An Examination of Telemedicine Adoption: Unpacking the Determinants of Behavioral Intention through an Extended UTAUT
DOI:
https://doi.org/10.38035/jsmd.v3i4.822Keywords:
Behavioral Intention, E-trust, Halodoc, Telemedicine, UTAUTAbstract
The COVID-19 pandemic has led to changes in people’s lives, including the increased use of telemedicine services. Extending the UTAUT framework, the purpose of this study is to analyze the factors that influence behavioral intentions towards the adoption of telemedicine platforms and investigate the role of e-trust in promoting the use of telemedicine. A non-probability sampling approach was used to select a research sample of 204 respondents. The SEM-PLS model was used in this quantitative research. The population of this study is Halodoc users. The study found that performance expectancy, effort expectancy, facilitating conditions, and e-trust all had significant, direct, and positive effects on respondents’ intentions to use telemedicine. The findings also revealed that social influence influences e-trust in online healthcare systems. No significant relationship between social influence and behavioral intention was found. The present study suggests a positive attitude toward technology can increase user intention in healthcare systems.
References
Aceto, G., Persico, V., & Pescapé, A. (2020). Industry 4.0 and Health: Internet of Things, Big Data, and Cloud Computing for Healthcare 4.0. Journal of Industrial Information Integration, 18, 1–14. https://doi.org/10.1016/j.jii.2020.100129
Amin, R., Hossain, M. A., Uddin, M. M., Jony, M. T. I., & Kim, M. (2022). Stimuli Influencing Engagement, Satisfaction, and Intention to Use Telemedicine Services: An Integrative Model. Healthcare, 10(7), 1327. https://doi.org/10.3390/healthcare10071327
Antarsih, N. R., Setyawati, S. P., Ningsih, S., Deprizon, Sulaiman, E., & Pujiastuti, N. (2022). Telehealth Business Potential in Indonesia. Proceedings of the International Conference on Social, Economics, Business, and Education (ICSEBE 2021), 205(Icsebe 2021), 73–78. https://doi.org/10.2991/aebmr.k.220107.015
Baron-Cohen, S., Knickmeyer, R. C., & Belmonte, M. K. (2006). Genetic Research in Autism. Science, 311(5763), 952.
Barrane, F. Z., Karuranga, G. E., & Poulin, D. (2018). Technology Adoption and Diffusion: A New Application of the UTAUT Model. International Journal of Innovation and Technology Management, 15(6), 1–19. https://doi.org/10.1142/S0219877019500044
Bashshur, R. (2002). Telemedicine and health care. Telemedicine and E-Health, 8(1), 2–6.
Bashshur, R. L., Ph, D., Shannon, G. W., & Ph, D. (2009). National Telemedicine Initiatives : 600–610.
Benitez, J., Henseler, J., Castillo, A., & Schuberth, F. (2020). How to perform and report an impactful analysis using partial least squares: Guidelines for confirmatory and explanatory IS research. Information & Management, 57(2), 103168.
Chakraborty, I., Ilavarasan, P. V., & Edirippulige, S. (2021). COVID-19 as a Catalyst for Telehealth Growth in India: Some Insights. Journal of the International Society for Telemedicine and EHealth, 9, 1–4. https://doi.org/10.29086/jisfteh.9.e3
Chawla, D., & Joshi, H. (2019). Consumer attitude and intention to adopt mobile wallet in India – An empirical study. International Journal of Bank Marketing, 37(7), 1590–1618. https://doi.org/10.1108/IJBM-09-2018-0256
Cho, J. (2016). The impact of post-adoption beliefs on the continued use of health apps. International Journal of Medical Informatics, 87, 75–83. https://doi.org/10.1016/j.ijmedinf.2015.12.016
Dewanta, I. P. K. S., Supriyadinata Gorda, A. A. N. E., Darma, G. S., & Mahyuni, L. P. (2023). Influence Attitude and Behavioral Intention of the Millenial Generation to Adoption of Telemedicine Platforms in Bali in the New Normal Era. International Journal of Social Science and Business, 7(2), 369–380. https://doi.org/10.23887/ijssb.v7i2.55468
Foon, Y. S., & Fah, B. C. Y. (2011). Internet banking adoption in Kuala Lumpur: an application of UTAUT model. International Journal of Business and Management, 6(4), 161.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.
Giao, H. N. K., Vuong, B. N., & Quan, T. N. (2020). The influence of website quality on consumer’s e-loyalty through the mediating role of e-trust and e-satisfaction: An evidence from online shopping in Vietnam. Uncertain Supply Chain Management, 8(2), 351–370. https://doi.org/10.5267/j.uscm.2019.11.004
Hair, J. F, Babin, B. J., A., & R. E., & Black, W. C. (2019). Multivariate data analysis (8th ed.) Pearson.
Hair, J. F., Howard, M. C., & Nitzl, C. (2020). Assessing measurement model quality in PLS-SEM using confirmatory composite analysis. Journal of Business Research, 109(August 2019), 101–110. https://doi.org/10.1016/j.jbusres.2019.11.069
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a Silver Bullet. Journal of Marketing Theory and Practice, 19(2), 139–152. https://doi.org/10.2753/MTP1069-6679190202
Hair, J.F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). SAGE Publications, Inc.
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43, 115–135.
Henseler, J., Ringle, C. M., & Sarstedt, M. (2016). Testing measurement invariance of composites using partial least squares. International Marketing Review, 33(3), 405–431.
Hooda, A., Gupta, P., Jeyaraj, A., Giannakis, M., & Dwivedi, Y. K. (2022). The effects of trust on behavioral intention and use behavior within e-government contexts. International Journal of Information Management, 67, 102553. https://doi.org/10.1016/j.ijinfomgt.2022.102553
Hoque, R., & Sorwar, G. (2017). Understanding factors influencing the adoption of mHealth by the elderly: An extension of the UTAUT model. International Journal of Medical Informatics, 101, 75–84. https://doi.org/10.1016/j.ijmedinf.2017.02.002
Iqbal Firdaus, M., A. Utama, C., Gayatri, G., & Rofianto, W. (2023). Co-creation experience in Indonesian mobile commerce: A self-determination theory perspective. Innovative Marketing, 19(3), 145–158. https://doi.org/10.21511/im.19(3).2023.13
Jarvenpaa, S. L., Tractinsky, N., & Vitale, M. (2000). Consumer trust in an Internet store. Information Technology and Management, 1, 45–71.
Kaium, M. A., Bao, Y., Alam, M. Z., & Hoque, M. R. (2020). Understanding continuance usage intention of mHealth in a developing country: An empirical investigation. International Journal of Pharmaceutical and Healthcare Marketing, 14(2), 251–272. https://doi.org/10.1108/IJPHM-06-2019-0041
Kim, H. S. (2021). Towards Telemedicine Adoption in Korea: 10 Practical Recommendations for Physicians. Journal of Korean Medical Science, 36(16), 1–9. https://doi.org/10.3346/jkms.2021.36.e103
Li, D., & Han, X. (2021). Assessing the influence of goal pursuit and emotional attachment on customer engagement behaviors. Journal of Retailing and Consumer Services, 59, 102355.
Liao, C. Te, Chang, W. T., Yu, W. L., & Toh, H. S. (2020). Management of acute cardiovascular events in patients with COVID-19. Reviews in Cardiovascular Medicine, 21(4), 577–581. https://doi.org/10.31083/J.RCM.2020.04.18 8
Liu, Y., Gayle, A. A., Wilder-Smith, A., & Rocklöv, J. (2020). The reproductive number of COVID-19 is higher compared to SARS coronavirus. Journal of Travel Medicine, 27(2), 1–4. https://doi.org/10.1093/jtm/taaa021
Lu, Y., Papagiannidis, S., & Alamanos, E. (2019). Exploring the emotional antecedents and outcomes of technology acceptance. Computers in Human Behavior, 90, 153–169. https://doi.org/10.1016/j.chb.2018.08.056
Luarn, P., & Lin, H.-H. (2005). Toward an understanding of the behavioral intention to use mobile banking. Computers in Human Behavior, 21(6), 873–891. https://doi.org/10.1016/j.chb.2004.03.003
Manley, S. C., Hair, J. F., Williams, R. I., & McDowell, W. C. (2021). Essential new PLS-SEM analysis methods for your entrepreneurship analytical toolbox. International Entrepreneurship and Management Journal, 17, 1805–1825.
Mauco, K. L., Scott, R. E., & Mars, M. (2018). Critical analysis of e-health readiness assessment frameworks: suitability for application in developing countries. Journal of Telemedicine and Telecare, 24(2), 110–117. https://doi.org/10.1177/1357633X16686548
Meng, F., Guo, X., Peng, Z., Lai, K. H., & Zhao, X. (2019). Investigating the adoption of mobile health services by elderly users: Trust transfer model and survey study. JMIR MHealth and UHealth, 7(1). https://doi.org/10.2196/12269
Nysveen, H., & Pedersen, P. E. (2016). Consumer adoption of RFID-enabled services. Applying an extended UTAUT model. Information Systems Frontiers, 18(2), 293–314. https://doi.org/10.1007/s10796-014-9531-4
Octaviani, R. D., Sucherly, Prabowo, H., & Sari, D. (2023). Determinants of Indonesian Gen Z’s purchase behavior on online travel platforms: Extending UTAUT model. Innovative Marketing, 19(4), 54–65. https://doi.org/10.21511/im.19(4).2023.05
Pusparisa, Y. (2019). This is the Urbanite’s Go-to Health App. Katadata Media Network. http://surl.li/mkbig
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. https://doi.org/10.1080/17517575.2020.1850872
Reicher, S., Sela, T., & Toren, O. (2021). Using Telemedicine During the COVID-19 Pandemic: Attitudes of Adult Health Care Consumers in Israel. Frontiers in Public Health, 9. https://doi.org/10.3389/fpubh.2021.653553
Rho, M. J., Kim, H. S., Chung, K., & Choi, I. Y. (2015). Factors influencing the acceptance of telemedicine for diabetes management. Cluster Computing, 18(1), 321–331. https://doi.org/10.1007/s10586-014-0356-1
Samosir, J., Purba, O. R., Ricardianto, P., Dinda, M., Rafi, S., Sinta, A. K., Wardhana, A., Anggara, D. C., Trisanto, F., & Endri, E. (2023). The role of social media marketing and brand equity on e-WOM: Evidence from Indonesia. International Journal of Data and Network Science, 7(2), 609–626. https://doi.org/10.5267/j.ijdns.2023.3.010
Sarstedt, M., Ringle, C. M., Smith, D., Reams, R., & Hair Jr, J. F. (2014). Partial least squares structural equation modeling (PLS-SEM): A useful tool for family business researchers. Journal of Family Business Strategy, 5(1), 105–115.
Schmitz, A., Díaz-Martín, A. M., & Yagüe Guillén, M. J. (2022). Modifying UTAUT2 for a cross-country comparison of telemedicine adoption. Computers in Human Behavior, 130(January). https://doi.org/10.1016/j.chb.2022.107183
Seethamraju, R., Diatha, K. S., & Garg, S. (2018). Intention to Use a Mobile-Based Information Technology Solution for Tuberculosis Treatment Monitoring – Applying a UTAUT Model. Information Systems Frontiers, 20(1), 163–181. https://doi.org/10.1007/s10796-017-9801-z
Shmueli, G., Sarstedt, M., Hair, J. F., Cheah, J.-H., Ting, H., Vaithilingam, S., & Ringle, C. M. (2019). Predictive model assessment in PLS-SEM: guidelines for using PLSpredict. European Journal of Marketing, 53(11), 2322–2347.
Sok Foon, Y., & Chan Yin Fah, B. (2011). Internet Banking Adoption in Kuala Lumpur: An Application of UTAUT Model. International Journal of Business and Management, 6(4), 161–167. https://doi.org/10.5539/ijbm.v6n4p161
Sripalawat, J., Thongmak, M., & Ngramyarn, A. (2011). M-Banking in Metropolitan Bangkok and a Comparison with other Countries. Journal of Computer Information Systems, 51(3), 67–76. https://doi.org/10.1080/08874417.2011.11645487
Suki, N. M., & Suki, N. M. (2017). Determining students’ behavioural intention to use animation and storytelling applying the UTAUT model: The moderating roles of gender and experience level. International Journal of Management Education, 15(3), 528–538. https://doi.org/10.1016/j.ijme.2017.10.002
Sumerli, C. H., Faizah, A., Fauzan, R., & Setiawan, E. B. (2023). Shopeefood Application During Covid-19 For Promotion And Service Quality On Consumer Purchase Decisions. 5(2), 184–193.
Tawil, M. R., Muhammad Subandi, Moch Arif Hernawan, Prety Diawati, & Kraugusteeliana K. (2023). The Role of Perceived Ease of Use, Trust, Product Knowledge and Perceived of Convenience on Intention to Use of Sharia Banking Card. JEMSI (Jurnal Ekonomi, Manajemen, Dan Akuntansi), 9(2), 260–265. https://doi.org/10.35870/jemsi.v9i2.1014
Tran, V. D., & Vu, Q. H. (2019). Inspecting the relationship among e-service quality, e-trust, e-customer satisfaction and behavioral intentions of online shopping customers. Global Business and Finance Review, 24(3), 29–42. https://doi.org/10.17549/gbfr.2019.24.3.29
Tusyanah, T., Wahyudin, A., & Khafid, M. (2021). Analyzing Factors Affecting the Behavioral Intention to Use e-Wallet with the UTAUT Model with Experience as Moderating Variable. Et Al / Journal of Economic Education, 10(2), 113–123. http://journal.unnes.ac.id/sju/index.php/jeec
Utomo, P., Kurniasari, F., & Purnamaningsih, P. (2021). The Effects of Performance Expectancy, Effort Expectancy, Facilitating Condition, and Habit on Behavior Intention in Using Mobile Healthcare Application. International Journal of Community Service & Engagement, 2(4), 183–197. https://doi.org/10.47747/ijcse.v2i4.529
Venkatesh, V., & Zhang, X. (2010). Unified theory of acceptance and use of technology: US vs. China. Journal of Global Information Technology Management, 13(1), 5–27.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 425–478.
Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Quarterly, 157–178.
Warsame, M. H., & Ireri, E. M. (2018). Moderation effect on mobile microfinance services in Kenya:An extended UTAUT model. Journal of Behavioral and Experimental Finance, 18, 67–75. https://doi.org/10.1016/j.jbef.2018.01.008
Wei, J., Vinnikova, A., Lu, L., & Xu, J. (2021). Understanding and Predicting the Adoption of Fitness Mobile Apps: Evidence from China. Health Communication, 36(8), 950–961. https://doi.org/10.1080/10410236.2020.1724637
Woldeyohannes, H. O., & Ngwenyama, O. K. (2017). Factors influencing acceptance and continued use of mHealth apps. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10293 LNCS, 239–256. https://doi.org/10.1007/978-3-319-58481-2_19
Yap, H. Y., & Lim, T.-M. (2017). Social trust: impacts on social influential diffusion. International Journal of Web Information Systems, 13(2), 199–219. https://doi.org/10.1108/IJWIS-11-2016-0067
Zhang, T., Tao, D., Qu, X., Zhang, X., Zeng, J., Zhu, H., & Zhu, H. (2020). Automated vehicle acceptance in China: Social influence and initial trust are key determinants. Transportation Research Part C: Emerging Technologies, 112(September 2019), 220–233. https://doi.org/10.1016/j.trc.2020.01.027
Zhang, Y., Liu, C., Luo, S., Xie, Y., Liu, F., Li, X., & Zhou, Z. (2019). Factors influencing patients’ intention to use diabetes management apps based on an extended unified theory of acceptance and use of technology model: Web-based survey. Journal of Medical Internet Research, 21(8), 1–17. https://doi.org/10.2196/15023
Zhou, T., & Li, H. (2014). Understanding mobile SNS continuance usage in China from the perspectives of social influence and privacy concern. Computers in Human Behavior, 37, 283–289.
Zhou, T., Lu, Y., & Wang, B. (2016). Examining online consumers’ initial trust building from an elaboration likelihood model perspective. Information Systems Frontiers, 18(2), 265–275. https://doi.org/10.1007/s10796-014-9530-5
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