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April 23, 2025
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December 30, 2024
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Abstract
This research aims to explore the factors influencing individual decisions regarding the use of e-health applications in Indonesia, specifically in the DKI Jakarta Province, utilizing a modified version of the UTAUT2 model. Despite the conclusion of the COVID-19 pandemic, telemedicine remains a focal point in healthcare delivery. A qualitative approach, supplemented by survey methods, was employed to collect data. The study utilized responses from users of various e- health applications, including Halodoc, Alodokter, Klikdokter, and Riliv, among others. Data were gathered from 420 respondents via an online survey administered through Google Forms. Structural Equation Modeling using Partial Least Squares (PLS-SEM) analysis was conducted to test the proposed hypotheses.The findings revealed that factors such as performance expectations, user friendliness, facilitating conditions, habits, hedonic motivation, price-value perception, perceived product superiority, and perceived safety significantly influence users' behavioral intentions to engage with e-health applications. Conversely, social influence and hedonic motivation did not demonstrate a significant effect on users' behavioral intentions. These results provide valuable insights into the preferences and factors that shape the adoption of e-health applications within Indonesian society.
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Copyright (c) 2024 Ade Akbar Kurniawan, Effed Darta Hadi
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Analysis of Factors Affecting the Behavior of Using E-Health Applications with Modified Technology Utaut Model 2. (2024). The Manager Review, 6(2), 97–128. https://doi.org/10.33369/tmr.v6i2.41265
References
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Abdillah, W., & Jogianto. (2015). Partial Least Squares (PLS): An Alternative to Structural Equation Modeling in Business Research. Yogyakarta: Andi Publisher.
Al-Azzam, M. K., & Alazzam, M. B. (2019). Smart city and smart health framework: Challenges and opportunities. International Journal of Advanced Computer Science and Applications, 10(2), 171–176. https://doi.org/10.14569/IJACSA.2019.0100223
Al-Azzam, M. K., Alazzam, M. B., & al-Manasra, M. K. (2019). mHealth for decision-making support: A case study of eHealth in the public sector. International Journal of Advanced Computer Science and Applications, 10(5), 381–387. https://doi.org/10.14569/IJACSA.2019.0100547
Alfansi, L., & Atmaja, F. T. (2009). Service failure and complaint behavior in the public hospital industry: The Indonesian experience. Journal of Nonprofit and Public Sector Marketing, 21(3), 309–325. https://doi.org/10.1080/10495140802644554
Alfansi, L., & Daulay, M. Y. I. (2021). Factors affecting the use of e-money in the millennial generation: Research model UTAUT 2. Jurnal Manajemen Dan Pemasaran Jasa, 14(1), 109–122. https://doi.org/10.25105/jmpj.v14i1.8212
Alpay, L. L., Henkemans, O. B., Otten, W., Rövekamp, T. A. J. M., & Dumay, A. C. M. (2010). E-health applications and services for patient empowerment: Directions for best practices in the Netherlands. Telemedicine and E- Health, 16(7), 787–791. https://doi.org/10.1089/tmj.2009.0156
Ami-Narh, J. T., & Williams, P. A. H. (2012). A revised UTAUT model to investigate e-health acceptance among health professionals in Africa. Journal of Emerging Trends in Computing and Information Sciences, 3(10), 1383–1391.
Annur, C. M. (2022). Survey: Halodoc is the most frequently used health application among mothers in Indonesia. Retrieved from https://databoks.katadata.co.id/datapublish/2021/12/22/survei-halodoc-jadi-aplikasi-kesehatan- paling-sering-digunakan-ibu-di-indonesia (Accessed June 5, 2022, 21:25 WIB).
Angelia, D. (2021). 10 most frequently used mental health service applications in Indonesian society in 2022. Retrieved from https://goodstats.id/article/10-aplikasi-layanan-kesehatan-mental-paling-sering-digunakan- masyarakat-indonesia-2022-6Fir1 (Accessed June 10, 2023).
Baudier, P., Kondrateva, G., & Ammi, C. (2020). The future of telemedicine cabins? The case of French students’ acceptability. Futures, 122, 102595. https://doi.org/10.1016/j.futures.2020.102595
Bol, N., Helberger, N., & Weert, J. C. M. (2018). Differences in mobile health app use: A source of new digital inequalities? Information Society, 34(3), 183–193. https://doi.org/10.1080/01972243.2018.1438550
Boontarig, W., Chutimaskul, W., Chongsuphajaisiddhi, V., & Papasratorn, B. (2012). Factors influencing the intention of Thai elderly to use smartphones for e-health services. 2012 IEEE Symposium on Humanities, Science and Engineering Research, 479–483. https://doi.org/10.1109/SHUSER.2012.6268881
Brauner, P., Van Heek, J., & Ziefle, M. (2017). Age, gender, and technology attitude as factors for acceptance of smart interactive textiles in home environments: Towards a smart textile technology acceptance model. ICT4AWE 2017 - Proceedings of the 3rd International Conference on Information and Communication Technologies for Ageing Well and e-Health, 13–24. https://doi.org/10.5220/0006255600130024
Chen, Q. L., & Zhou, Z. H. (2016). Unusual formations of superoxo heptaoxomolybdates from peroxo molybdates.
Inorganic Chemistry Communications, 67(3), 95–98. https://doi.org/10.1016/j.inoche.2016.03.015
De Veer, A. J. E., Peeters, J. M., Brabers, A. E. M., Schellevis, F. G., Rademakers, J. J. D. J. M., & Francke, A. L. (2015). Determinants of the intention to use e-health by community-dwelling older people. BMC Health Services Research, 15(1), 1–9. https://doi.org/10.1186/s12913-015-0765-8
Duarte, P., & Pinho, J. C. (2019). A mixed methods UTAUT2-based approach to assess mobile health adoption.
Journal of Business Research, 102(February), 140–150. https://doi.org/10.1016/j.jbusres.2019.05.022
Edeh, E., Lo, W.-J., & Khojasteh, J. (2022). Review of Partial Least Squares Structural Equation Modeling (PLS- SEM) using R: A workbook. Structural Equation Modeling: A Multidisciplinary Journal. https://doi.org/10.1080/10705511.2022.2108813
Enaizan, O., Eneizan, B., Almaaitah, M., Al-Radaideh, A. T., & Saleh, A. M. (2020). Effects of privacy and security on the acceptance and usage of electronic medical records: The mediating role of trust based on multiple perspectives. Informatics in Medicine Unlocked, 21, 100450. https://doi.org/10.1016/j.imu.2020.100450
Enaizan, O., Zaidan, A. A., Alwi, N. H. M., Zaidan, B. B., Alsalem, M. A., Albahri, O. S., & Albahri, A. S. (2020). Electronic medical record systems: Decision support examination framework for individual security and privacy concerns using multi-perspective analysis. Health and Technology, 10(3), 795–822. https://doi.org/10.1007/s12553-018-0278-7
Expectancy, P. (2005). The UTAUT questionnaire items. In E-Health Systems Diffusion and Use: The Innovation, the User and the USE IT Model (pp. 93–97). https://doi.org/10.4018/978-1-59140-423-1.ch005
Ghozali, I., & Latan, H. (2020). Partial Least Squares: Concepts, Techniques, and Applications Using SmartPLS 3.0 for Empirical Research (2nd ed.). Semarang: Undip Publishing.
Ghozali, I. (2021). Partial Least Squares: Concepts, Techniques, and Applications Using SmartPLS 3.2.9 for Empirical Research (3rd ed.). Semarang: Undip Publishing.
Ghozali, I., & Kusumadewi, K. A. (2023). Partial Least Squares: Concepts, Techniques, and Applications Using SmartPLS 4.0 for Empirical Research (3rd ed.). Semarang: Undip Publishing.
Goleman, D., Boyatzis, R., McKee, A., & Perdana. (2018). Blueprint for the Indonesian Payment System 2025. Bank Indonesia: Navigating the National Payment System in the Digital Era. Journal of Chemical Information and Modeling, 53(9), 1689–1699.
Gu, D., Khan, S., Khan, I. U., Khan, S. U., Xie, Y., Li, X., & Zhang, G. (2021). Assessing the adoption of e-health technology in a developing country: An extension of the UTAUT model. SAGE Open, 11(3). https://doi.org/10.1177/21582440211027565
Guo, X., Han, X., Zhang, X., Dang, Y., & Chen, C. (2015). Investigating m-health acceptance from a protection motivation theory perspective: Gender and age differences. Telemedicine and E-Health, 21(8), 661–669. https://doi.org/10.1089/tmj.201
Hair, J. (2017). Multivariate Data Analysis (8th ed.). Cengage.
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2018). The results of PLS-SEM. European Business Review, 31(1), 2–24.
References
Abdillah, W., & Jogianto. (2015). Partial Least Squares (PLS): An Alternative to Structural Equation Modeling in Business Research. Yogyakarta: Andi Publisher.
Al-Azzam, M. K., & Alazzam, M. B. (2019). Smart city and smart health framework: Challenges and opportunities. International Journal of Advanced Computer Science and Applications, 10(2), 171–176. https://doi.org/10.14569/IJACSA.2019.0100223
Al-Azzam, M. K., Alazzam, M. B., & al-Manasra, M. K. (2019). mHealth for decision-making support: A case study of eHealth in the public sector. International Journal of Advanced Computer Science and Applications, 10(5), 381–387. https://doi.org/10.14569/IJACSA.2019.0100547
Alfansi, L., & Atmaja, F. T. (2009). Service failure and complaint behavior in the public hospital industry: The Indonesian experience. Journal of Nonprofit and Public Sector Marketing, 21(3), 309–325. https://doi.org/10.1080/10495140802644554
Alfansi, L., & Daulay, M. Y. I. (2021). Factors affecting the use of e-money in the millennial generation: Research model UTAUT 2. Jurnal Manajemen Dan Pemasaran Jasa, 14(1), 109–122. https://doi.org/10.25105/jmpj.v14i1.8212
Alpay, L. L., Henkemans, O. B., Otten, W., Rövekamp, T. A. J. M., & Dumay, A. C. M. (2010). E-health applications and services for patient empowerment: Directions for best practices in the Netherlands. Telemedicine and E- Health, 16(7), 787–791. https://doi.org/10.1089/tmj.2009.0156
Ami-Narh, J. T., & Williams, P. A. H. (2012). A revised UTAUT model to investigate e-health acceptance among health professionals in Africa. Journal of Emerging Trends in Computing and Information Sciences, 3(10), 1383–1391.
Annur, C. M. (2022). Survey: Halodoc is the most frequently used health application among mothers in Indonesia. Retrieved from https://databoks.katadata.co.id/datapublish/2021/12/22/survei-halodoc-jadi-aplikasi-kesehatan- paling-sering-digunakan-ibu-di-indonesia (Accessed June 5, 2022, 21:25 WIB).
Angelia, D. (2021). 10 most frequently used mental health service applications in Indonesian society in 2022. Retrieved from https://goodstats.id/article/10-aplikasi-layanan-kesehatan-mental-paling-sering-digunakan- masyarakat-indonesia-2022-6Fir1 (Accessed June 10, 2023).
Baudier, P., Kondrateva, G., & Ammi, C. (2020). The future of telemedicine cabins? The case of French students’ acceptability. Futures, 122, 102595. https://doi.org/10.1016/j.futures.2020.102595
Bol, N., Helberger, N., & Weert, J. C. M. (2018). Differences in mobile health app use: A source of new digital inequalities? Information Society, 34(3), 183–193. https://doi.org/10.1080/01972243.2018.1438550
Boontarig, W., Chutimaskul, W., Chongsuphajaisiddhi, V., & Papasratorn, B. (2012). Factors influencing the intention of Thai elderly to use smartphones for e-health services. 2012 IEEE Symposium on Humanities, Science and Engineering Research, 479–483. https://doi.org/10.1109/SHUSER.2012.6268881
Brauner, P., Van Heek, J., & Ziefle, M. (2017). Age, gender, and technology attitude as factors for acceptance of smart interactive textiles in home environments: Towards a smart textile technology acceptance model. ICT4AWE 2017 - Proceedings of the 3rd International Conference on Information and Communication Technologies for Ageing Well and e-Health, 13–24. https://doi.org/10.5220/0006255600130024
Chen, Q. L., & Zhou, Z. H. (2016). Unusual formations of superoxo heptaoxomolybdates from peroxo molybdates.
Inorganic Chemistry Communications, 67(3), 95–98. https://doi.org/10.1016/j.inoche.2016.03.015
De Veer, A. J. E., Peeters, J. M., Brabers, A. E. M., Schellevis, F. G., Rademakers, J. J. D. J. M., & Francke, A. L. (2015). Determinants of the intention to use e-health by community-dwelling older people. BMC Health Services Research, 15(1), 1–9. https://doi.org/10.1186/s12913-015-0765-8
Duarte, P., & Pinho, J. C. (2019). A mixed methods UTAUT2-based approach to assess mobile health adoption.
Journal of Business Research, 102(February), 140–150. https://doi.org/10.1016/j.jbusres.2019.05.022
Edeh, E., Lo, W.-J., & Khojasteh, J. (2022). Review of Partial Least Squares Structural Equation Modeling (PLS- SEM) using R: A workbook. Structural Equation Modeling: A Multidisciplinary Journal. https://doi.org/10.1080/10705511.2022.2108813
Enaizan, O., Eneizan, B., Almaaitah, M., Al-Radaideh, A. T., & Saleh, A. M. (2020). Effects of privacy and security on the acceptance and usage of electronic medical records: The mediating role of trust based on multiple perspectives. Informatics in Medicine Unlocked, 21, 100450. https://doi.org/10.1016/j.imu.2020.100450
Enaizan, O., Zaidan, A. A., Alwi, N. H. M., Zaidan, B. B., Alsalem, M. A., Albahri, O. S., & Albahri, A. S. (2020). Electronic medical record systems: Decision support examination framework for individual security and privacy concerns using multi-perspective analysis. Health and Technology, 10(3), 795–822. https://doi.org/10.1007/s12553-018-0278-7
Expectancy, P. (2005). The UTAUT questionnaire items. In E-Health Systems Diffusion and Use: The Innovation, the User and the USE IT Model (pp. 93–97). https://doi.org/10.4018/978-1-59140-423-1.ch005
Ghozali, I., & Latan, H. (2020). Partial Least Squares: Concepts, Techniques, and Applications Using SmartPLS 3.0 for Empirical Research (2nd ed.). Semarang: Undip Publishing.
Ghozali, I. (2021). Partial Least Squares: Concepts, Techniques, and Applications Using SmartPLS 3.2.9 for Empirical Research (3rd ed.). Semarang: Undip Publishing.
Ghozali, I., & Kusumadewi, K. A. (2023). Partial Least Squares: Concepts, Techniques, and Applications Using SmartPLS 4.0 for Empirical Research (3rd ed.). Semarang: Undip Publishing.
Goleman, D., Boyatzis, R., McKee, A., & Perdana. (2018). Blueprint for the Indonesian Payment System 2025. Bank Indonesia: Navigating the National Payment System in the Digital Era. Journal of Chemical Information and Modeling, 53(9), 1689–1699.
Gu, D., Khan, S., Khan, I. U., Khan, S. U., Xie, Y., Li, X., & Zhang, G. (2021). Assessing the adoption of e-health technology in a developing country: An extension of the UTAUT model. SAGE Open, 11(3). https://doi.org/10.1177/21582440211027565
Guo, X., Han, X., Zhang, X., Dang, Y., & Chen, C. (2015). Investigating m-health acceptance from a protection motivation theory perspective: Gender and age differences. Telemedicine and E-Health, 21(8), 661–669. https://doi.org/10.1089/tmj.201
Hair, J. (2017). Multivariate Data Analysis (8th ed.). Cengage.
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2018). The results of PLS-SEM. European Business Review, 31(1), 2–24.
Al-Azzam, M. K., & Alazzam, M. B. (2019). Smart city and smart health framework: Challenges and opportunities. International Journal of Advanced Computer Science and Applications, 10(2), 171–176. https://doi.org/10.14569/IJACSA.2019.0100223
Al-Azzam, M. K., Alazzam, M. B., & al-Manasra, M. K. (2019). mHealth for decision-making support: A case study of eHealth in the public sector. International Journal of Advanced Computer Science and Applications, 10(5), 381–387. https://doi.org/10.14569/IJACSA.2019.0100547
Alfansi, L., & Atmaja, F. T. (2009). Service failure and complaint behavior in the public hospital industry: The Indonesian experience. Journal of Nonprofit and Public Sector Marketing, 21(3), 309–325. https://doi.org/10.1080/10495140802644554
Alfansi, L., & Daulay, M. Y. I. (2021). Factors affecting the use of e-money in the millennial generation: Research model UTAUT 2. Jurnal Manajemen Dan Pemasaran Jasa, 14(1), 109–122. https://doi.org/10.25105/jmpj.v14i1.8212
Alpay, L. L., Henkemans, O. B., Otten, W., Rövekamp, T. A. J. M., & Dumay, A. C. M. (2010). E-health applications and services for patient empowerment: Directions for best practices in the Netherlands. Telemedicine and E- Health, 16(7), 787–791. https://doi.org/10.1089/tmj.2009.0156
Ami-Narh, J. T., & Williams, P. A. H. (2012). A revised UTAUT model to investigate e-health acceptance among health professionals in Africa. Journal of Emerging Trends in Computing and Information Sciences, 3(10), 1383–1391.
Annur, C. M. (2022). Survey: Halodoc is the most frequently used health application among mothers in Indonesia. Retrieved from https://databoks.katadata.co.id/datapublish/2021/12/22/survei-halodoc-jadi-aplikasi-kesehatan- paling-sering-digunakan-ibu-di-indonesia (Accessed June 5, 2022, 21:25 WIB).
Angelia, D. (2021). 10 most frequently used mental health service applications in Indonesian society in 2022. Retrieved from https://goodstats.id/article/10-aplikasi-layanan-kesehatan-mental-paling-sering-digunakan- masyarakat-indonesia-2022-6Fir1 (Accessed June 10, 2023).
Baudier, P., Kondrateva, G., & Ammi, C. (2020). The future of telemedicine cabins? The case of French students’ acceptability. Futures, 122, 102595. https://doi.org/10.1016/j.futures.2020.102595
Bol, N., Helberger, N., & Weert, J. C. M. (2018). Differences in mobile health app use: A source of new digital inequalities? Information Society, 34(3), 183–193. https://doi.org/10.1080/01972243.2018.1438550
Boontarig, W., Chutimaskul, W., Chongsuphajaisiddhi, V., & Papasratorn, B. (2012). Factors influencing the intention of Thai elderly to use smartphones for e-health services. 2012 IEEE Symposium on Humanities, Science and Engineering Research, 479–483. https://doi.org/10.1109/SHUSER.2012.6268881
Brauner, P., Van Heek, J., & Ziefle, M. (2017). Age, gender, and technology attitude as factors for acceptance of smart interactive textiles in home environments: Towards a smart textile technology acceptance model. ICT4AWE 2017 - Proceedings of the 3rd International Conference on Information and Communication Technologies for Ageing Well and e-Health, 13–24. https://doi.org/10.5220/0006255600130024
Chen, Q. L., & Zhou, Z. H. (2016). Unusual formations of superoxo heptaoxomolybdates from peroxo molybdates.
Inorganic Chemistry Communications, 67(3), 95–98. https://doi.org/10.1016/j.inoche.2016.03.015
De Veer, A. J. E., Peeters, J. M., Brabers, A. E. M., Schellevis, F. G., Rademakers, J. J. D. J. M., & Francke, A. L. (2015). Determinants of the intention to use e-health by community-dwelling older people. BMC Health Services Research, 15(1), 1–9. https://doi.org/10.1186/s12913-015-0765-8
Duarte, P., & Pinho, J. C. (2019). A mixed methods UTAUT2-based approach to assess mobile health adoption.
Journal of Business Research, 102(February), 140–150. https://doi.org/10.1016/j.jbusres.2019.05.022
Edeh, E., Lo, W.-J., & Khojasteh, J. (2022). Review of Partial Least Squares Structural Equation Modeling (PLS- SEM) using R: A workbook. Structural Equation Modeling: A Multidisciplinary Journal. https://doi.org/10.1080/10705511.2022.2108813
Enaizan, O., Eneizan, B., Almaaitah, M., Al-Radaideh, A. T., & Saleh, A. M. (2020). Effects of privacy and security on the acceptance and usage of electronic medical records: The mediating role of trust based on multiple perspectives. Informatics in Medicine Unlocked, 21, 100450. https://doi.org/10.1016/j.imu.2020.100450
Enaizan, O., Zaidan, A. A., Alwi, N. H. M., Zaidan, B. B., Alsalem, M. A., Albahri, O. S., & Albahri, A. S. (2020). Electronic medical record systems: Decision support examination framework for individual security and privacy concerns using multi-perspective analysis. Health and Technology, 10(3), 795–822. https://doi.org/10.1007/s12553-018-0278-7
Expectancy, P. (2005). The UTAUT questionnaire items. In E-Health Systems Diffusion and Use: The Innovation, the User and the USE IT Model (pp. 93–97). https://doi.org/10.4018/978-1-59140-423-1.ch005
Ghozali, I., & Latan, H. (2020). Partial Least Squares: Concepts, Techniques, and Applications Using SmartPLS 3.0 for Empirical Research (2nd ed.). Semarang: Undip Publishing.
Ghozali, I. (2021). Partial Least Squares: Concepts, Techniques, and Applications Using SmartPLS 3.2.9 for Empirical Research (3rd ed.). Semarang: Undip Publishing.
Ghozali, I., & Kusumadewi, K. A. (2023). Partial Least Squares: Concepts, Techniques, and Applications Using SmartPLS 4.0 for Empirical Research (3rd ed.). Semarang: Undip Publishing.
Goleman, D., Boyatzis, R., McKee, A., & Perdana. (2018). Blueprint for the Indonesian Payment System 2025. Bank Indonesia: Navigating the National Payment System in the Digital Era. Journal of Chemical Information and Modeling, 53(9), 1689–1699.
Gu, D., Khan, S., Khan, I. U., Khan, S. U., Xie, Y., Li, X., & Zhang, G. (2021). Assessing the adoption of e-health technology in a developing country: An extension of the UTAUT model. SAGE Open, 11(3). https://doi.org/10.1177/21582440211027565
Guo, X., Han, X., Zhang, X., Dang, Y., & Chen, C. (2015). Investigating m-health acceptance from a protection motivation theory perspective: Gender and age differences. Telemedicine and E-Health, 21(8), 661–669. https://doi.org/10.1089/tmj.201
Hair, J. (2017). Multivariate Data Analysis (8th ed.). Cengage.
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2018). The results of PLS-SEM. European Business Review, 31(1), 2–24.