MODEL OF INTENTION TO USE E-HEALTH APPLICATION (MOBILE JKN) DURING THE COVID-19 PANDEMIC IN THE SPECIAL CAPITAL REGION OF JAKARTA
DOI:
https://doi.org/10.31933/dijms.v3i2.1048Keywords:
Perceived Ease of Use, Perceived Usefulness, Social Influence, Attitude, Intention to Use.Abstract
This study aims to analyse the relationship between Perceived Ease of Use, Perceived Usefulness, Social Influence and Attitude to Intention to Use. This study also examines the mediating effect of Attitude on Perceived Ease of Use, Perceived Usefulness, Social Influence and Intention to Use. The study involved 384 users of Mobile JKN application. Researchers used Partial Least Square (PLS) as the technique to analyse measurements and structural models. The results of this study indicate that Perceived Usefulness and Social Influence do not have a significant effect on Intention to Use but Attitude has a significant influence in mediating the relationship between Perceived Ease of Use, Perceived Usefulness and Social Influence on Intention to Use. For theoretical and practical implications, researchers need to test Intention to Use with other variables, and the marketers need to pay attention to users who already have the habit and the experience of using e-health applications to maintain their retention.
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Alanza, Z., Loveldy, C., Ahmad, T., Ismail, T., & Siti, S. (2021). Examining the Attitude-Behavior Gap and Adoption Intention of SHS Technology?: The Role of Social Influence. 3(1), 14–24. https://doi.org/10.35313/ijabr.v3i1.119.
Bailey, A. A., Pentina, I., Mishra, A. S., & Ben Mimoun, M. S. (2017). Mobile payments adoption by US consumers: an extended TAM. International Journal of Retail and Distribution Management, 45(6), 626–640. https://doi.org/10.1108/IJRDM-08-2016-0144.
Bouteraa, M., & Al-Aidaros, A.-H. (2020). The Role of Attitude As Mediator In the Intention to Have Islamic Will. International Journal of Advanced Research in Economics and Finance, 2(1), 22–37
Davis, F. D. (1986). A technology acceptance model for empirically testing new end-user information systems: Theory and results. Management, Ph.D. 291. https://doi.org/oclc/56932490.
Davis, F. D. (1989). Perceived Ease of Use, perceived ease of use, and user acceptance of information technology. MIS Quarterly: Management Information Systems, 13(3), 319–339. https://doi.org/10.2307/249008.
Ghozali, Imam. 2014. Structural Equation Modeling, Metode Alternatif dengan Partial Least Square (PLS). Edisi 4. Semarang : Badan Penerbit Universitas Diponegoro.
Ghozali, Imam dan Latan, Hengky. (2015). Partial Least Square Konsep Teknik dan Aplikasi Menggunakan Program SmartPLS 3.0 (2nd Edition). Semarang: Badan Penerbit Universitas Diponegoro.
Labrique, A., Vasudevan, L., Chang, L. W., & Mehl, G. (2013). H_pe for mHealth: More “ y” or “ o” on the horizon? International Journal of Medical Informatics, 82(5), 467–469. https://doi.org/10.1016/j.ijmedinf.2012.11.016.
Laporan Survei Internet APJII 2019-2020 (Q2). Asosiasi Penyelenggara Jasa Internet Indonesia. https://apjii.or.id/survei
Malhotra, Y., & Galletta, D. F. (1999). Extending the Technology Acceptance Model to account for social influence: Theoretical bases and empirical validation. Proceedings of the Hawaii International Conference on System Sciences, 00(c), 5. https://doi.org/10.1109/hicss.1999.772658.
Mariana Hotria, Kompas.com 2020, ‘Mobile JKN, Jawaban Kemudahan Layanan Kesehatan di Masa Pandemi’, diakses pada 1April 2021, https://nasional.kompas.com/read/2020/10/20/12110021/mobile-jkn-jawaban-kemudahan-layanan-kesehatan-di-masa-pandemi
Muñoz-Leiva, F., Climent-Climent, S., & Liébana-Cabanillas, F. (2017). Determinants of Intention to Use the mobile banking apps: apps: An extension of the classic TAM model. Spanish Journal of Marketing - ESIC, 21(1), 25–38. https://doi.org/10.1016/j.sjme.2016.12.001.
Peraturan Pemerintah, Nomor 21 tahun 2020, ‘Pembatasan Sosial Berskala Besar (PSBB) dalam Rangka Percepatan Penangan Corona Virus Desease 2019 (COVID-19)’, Lembar Negara Republik Indonesia Tahun 2020 Nomor 91.
Pikkarainen, T., Pikkarainen, K., Karjaluoto, H., & Pahnila, S. (2004). Consumer acceptance of online banking: An extension of the technology acceptance model. Internet Research, 14(3), 224–235. https://doi.org/10.1108/10662240410542652.
Rachbini, 2020, Penjaminan Kesehatan di Indonesia: Sejarah dan Transformasi BPJS Kesehatan, BPJS Kesehatan, Indonesia
Riza, Alex Fahrur Hafizi, Muhammad Riza. (2019). Customers Attitude toward Islamic mobile banking in Indonesia: Implementation of TAM. Asian Journal of Islamic Management (AJIM).
Scherer, R., Siddiq, F., & Tondeur, J. (2019). The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers’ adoption of digital technology in education. Computers and Education, 128, 13–35. https://doi.org/10.1016/j.compedu.2018.09.009
Sigar, J. F. (2016). the Influence of Perceived Ease of Use, Perceived Ease of Use and Perceived Enjoyment To Intention to Use Electronic Money in Manado. Jurnal EMBA: Jurnal Riset Ekonomi, Manajemen, Bisnis Dan Akuntansi, 4(2), 498–507. https://doi.org/10.35794/emba.v4i2.13083.
Suki, N. M., & Ramayah, T. (2010). User acceptance of the e-Government services in Malaysia: Structural Equation Modelling approach. Interdisciplinary Journal of Information, Knowledge, and Management, 5(November 2014), 395–413. https://doi.org/10.28945/1308.
Velicia-Martin, F., Cabrera-Sanchez, J. P., Gil-Cordero, E., & Palos-Sanchez, P. R. (2021). Researching COVID-19 tracing app acceptance: incorporating theory from the technological acceptance model. PeerJ Computer Science, 7(December 2019), 1–20. https://doi.org/10.7717/peerj-cs.316.
Venkatesh, V., & Morris, M. G. (2000). Why don’t men ever stop to ask for directions? Gender, social influence, and their role in technology acceptance and usage behavior. MIS Quarterly: Management Information Systems, 24(1), 115–136. https://doi.org/10.2307/3250981.
Wu, B., & Chen, X. (2016). Continuance Intention to Use MOOCs: Integrating the technology acceptance model (TAM) and task technology fit (TTF) model. Computers in Human Behavior, 67, 221–232. https://doi.org/10.1016/j.chb.2016.10.028.
Zayyad, M. A., & Toycan, M. (2018). Factors affecting sustainable adoption of e-health technology in developing countries: An exploratory survey of Nigerian hospitals from the perspective of healthcare professionals. PeerJ, 2018(3). https://doi.org/10.7717/peerj.4436.
Zhang, X., Han, X., Dang, Y., Meng, F., Guo, X., & Lin, J. (2017). User acceptance of mobile health services from users’ perspectives: The role of self-efficacy and response-efficacy in technology acceptance. Informatics for Health and Social Care,
Zhao, J., Fang, S., & Jin, P. (2018). Modeling and quantifying user acceptance of personalized business modes based on TAM, trust and Attitude. Sustainability. (Switzerland), 10(2), 1–26. https://doi.org/10.3390/su10020356.
Zhao, Y., Ni, Q., & Zhou, R. (2017). What factors influence the mobile health service adoption? A meta-analysis and the moderating role of age. International Journal of Information Management, 43(August), 342–350. https://doi.org/10.1016/j.ijinfomgt.2017.08.006.
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Published
2021-11-30
How to Cite
Akhmad T, R. ., & Didik J, R. (2021). MODEL OF INTENTION TO USE E-HEALTH APPLICATION (MOBILE JKN) DURING THE COVID-19 PANDEMIC IN THE SPECIAL CAPITAL REGION OF JAKARTA. Dinasti International Journal of Management Science, 3(2), 359–373. https://doi.org/10.31933/dijms.v3i2.1048
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