Factors influencing telehealth adoption among consumers in Malaysia

Michelle Hii Siu Yin, Rajani Balakrishnan, Asokan Vasudevan, Sharmila Ramachandran, Huimin Fan, Muhamad Saufi Che Rusuli

Article ID: 6114
Vol 8, Issue 13, 2024

VIEWS - 18 (Abstract) 14 (PDF)

Abstract


This study investigates the factors influencing the adoption of telehealth among consumers in Malaysia, aiming to understand the impact of effort expectancy, performance expectancy, computer self-efficacy, and trust on the intention to use telehealth, building on the Unified Theory of Acceptance and Use of Technology (UTAUT). A quantitative descriptive methodology was used, collecting data from 390 Malaysian consumers via an online survey. The data were analyzed using IBM SPSS software to evaluate the relationships between the variables. The analysis revealed significant positive relationships between all examined factors and the adoption of telehealth. Performance expectancy was the most influential factor, followed by trust, effort expectancy, and computer self-efficacy. The multiple regression model indicated that these variables collectively explain 82.1% of the variance in telehealth adoption intention. The findings provide valuable insights for providers and marketers, suggesting that telehealth platforms should focus on performance expectancy, trust, and ease of use. Additionally, the study emphasizes the need for supportive policies from the Malaysian government to enhance telehealth adoption. The results contribute to the literature on healthcare technology adoption, offering practical implications for improving telehealth implementation in Malaysia.


Keywords


telehealth; consumer behavior; effort expectancy; performance expectancy; computer self-efficacy; trust

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References


Achim, N., & Kassim, A. A. (2015). Computer usage: The impact of computer anxiety and computer self-efficacy. Procedia - Social and Behavioral Sciences, 172, 701-708.

AlQudah, A.A., Al-Emran, M., & Shaalan, K. (2021). Technology acceptance in healthcare: a systematic review. Applied Sciences, 11, 10537.

Ball, H. L. (2019). Conducting online surveys. Journal of Human Lactation, 35(3), 413-417.

Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS quarterly, 189-211.

Gauthier, G. (2021). 18 months of COVID: which impact on the digital landscape and theconsumer? Global ePharmacy Summit, 22-24 September.

Hadji, B., & Degoulet, P. (2016). Information System End-user Satisfaction and Continuance Intention: A Unified Modeling Approach. Journal of Biomedical Informatics, 61, 185-193.

Ho, F. (2020). Reigniting Malaysia’s healthcare with telemedicine. New Straits Times. Retrieved November 3, 2022, from https://www.nst.com.my/opinion/columnists/2020/11/645592/reigniting-malaysias-healthcare-telemedicine

Kanagaraj, R. (2021). Rising telehealth trend is here to stay. [Online]. Available at https://www.businesstoday.com.my/2021/03/09/rising-telehealth-trend-accelerated-by-COVID-19-is-here-to-stay/ [Accessed 1 May 2022].

Kissi, J., Dai, B., Dogbe, C.S., Banahene J., & Ernest O. (2020). Predictive factors of physicians’ satisfaction with telemedicine services acceptance. Health Informatics Journal, 26(3), 1866-1880.

Lee, S. (2020). Malaysia Telehealth Grows in Contactless World. CodeBlue. Retrieved November 3, 2022, from https://codeblue.galencentre.org/2020/06/08/malaysia-telehealth-grows-incontactless-world/

Maillet, É., Mathieu, L., & Sicotte, C. (2015). Modeling Factors Explaining the Acceptance, Actual Use and Satisfaction of Nurses Using an Electronic Patient Record in Acute Care Settings: An Extension of the UTAUT. International Journal of Medical Informatics, 84(1), 36-47.

Marikyan, D., & Papagiannidis, S. (2021). Unified theory of acceptance and use of technology: A review. In S. Papagiannidis (Ed.), TheoryHub book.

Napitupulu, D., Yacub, R., & Kusuma, A. H. P. (2021). Factor influencing telehealth acceptance during the COVID-19 outbreak: Extending the UTAUT model. International Journal of Engineering Intelligent Systems for Electrical Engineering and Communications, 14(3), 267-281.

Nayak, S. D. P., & Narayan, K. A. (2019). Strengths and weaknesses of online surveys. Journal of Humanities and Social Science, 24(5), 31-38.

Omboni, S., Padwal, R.S., Alessa, T., Benczúr, B., Green, B.B., Hubbard, I., Kario, K., Khan, N.A., Konradi, A., Logan, A.G., Lu, Y., Mars, M., McManus, R.J., Melville, S., Neumann, C.L., Parati, G., Renna, N.F., Ryvlin, P., Saner, H., Schutte, A.E., & Wang, J. (2022). Theworldwide impact of telemedicine during COVID-19: current evidence and recommendations for the future. Conn Health, 1, 7-35.

Rahi, S., Khan, M.M., & Alghizzawi, M. (2021). Factors influencing the adoption of telemedicine health services during COVID-19 pandemic crisis: an integrative research model. Enterprise Information Systems, 15(6), 769-793.

Sabrina, M. I., & Defi, I. R. (2021). Telemedicine guidelines in South East Asia—A scopingreview. Frontiers in Neurology, 11, 581649.

Sarstedt, M., & Mooi, E. (2019). Regression analysis in a concise guide to market research. Springer, Berlin, Heidelberg.

Sekaran, U., & Bougie, R. (2016). Research methods for business: A skill-building approach (7th ed.). Haddington: John Wiley and Sons.

Tan, Z. Y. (2019). Cover story: Bringing the future of healthcare to Malaysia. The Edge Markets. Retrieved November 3, 2022, from https://www.theedgemarkets.com/article/cover-story-bringing-future-healthcare-malaysia

Thong, H.K., Wong, D.K.C., Gendeh, H.S., Saim, L., Athar, P.P.B.S.H., & Saim, A. (2021). Perception of telemedicine among medical practitioners in Malaysia during COVID-19. Journal of Medical Life, 14(4), 468-480.

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of informationtechnology: toward a unified view. MIS Quarterly, 27(3), 425-478.

Wang, X., White, L., Chen, X., Gao, Y., Li, H., & Luo, Y. (2015). An Empirical Study of Wearable Technology Acceptance in Healthcare. Industrial Management & Data Systems, 115(9), 1704-1723.

Wu, D., Gu, H., Gu, S., & You, H. (2021). Individual motivation and social influence: A study of telemedicine adoption in China based on social cognitive theory. Health Policy and Technology, 10(3), 100525.

Yeo, K.J., Al-Ashwal, R.H.A., Handayani, L., & Lee, S.H. (2019). Healthcare receivers’ acceptance of telecardiology in Malaysia. Telkomnika, 17(3), 1128-1135.

Zailani, S., Gilani, M. S., Nikbin, D., & Iranmanesh, M. (2014). Determinants of telemedicine acceptance in selected public hospitals in Malaysia: Clinical perspective. J Med Syst., 38(9).




DOI: https://doi.org/10.24294/jipd6114

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