Extending UTAUT with Perceived Trust and Perceived Risk to Explain Telehealth Revisit Intention Among Young Adults in Jakarta, Indonesia: A PLS-SEM Study
DOI:
https://doi.org/10.56427/jcbd.v5i3.1049
Keywords:
Extended UTAUT, Perceived trust, Perceived risk, Revisit intention, TelehealthAbstract
This study examines the determinants of young adults’ intention to revisit telehealth services in a post-pandemic developing-country setting. An extended Unified Theory of Acceptance and Use of Technology (UTAUT) framework incorporating perceived trust and perceived risk was applied. Data were collected from 12 September to 31 October 2025 using non-probability purposive sampling, yielding 294 valid responses from young adults who had used private telehealth platforms in Jakarta, Indonesia. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS. The model explained 63.1% of the variance in behavioral intention. Perceived trust was the strongest predictor of revisit intention (β = 0.391, p < 0.001), followed by performance expectancy (β = 0.241, p < 0.001), social influence (β = 0.169, p = 0.006), and effort expectancy (β = 0.150, p = 0.019). Facilitating conditions and perceived risk were not significant. The findings extend the UTAUT framework to a post-adoption telehealth context in an urban developing-country market. Practically, sustaining revisit intention depends more on cultivating perceived trust through reliable service delivery, transparent data practices, and consistent platform performance than on technical support or direct risk mitigation.
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[1] M. J. Rahim et al., “Telehealth utilization barriers among Alabama parents of pediatric patients during COVID-19 outbreak,” BMC Health Serv. Res., vol. 23, no. 1, p. 693, Jun. 2023, doi: 10.1186/s12913-023-09732-w.
[2] M. Stoltzfus, A. Kaur, A. Chawla, V. Gupta, F. N. U. Anamika, and R. Jain, “The role of telemedicine in healthcare: an overview and update,” Egypt. J. Intern. Med., vol. 35, no. 1, p. 49, Jun. 2023, doi: 10.1186/s43162-023-00234-z.
[3] C. S. Kruse, K. Williams, J. Bohls, and W. Shamsi, “Telemedicine and health policy: A systematic review,” Health Policy Technol., vol. 10, no. 1, pp. 209–229, Mar. 2021, doi: 10.1016/j.hlpt.2020.10.006.
[4] E. Pramudita, H. Achmadi, and H. Nurhaida, “Determinants of behavioral intention toward telemedicine services among Indonesian Gen-Z and Millenials: a PLS–SEM study on Alodokter application,” J. Innov. Entrep., vol. 12, no. 1, p. 68, Oct. 2023, doi: 10.1186/s13731-023-00336-6.
[5] G. Aydin and S. Kumru, “Paving the way for increased e-health record use: elaborating intentions of Gen-Z,” Health Systems, vol. 12, no. 3, pp. 281–298, Jul. 2023, doi: 10.1080/20476965.2022.2129471.
[6] A. Chen, W.-M. Chu, and N. Peng, “Promoting new users’ online health consultation services usage behavior strategically,” Health Mark. Q., vol. 41, no. 2, pp. 214–239, Apr. 2024, doi: 10.1080/07359683.2024.2340196.
[7] A. I. Alzahrani, H. Al-Samarraie, A. Eldenfria, J. E. Dodoo, and N. Alalwan, “Users’ intention to continue using mHealth services: A DEMATEL approach during the COVID-19 pandemic,” Technol. Soc., vol. 68, p. 101862, Feb. 2022, doi: 10.1016/j.techsoc.2022.101862.
[8] M. Duplaga and N. Turosz, “User satisfaction and the readiness-to-use e-health applications in the future in Polish society in the early phase of the COVID-19 pandemic: A cross-sectional study,” Int. J. Med. Inform., vol. 168, p. 104904, Dec. 2022, doi: 10.1016/j.ijmedinf.2022.104904.
[9] C. Vidal-Silva et al., “Social influence, performance expectancy, and price value as determinants of telemedicine services acceptance in Chile,” Heliyon, vol. 10, no. 5, p. e27067, Mar. 2024, doi: 10.1016/j.heliyon.2024.e27067.
[10] F. R. Muharram et al., “Adequacy and Distribution of the Health Workforce in Indonesia,” WHO South. East. Asia J. Public Health, vol. 13, no. 2, pp. 45–55, Jul. 2024, doi: 10.4103/WHO-SEAJPH.WHO-SEAJPH_28_24.
[11] Mordor Intelligence, “INDONESIA CONNECTED HEALTHCARE MARKET SIZE & SHARE ANALYSIS - GROWTH TRENDS AND FORECAST (2026 - 2031).” Accessed: Aug. 16, 2026. [Online]. Available: https://www.mordorintelligence.com/industry-reports/indonesia-connected-healthcare-market
[12] S. Vannelli, F. Visintin, and S. Gitto, “Investigating Continuance Intention for Telehealth Visits in Children’s Hospitals: Survey-Based Study,” J. Med. Internet Res., vol. 27, p. e60694, Apr. 2025, doi: 10.2196/60694.
[13] L. Zhu, X. Jiang, and J. Cao, “Factors Affecting Continuance Intention in Non-Face-to-Face Telemedicine Services: Trust Typology and Privacy Concern Perspectives,” Healthcare, vol. 11, no. 3, p. 374, Jan. 2023, doi: 10.3390/healthcare11030374.
[14] J. Su, Y. Wang, H. Liu, Z. Zhang, Z. Wang, and Z. Li, “Investigating the factors influencing users’ adoption of artificial intelligence health assistants based on an extended UTAUT model,” Sci. Rep., vol. 15, no. 1, p. 18215, May 2025, doi: 10.1038/s41598-025-01897-0.
[15] Md. A. Hossain, R. Amin, A. Al Masud, Md. I. Hossain, M. A. Hossen, and M. K. Hossain, “What Drives People’s Behavioral Intention Toward Telemedicine? An Emerging Economy Perspective,” Sage Open, vol. 13, no. 3, Jul. 2023, doi: 10.1177/21582440231181394.
[16] A. S. Ahadzadeh, S. L. Wu, F. S. Ong, and R. Deng, “The Mediating Influence of the Unified Theory of Acceptance and Use of Technology on the Relationship Between Internal Health Locus of Control and Mobile Health Adoption: Cross-sectional Study,” J. Med. Internet Res., vol. 23, no. 12, Dec. 2021, doi: 10.2196/28086.
[17] J. Zhao, B. Li, J. Sun, X. Zeng, and J. Zheng, “Determinants of chronic disease patients’ intention to use Internet diagnosis and treatment services: based on the UTAUT2 model,” Front. Digit. Health, vol. 7, 2025, doi: 10.3389/fdgth.2025.1543428.
[18] H. Shao, C. Liu, L. Tang, B. Wang, H. Xie, and Y. Zhang, “Factors Influencing the Behavioral Intentions and Use Behaviors of Telemedicine in Patients With Diabetes:Web-Based Survey Study,” JMIR Hum. Factors, vol. 10, no. 1, Jan. 2023, doi: 10.2196/46624.
[19] G. S. Octavius and F. Antonio, “Antecedents of Intention to Adopt Mobile Health (mHealth) Application and Its Impact on Intention to Recommend: An Evidence from Indonesian Customers,” Int. J. Telemed. Appl., vol. 2021, 2021, doi: 10.1155/2021/6698627.
[20] M. Yang, J. Jiang, M. Kiang, and F. Yuan, “Re-Examining the Impact of Multidimensional Trust on Patients’ Online Medical Consultation Service Continuance Decision,” Information Systems Frontiers, 2021, doi: 10.1007/s10796-021-10117-9.
[21] T. C. Wu and C. T. Ho, “Reconstructing Risk Dimensions in Telemedicine: Investigating Technology Adoption and Barriers During the COVID-19 Pandemic in Taiwan,” J. Med. Internet Res., vol. 27, 2025, doi: 10.2196/53306.
[22] C. Jhantasana, “Should A Rule of Thumb be used to Calculate PLS-SEM Sample Size,” Asia Social Issues, vol. 16, no. 5, p. e254658, Apr. 2023, doi: 10.48048/asi.2023.254658.
[23] M. K. Ward and A. W. Meade, “Annual Review of Psychology Dealing with Careless Responding in Survey Data: Prevention, Identiication, and Recommended Best Practices,” Annu. Rev. Psychol. 2023, vol. 74, p. 10, 2026, doi: 10.1146/annurev-psych-040422.
[24] L. Kuen, F. Schürmann, D. Westmattelmann, S. Hartwig, S. Tzafrir, and G. Schewe, “Trust transfer effects and associated risks in telemedicine adoption,” Electronic Markets, vol. 33, no. 1, Dec. 2023, doi: 10.1007/s12525-023-00657-0.
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