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Published
13 August 2022
Keywords:
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health services, structural equation modeling, technology adoption, telemedicine, usage intention
Abstract
The Covid-19 pandemic has accelerated the adoption of technology in various sectors, one of which is the healthcare industry. Telemedicine users increased during the Covid-19 pandemic, but only 10% of Indonesia's population. This study aims to analyze the factors influencing the intention to use telemedicine. This research uses a descriptive quantitative method. The sampling technique used non-probability sampling with a voluntary sampling technique. Data analysis applied Structural Equation Modeling using LISREL version 8.8. Data were obtained from 225 respondents in Greater Jakarta and Greater Bandung from January to March 2022, but only 192 were included in the analysis. The results showed that the intention to use telemedicine was directly influenced by attitude (A) and indirectly influenced by interrelated variables such as trust (T), perceived ease of use (PEU), perceived usefulness (PU), information quality (IQ), service quality (SrQ), and system quality (SQ). Implications that telemedicine service providers can apply to increase the use of telemedicine are to create the best experience, user friendly, provide complete information, and increase the reliability of information systems.
References
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Balogh, Z., & Mészáros, K. (2020). Consumer perceived risk by online purchasing: the experiences in Hungary. Naše Gospodarstvo Our Economy, 66 (3), 14-21. doi: https://doi.org/10.2478/ngoe-2020-0014
Bokolo, A. J. (2021). Application of telemedicine and eHealth technology for clinical services in response to the Covid-19 pandemic. Journal of Health and Technology, 11(2), 359–366. doi: https://doi.org/10.1007/s12553-020-00516-4
[BPS] Badan Pusat Statistik. (2021). Portrait of the 2020 Jakarta Population Census (Potret Sensus Penduduk Jakarta 2020). Jakarta(ID): BPS.
CNN Indonesia. (2020) Telemedical Application Visits Soared 600 Percent During Covid. Retrieved from https://www.cnnindonesia.com/technology/20200822125041-52-538097/kunjungan-application-telemedis-melonjak-600-persen-saat-covid.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of Information Technology. MIS Quarterly, 13(3), 319-340. doi: https://doi.or/10.2307/249008.
Demoulin, N. T. M., & Coussement, K. (2018). Acceptance of text-mining systems: the signaling role of information quality. Journals of Information Management, 57(1), 103120. doi: https://doi.org/10.1016/j.im2018.10.006
Faradila, R. S. N., & Soesanto, H. (2016). Analysis of the effect of perceived ease of use and perceived benefits on buying interest with trust as an intervening variable (Study on berrybenka.com online shop visitors among Diponegoro University Students) (analisis pengaruh perceived ease of use dan perceived benefit terhadap minat beli dengan kepercayaan sebagai variabel intervening (Studi pada pengunjung toko online berrybenka.com di kalangan Mahasiswa Universitas Diponegoro). Journals of Management and Organizational Studies, 5(3), 239-250.
Gariboldi, M. I., Lin, V., Bland, J., Auplish, M., & Cawthorne, A. (2021). Foresight in the time of COVID-19. The Lancet Regional Health - Western Pacific, 6, 1-6. doi: https://doi.org/10.1016/j.lanwpc.2020.100049
Ghaddar, S., Vatcheva, K. P., Alvarado, S.G., & Mykyta, L. (2020). Understanding the intention to use telehealth services in underserved Hispanic border communities: a cross-sectional study. Medical Internet Research, 22(9), e21012. doi: https://doi.org/10.2196/21012
Guo, X., Chen, S., Zhang, X., Ju, X., & Wang, X. (2020). Exploring patients' intentions for continuous usage services: elaboration-likelihood perspective study. JMIR mHealth and UnHealth, 8(4), e17258. doi: https://doi.org/10.2196/17258
Guru, S., Nenavani, J., Patel, V., & Bhatt, N. (2020). Ranking of perceived risks in online shopping decision. DECISION, 47(2), 137-152.
Indria, D., Alajlani, M., Fraser, H. S. F. (2020). Clinicians perceptions of a telemedicine system: a mixed method study of Makassar City, Indonesia. BMC Medical Informatics and Decision Making, 20(1), 233. doi: https://doi.org/10.1186/s12911-020-01234-7
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. doi: https://doi.org/10.1108/IJPHM-06-2019-0041
Katadata. (2020). What are the opportunities for telemedicine to Improve RI's health services?. Retrieved from https://katadata.co.id/muhammadridhoi/analysisdata/5fb4b30d9c3cd/carapeluangtelemedicine-benahi-jasa-kesehatan-ri
Kompas.com. (2020, April 27). During the coronavirus outbreak, telemedicine users reached 300,000. Retrieved from https://nasional.kompas.com/read/2020/04/27/19033501/selama-wabah-virus-corona-user-telemedicine-reach-300000
Kwon, S. J., Park, E., & Kim, K. J. (2014). What drives successful social networking services? A comparative analysis of user acceptance of Facebook and Twitter. The Social Science Journal, 51(4), 534-544. doi: https://doi.org/10.1016/j.soscij.2014.04.005
Ministry of Health of the Republic of Indonesia. (2021). Health Profile of Indonesia 2020. Jakarta(ID): Ministry of Health of the Republic of Indonesia.
Lee, S., Choi, J., & Sawng, Y. (2019). Foresight of promising technologies for healthcare-iot convergence service by patent analysis. Scientific & Industrial Research, 78, 489-494.
Lin, W. R., Yang, F. J., & Chang, Y. H.(2020). The impact of risk factors and attitudes on use of mobile payment intentions. Journal of Accounting Finance & Management Strategy, 15(1), 129-158.
Liou, D. K., Hsu, L. C., & Chih, W. H. (2015). Understanding broadband television users' continuance intention to use. Journals of Industrial Management and Data Systems, 115(2), 210-234.
Lv, W. (2021). Analysis on the influencing factors of users' usage intentions and user behavior patterns in online medical community under COVID-19. IOP Conf Series: Earth and Environmental Science, 692, 1-13. doi: https://doi.org/10.1088/1751315/692/3/032112
Nurzanita, R., & Marlena, N. (2020). The effect of perceived benefits on the decision to use gopay in Surabaya with trust as an intervening variable. Accountable, 17(2), 277-288.
Ozlena, M. K., & Djedovic, I. (2017). Online banking acceptance: the influence of perceived system security on perceived system quality. Journal of Accounting and Management Information Systems, 16(1),164-178.
Pan, M., & Gao, W. (2021). Determinants of the behavioral intention to use a mobile nursing application by nurses in China. BMC Health Services Research, 21(228), 1-11.
Pappan, N., Benkhadra, R., Papincak D., Ashker, K., Uchin, J., Sidique, N., Pirani, Z., & Clemenza, P. (2021). Values and limits of telemedicine: a case report. SN Comprehensive Clinical Medicine Journal, 3(1), 317-319. doi: 10.1007/s42399-020-00725-y
Pasaribu, K.F., Arisjulyanto, D., & Hikmatushaliha, B.T. (2018). Development of telemedicine in overcoming connectivity and accessibility of health services (Pengembangan telemedicine dalam mengatasi konektivitas dan aksesibilitas pelayanan kesehatan). Berita Kedokteran Masyarakat/Public Medical News, 34(11), 15-17.
Petrovski, B.E., Lumi, X., Znaor, L., Ivastinovic, D., Confalonieri, F., Petrovic, M.G., & Petrovski, G. (2020). Reorganize and survive - a recommendation for a healthcare services affected by COVID-19- the ophthalmology experience. Eye Journal, 34, 1177-1179. doi: https://doi.org/10.1038/s41433-020-0871-7
Prakosa, A., & Sumantika, A. (2020). An analysis of online shoppers' acceptance and trust toward the electronic marketplace using the TAM model. Journals of Physics: Conference Series, 1823, 1-7. doi: https://doi.org/10.1088/17426596/1823/1/012008
Priyadarshini, C., Sreejesh, S., & Anusree, M.R. (2017). Effect of information quality of employment website on attitude toward the website. International Journal of Manpower, 38(5), 729-745. doi: https://doi.org/10.1108/IJM-12-2015-0235
Riana, D., Hidayanto, A.N, Hadianti, S., & Napitupulu, D. (2021). Integrative factors of e-Health laboratory adoption: a case of Indonesia. Future Internet, 13 (26), 1-27. doi: https://doi.org/10.3390/fi13020026
Saigi-Rubio, F., Jimenez-Zarco, A., & Torrent-Sellens, A. (2016). Determinants of intention to use telemedicine: evidence from primary care physicians. International Journal of Technology Assessment in Health Care, 32(1-2), 29-36. doi: https://doi.org/10.1017/s02664623160000015
Zhang, X., Liu, S., Zhang, Y., & Wang, J. (2020). Mobile health service adoption in China: integration of theory of planned behavior, protection motivation theory and personal health differenced. Online Information Review, 44(1), 1-23. doi: http://dx.doi.org/10.1108/OIR-11-2016-0339
An, M. H., You, S. C., Park, R. W., & Lee, S. (2021). Using an extended Technology Acceptance Model to understand the factors influencing telehealth utilization after flattening the Covid-19 curve in South Korea: cross-sectional survey study. JMIR Med Inform, 9(1), 1-15.
Balogh, Z., & Mészáros, K. (2020). Consumer perceived risk by online purchasing: the experiences in Hungary. Naše Gospodarstvo Our Economy, 66 (3), 14-21. doi: https://doi.org/10.2478/ngoe-2020-0014
Bokolo, A. J. (2021). Application of telemedicine and eHealth technology for clinical services in response to the Covid-19 pandemic. Journal of Health and Technology, 11(2), 359–366. doi: https://doi.org/10.1007/s12553-020-00516-4
[BPS] Badan Pusat Statistik. (2021). Portrait of the 2020 Jakarta Population Census (Potret Sensus Penduduk Jakarta 2020). Jakarta(ID): BPS.
CNN Indonesia. (2020) Telemedical Application Visits Soared 600 Percent During Covid. Retrieved from https://www.cnnindonesia.com/technology/20200822125041-52-538097/kunjungan-application-telemedis-melonjak-600-persen-saat-covid.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of Information Technology. MIS Quarterly, 13(3), 319-340. doi: https://doi.or/10.2307/249008.
Demoulin, N. T. M., & Coussement, K. (2018). Acceptance of text-mining systems: the signaling role of information quality. Journals of Information Management, 57(1), 103120. doi: https://doi.org/10.1016/j.im2018.10.006
Faradila, R. S. N., & Soesanto, H. (2016). Analysis of the effect of perceived ease of use and perceived benefits on buying interest with trust as an intervening variable (Study on berrybenka.com online shop visitors among Diponegoro University Students) (analisis pengaruh perceived ease of use dan perceived benefit terhadap minat beli dengan kepercayaan sebagai variabel intervening (Studi pada pengunjung toko online berrybenka.com di kalangan Mahasiswa Universitas Diponegoro). Journals of Management and Organizational Studies, 5(3), 239-250.
Gariboldi, M. I., Lin, V., Bland, J., Auplish, M., & Cawthorne, A. (2021). Foresight in the time of COVID-19. The Lancet Regional Health - Western Pacific, 6, 1-6. doi: https://doi.org/10.1016/j.lanwpc.2020.100049
Ghaddar, S., Vatcheva, K. P., Alvarado, S.G., & Mykyta, L. (2020). Understanding the intention to use telehealth services in underserved Hispanic border communities: a cross-sectional study. Medical Internet Research, 22(9), e21012. doi: https://doi.org/10.2196/21012
Guo, X., Chen, S., Zhang, X., Ju, X., & Wang, X. (2020). Exploring patients' intentions for continuous usage services: elaboration-likelihood perspective study. JMIR mHealth and UnHealth, 8(4), e17258. doi: https://doi.org/10.2196/17258
Guru, S., Nenavani, J., Patel, V., & Bhatt, N. (2020). Ranking of perceived risks in online shopping decision. DECISION, 47(2), 137-152.
Indria, D., Alajlani, M., Fraser, H. S. F. (2020). Clinicians perceptions of a telemedicine system: a mixed method study of Makassar City, Indonesia. BMC Medical Informatics and Decision Making, 20(1), 233. doi: https://doi.org/10.1186/s12911-020-01234-7
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. doi: https://doi.org/10.1108/IJPHM-06-2019-0041
Katadata. (2020). What are the opportunities for telemedicine to Improve RI's health services?. Retrieved from https://katadata.co.id/muhammadridhoi/analysisdata/5fb4b30d9c3cd/carapeluangtelemedicine-benahi-jasa-kesehatan-ri
Kompas.com. (2020, April 27). During the coronavirus outbreak, telemedicine users reached 300,000. Retrieved from https://nasional.kompas.com/read/2020/04/27/19033501/selama-wabah-virus-corona-user-telemedicine-reach-300000
Kwon, S. J., Park, E., & Kim, K. J. (2014). What drives successful social networking services? A comparative analysis of user acceptance of Facebook and Twitter. The Social Science Journal, 51(4), 534-544. doi: https://doi.org/10.1016/j.soscij.2014.04.005
Ministry of Health of the Republic of Indonesia. (2021). Health Profile of Indonesia 2020. Jakarta(ID): Ministry of Health of the Republic of Indonesia.
Lee, S., Choi, J., & Sawng, Y. (2019). Foresight of promising technologies for healthcare-iot convergence service by patent analysis. Scientific & Industrial Research, 78, 489-494.
Lin, W. R., Yang, F. J., & Chang, Y. H.(2020). The impact of risk factors and attitudes on use of mobile payment intentions. Journal of Accounting Finance & Management Strategy, 15(1), 129-158.
Liou, D. K., Hsu, L. C., & Chih, W. H. (2015). Understanding broadband television users' continuance intention to use. Journals of Industrial Management and Data Systems, 115(2), 210-234.
Lv, W. (2021). Analysis on the influencing factors of users' usage intentions and user behavior patterns in online medical community under COVID-19. IOP Conf Series: Earth and Environmental Science, 692, 1-13. doi: https://doi.org/10.1088/1751315/692/3/032112
Nurzanita, R., & Marlena, N. (2020). The effect of perceived benefits on the decision to use gopay in Surabaya with trust as an intervening variable. Accountable, 17(2), 277-288.
Ozlena, M. K., & Djedovic, I. (2017). Online banking acceptance: the influence of perceived system security on perceived system quality. Journal of Accounting and Management Information Systems, 16(1),164-178.
Pan, M., & Gao, W. (2021). Determinants of the behavioral intention to use a mobile nursing application by nurses in China. BMC Health Services Research, 21(228), 1-11.
Pappan, N., Benkhadra, R., Papincak D., Ashker, K., Uchin, J., Sidique, N., Pirani, Z., & Clemenza, P. (2021). Values and limits of telemedicine: a case report. SN Comprehensive Clinical Medicine Journal, 3(1), 317-319. doi: 10.1007/s42399-020-00725-y
Pasaribu, K.F., Arisjulyanto, D., & Hikmatushaliha, B.T. (2018). Development of telemedicine in overcoming connectivity and accessibility of health services (Pengembangan telemedicine dalam mengatasi konektivitas dan aksesibilitas pelayanan kesehatan). Berita Kedokteran Masyarakat/Public Medical News, 34(11), 15-17.
Petrovski, B.E., Lumi, X., Znaor, L., Ivastinovic, D., Confalonieri, F., Petrovic, M.G., & Petrovski, G. (2020). Reorganize and survive - a recommendation for a healthcare services affected by COVID-19- the ophthalmology experience. Eye Journal, 34, 1177-1179. doi: https://doi.org/10.1038/s41433-020-0871-7
Prakosa, A., & Sumantika, A. (2020). An analysis of online shoppers' acceptance and trust toward the electronic marketplace using the TAM model. Journals of Physics: Conference Series, 1823, 1-7. doi: https://doi.org/10.1088/17426596/1823/1/012008
Priyadarshini, C., Sreejesh, S., & Anusree, M.R. (2017). Effect of information quality of employment website on attitude toward the website. International Journal of Manpower, 38(5), 729-745. doi: https://doi.org/10.1108/IJM-12-2015-0235
Riana, D., Hidayanto, A.N, Hadianti, S., & Napitupulu, D. (2021). Integrative factors of e-Health laboratory adoption: a case of Indonesia. Future Internet, 13 (26), 1-27. doi: https://doi.org/10.3390/fi13020026
Saigi-Rubio, F., Jimenez-Zarco, A., & Torrent-Sellens, A. (2016). Determinants of intention to use telemedicine: evidence from primary care physicians. International Journal of Technology Assessment in Health Care, 32(1-2), 29-36. doi: https://doi.org/10.1017/s02664623160000015
Zhang, X., Liu, S., Zhang, Y., & Wang, J. (2020). Mobile health service adoption in China: integration of theory of planned behavior, protection motivation theory and personal health differenced. Online Information Review, 44(1), 1-23. doi: http://dx.doi.org/10.1108/OIR-11-2016-0339
Authors
Khotimah F. K. H., Fahmi I., & Hartono S. (2022). The Antecedents of Intention to Use Telemedicine. Journal of Consumer Sciences, 7(2), 97-114. https://doi.org/10.29244/jcs.7.2.97-115
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