Analisis Technology Acceptance Model (TAM) untuk Mengetahui Persepsi Mahasiswa Unissula pada Aplikasi Kesehatan Halodoc

Vita Qiftiyah; Ardian Adhiatma

doi:10.53697/emak.v7i2.3813

Authors

Vita Mariyatul Qiftiyah Universitas Islam Sultan Agung
Ardian Adhiatma Universitas Islam Sultan Agung

DOI:

https://doi.org/10.53697/emak.v7i2.3813

Keywords:

Technology Acceptance Model, Aplikasi Halodoc, Persepsi Mahasiswa, Aplikasi Kesehatan Digital

Abstract

Perkembangan teknologi digital telah menghadirkan inovasi dalam layanan kesehatan melalui aplikasi telemedicine seperti Halodoc. Meskipun mahasiswa merupakan generasi digital native, penerimaan mereka terhadap aplikasi kesehatan digital masih menunjukkan variasi yang perlu diteliti. Tujuan penelitian ini adalah menganalisis persepsi mahasiswa Universitas Islam Sultan Agung terhadap aplikasi Halodoc menggunakan pendekatan Technology Acceptance Model dengan fokus pada dimensi Perceived Usefulnessdan Perceived Ease of Use. Metode penelitian yang digunakan adalah kualitatif deskriptif dengan teknik purposive sampling melibatkan dua belas mahasiswa yang telah menggunakan aplikasi Halodoc minimal satu kali. Pengumpulan data dilakukan melalui wawancara mendalam dan dianalisis menggunakan model Miles dan Huberman. Hasil penelitian menunjukkan bahwa mahasiswa memiliki persepsi positif terhadap aplikasi Halodoc pada kedua dimensi TAM. Dari aspek kemudahan penggunaan, aplikasi dinilai mudah dioperasikan dengan proses sederhana meskipun terdapat kurva pembelajaran bagi pengguna baru karena banyaknya fitur. Dari aspek kemanfaatan, mahasiswa merasakan manfaat signifikan berupa efektivitas dalam mengatasi keluhan kesehatan, kemudahan akses tanpa antre dengan layanan 24 jam, efisiensi waktu dan biaya, serta kenyamanan berkonsultasi dengan privasi terjaga. Faktor yang mempengaruhi penerimaan meliputi tingkat literasi digital, kualitas responsivitas dokter, kemudahan navigasi aplikasi, dan kebutuhan privasi pengguna. Simpulan penelitian ini membuktikan bahwa model TAM valid dalam menjelaskan penerimaan aplikasi kesehatan digital di kalangan mahasiswa, dimana kombinasi persepsi kemanfaatan tinggi dan kemudahan penggunaan yang memadai menghasilkan sikap positif terhadap adopsi teknologi kesehatan digital.

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References

Alsyouf, A., Ishak, A. K., Lutfi, A., & Alhazmi, F. N. (2022). The Role of Personality and Top Management Support in Continuance Intention to Use Electronic Health Record Systems among Nurses.

Anderson, K., Burford, O., & Emmerton, L. (2016). Mobile health apps to facilitate self-care: A qualitative study of user experiences. PLoS ONE, 11(5), 1–21. https://doi.org/10.1371/journal.pone.0156164

Baumel, A., Muench, F., Edan, S., & Kane, J. M. (2019). Objective user engagement with mental health apps: Systematic search and panel-based usage analysis. Journal of Medical Internet Research, 21(9). https://doi.org/10.2196/14567

Borghouts, J., Eikey, E., Mark, G., De Leon, C., Schueller, S. M., Schneider, M., Stadnick, N., Zheng, K., Mukamel, D., & Sorkin, D. H. (2021). Barriers to and facilitators of user engagement with digital mental health interventions: Systematic review. Journal of Medical Internet Research, 23(3). https://doi.org/10.2196/24387

Cajita, M. I., Hodgson, N. A., Lam, K. W., Yoo, S., & Han, H. R. (2018). Facilitators of and Barriers to mHealth Adoption in Older Adults With Heart Failure. Computers, Informatics, Nursing, 38(6), 376–382. https://doi.org/10.1097/CIN.0000000000000442

Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319. https://doi.org/10.2307/249008

Dou, K., Yu, P., Deng, N., Liu, F., Guan, Y., & Li, Z. (2017). Patients’ Acceptance of Smartphone Health Technology for Chronic Disease Management : A Theoretical Model and Empirical Test Corresponding Author : 5, 1–15. https://doi.org/10.2196/mhealth.7886

Wildenbos, G. A. & Peute, L. M. J. (2018). Aging barriers influencing mobile health usability for older adults: A literature based framework (MOLD-US). https://doi.org/https://doi.org/10.1016/j.ijmedinf.2018.03.012

Garavand, A., Mohseni, M., Asadi, H., Etemadi, M., Moradi-joo, M., Services, H., Sciences, M., & Sciences, M. (2016). Electronic Physician. August, 2713–2718.

Holden, R. J., & Karsh, B. (2010). The Technology Acceptance Model : Its past and its future in health care. Journal of Biomedical Informatics, 43(1), 159–172. https://doi.org/10.1016/j.jbi.2009.07.002

Jung, W., & Yim, H. R. (2016). Effects of mental model and intrinsic motivation on behavioral intention of smartphone application users. ETRI Journal, 38(3), 589–598. https://doi.org/10.4218/etrij.16.0115.0447

Alam, M. Z., Hu, W., Kaium, M. A. & Hoque, M. R. (2020). Understanding the determinants of mHealth apps adoption in Bangladesh: A SEM-Neural network approach. https://doi.org/10.1016/j.techsoc.2020.101255

Nadal, C., Sas, C., & Doherty, G. (2020). Technology acceptance in mobile health: Scoping review of definitions, models, and measurement. Journal of Medical Internet Research, 22(7), 1–17. https://doi.org/10.2196/17256

Handayani, P. W., Hidayanto, A. N., Sandhyaduhita, P. I. & Kasiyah, D. A. (2015). Strategic hospital services quality analysis in Indonesia. 42(6). https://doi.org/10.1016/j.eswa.2014.11.065

Marangunić, N. & Granić, A. (2015). Technology acceptance model: a literature review from 1986 to 2013. https://doi.org/10.1007/s10209-014-0348-1

Rakibul, H. G. S. (2017). Understanding factors influencing the adoption of mHealth by the elderly: An extension of the UTAUT model. https://doi.org/10.1016/j.ijmedinf.2017.02.002

Sugiyono. (2017). metode penelitian kuantitatif, kualitatif dan R&D. Alfabeta.

Sugiyono. (2022). metode penelitian kualitatif, kantitatif, dan R&D. Alfabeta.

Sugiyono. (2023). Metode penelitian kuantitatif, kualitatif, R&D. ALFABETA, cv I Hotline: 081.1213.9484 TT. Gegerkalong Hilir No. 84 Bandung Telp. (022) 200 8822 Fax. (022) 2020 373. www.cvalfabeta.com

Sun, Y., & Wang, N. (2013). Understanding The Acceptance Of Mobile Health Services. 14(2), 183–200.

Tavares, J., & Oliveira, T. (2018). New Integrated Model Approach to Understand the Factors That Drive Electronic Health Record Portal Adoption : Cross-Sectional National Survey Corresponding Author : 20, 1–17. https://doi.org/10.2196/11032

Thinnukool, O., Khuwuthyakorn, P., Wientong, P., & Panityakul, T. (2017). Non-prescription medicine mobile healthcare application: Smartphone-based software design and development review. International Journal of Interactive Mobile Technologies, 11(5), 130–146. https://doi.org/10.3991/ijim.v11i5.7123

Venkatesh, V., Davis, F. D., & Studies, F. (2000). A Theoretical Extension of the Technology Acceptance Model : Four Longitudinal. January 2015. https://doi.org/10.1287/mnsc.46.2.186.11926

Wang, T., Wang, W., Liang, J., Nuo, M., Wen, Q., Wei, W., Han, H., & Lei, J. (2022). Identifying major impact factors affecting the continuance intention of mHealth: a systematic review and multi-subgroup meta-analysis. Npj Digital Medicine, 5(1). https://doi.org/10.1038/s41746-022-00692-9

Zhao, Y., Ni, Q., & Zhou, R. (2018). International Journal of Information Management What factors in fl uence the mobile health service adoption ? A meta-analysis and the moderating role of age. 43(October 2017), 342–350. https://doi.org/10.1016/j.ijinfomgt.2017.08.006