physical, 366, 114993.
- Kim, J., Jeon, S. W., Byun, H., & Yi, E. (2023). Exploring E-health literacy and technology-use anxiety among older adults in Korea. Healthcare, 11, 1556.
- Kim, S., Zhong, Y., Wang, J., & Kim, H. S. (2024). Exploring technology acceptance of healthcare devices: the moderating role of device type and generation. Sensors, 24(24), 7921.
- Kim, Y. J., Choi, J. H., & Fotso, G. M. N. (2024). Medical professionals' adoption of AI-based medical devices: UTAUT model with trust mediation. Journal of Open Innovation: Technology, Market, and Complexity, 10(1), 100220.
- Kim, Y. S., Shin, H., Lee, M., Kim, N. H., Kim, E. H., Jung, D., & Choi, K. H. (2024). Changes in Technology Acceptance of Smart Care Beds Among Long-Term Care Workers in Korea. Healthcare, 12(21), 2195.
- Lee, H., Choi, J. Y., Kim, S. W., Ko, K. P., Park, Y. S., Kim, K. J., & Kim, K. I. (2024). Digital health technology use among older adults: exploring the impact of frailty on utilization, purpose, and satisfaction in Korea. Journal of Korean Medical Science, 39(1).
- Liu, J. Y. W., Sorwar, G., Rahman, M. S., & Hoque, M. R. (2023). The role of trust and habit in the adoption of mHealth by older adults in Hong Kong: a healthcare technology service acceptance (HTSA) model. BMC geriatrics, 23(1), 73.
- Mensah, I. K., Zeng, G., & Mwakapesa, D. S. (2022). The behavioral intention to adopt mobile health services: The moderating impact of mobile self-efficacy. Frontiers in Public Health, 10, 1020474.
- Moreno-Llamas, A., Garcia-Mayor, J., & De la Cruz-Sanchez, E. (2020). The impact of digital technology development on sitting time across Europe. Technology in society, 63, 101406.
- Pal, D., Funilkul, S., Charoenkitkarn, N., & Kanthamanon, P. (2018). Internet-of-Things and Smart Homes for Elderly Healthcare: An End User Perspective. IEEE Access, 6, 10483-10496.
- Raffaghelli, J. E., Rodriguez, M. E., Guerrero-Roldan, A.-E., & Baneres, D. (2022). Applying the UTAUT model to explain the students' acceptance of an early warning system in higher education. Computers & Education, 182, 104468.
- Sensors Market Report. (2022). Wearable healthcare devices market size, share & trends, 2022-2030.
- Soelasih, Y., Sumani, & Efendi. (2025). Consumer trust in telemedicine in Indonesia. Health Informatics Journal, 31(2), 14604582251345328.
- Son, H. (2023). South Korean Market Intelligence Report 2023.
- Stavropoulos, T. G., Papastergiou, A., Mpaltadoros, L., Nikolopoulos, S., & Kompatsiaris, I. (2020). IoT Wearable Sensors and Devices in Elderly Care: A Literature Review. Sensors, 20(10), 2826.
- Venkatesh, Morris, Davis, & Davis. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27(3), 425.
- Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS quarterly, 157-178.
- Vidal-Silva, C., Sanchez-Ortiz, A., Serrano-Malebran, J., Arriagada, V., Flores, M., Godoy, M., & Vargas, C. (2024). Social influence, performance expectancy, and price value as determinants of telemedicine services acceptance in Chile. Heliyon, 10(5), e27067.
- Wang, H., Tao, D., Yu, N., & Qu, X. (2020). Understanding consumer acceptance of healthcare wearable devices: An integrated model of UTAUT and TTF. International Journal of Medical Informatics, 139, 104156.
- Wang, J., Li, X., Wang, P., Liu, Q., Deng, Z., & Wang, J. (2021). Research trend of the unified theory of acceptance and use of technology theory: A bibliometric analysis. Sustainability, 14(1), 10.
- Wissawaswaengsuk, P., Kumar, P., Frank, B., & Badir, Y. F. (2025). The Role of Trust as the Facilitator and Contingency Factor in the Adoption of Digital Healthcare Services: A Telemedicine Context. Computers in Human Behavior, 108722.
- World Health Organization. (2025). Ageing and health.
- Yeoh, S. Y., & Chin, P. N. (2022). Exploring home health-care robots adoption in Malaysia: extending the UTAUT model. International Journal of Pharmaceutical and Healthcare Marketing, 16(3), 392-411.