Main Article Content

Abstract

Self-medication requires accurate drug information to ensure its safe and appropriate use. e-Health has the potential to meet this need; however, its adoption among the public remains variable, highlighting the importance of examining the factors influencing its adoption. This study aimed to analyze the determinants of e-Health adoption as a source of self-medication information among the community in Banjarbaru City using the Unified Theory of Acceptance and Use of Technology (UTAUT) model. This cross-sectional analytical study involved 229 respondents selected through purposive sampling. Data were collected from July to August 2026 using an online questionnaire based on six UTAUT constructs and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4. The results showed that performance expectancy (p = 0.003) and social influence (p < 0.001) had significant positive effects on behavioral intention to use e-Health, whereas effort expectancy had no significant effect (p = 0.078). Facilitating conditions and behavioral intention significantly influenced use behavior (p < 0.001). The model explained 72.8% of the variance in behavioral intention and 77.6% of the variance in use behavior. Social influence was the strongest determinant of behavioral intention, while behavioral intention was the strongest predictor of use behavior. The study concluded that performance expectancy, social influence, facilitating conditions, and behavioral intention are key determinants of e-Health adoption as a source of self-medication information. Strengthening these factors may enhance the adoption and utilization of e-Health among the community.

Keywords

e-Health Self-medication Technology adoption UTAUT PLS-SEM

Article Details

Author Biographies

Esty Restiana Rusida, Program Studi Sarjana Farmasi, Fakultas Farmasi, Universitas Borneo Lestari

Program Studi Sarjana Farmasi, Fakultas Farmasi, Universitas Borneo Lestari

Rahmayanti Fitriah, Program Studi Sarjana Farmasi, Fakultas Farmasi, Universitas Borneo Lestari

Program Studi Sarjana Farmasi, Fakultas Farmasi, Universitas Borneo Lestari

Mochammad Maulidie Alfianoor Saputra, Program Studi Sarjana Farmasi, Fakultas Farmasi, Universitas Borneo Lestari

Program Studi Sarjana Farmasi, Fakultas Farmasi, Universitas Borneo Lestari

Depy Oktapian Akbar, Program Studi Pendidikan Profesi Apoteker, Fakultas Farmasi, Universitas Borneo Lestari

Program Studi Pendidikan Profesi Apoteker, Fakultas Farmasi, Universitas Borneo Lestari

Helmina Wati, Program Studi Pendidikan Profesi Apoteker, Fakultas Farmasi, Universitas Borneo Lestari

Program Studi Pendidikan Profesi Apoteker, Fakultas Farmasi, Universitas Borneo Lestari
How to Cite
Yatminto, E., Rusida, E. R., Fitriah, R., Alfianoor Saputra, M. M., Akbar, D. O., & Wati, H. (2026). Determinan Penggunaan e-Health sebagai Sumber Informasi Swamedikasi pada Masyarakat Kota Banjarbaru Pendekatan UTAUT. Borneo Journal of Pharmascientech, 10(2), 194-205. https://doi.org/10.59053/bjp.v10i2.866

References

  1. Admassu, W., & Gorems, K. (2024). Analyzing health service employees’ intention to use e-health systems in southwest Ethiopia: using UTAUT-2 model. BMC Health Services Research, 24(1), 1136.
  2. Ajzen, I. (1991). The Theory of Planned Behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
  3. Andriyana, A., Astuti, W., Faida, E. W., & Apriliansyah, U. (2024). Tingkat penggunaan rekam medis elektronik dengan metode Unified Theory of Acceptance and Use of Technology (UTAUT) di Rumah Sakit Mata Undaan Surabaya. Indonesian of Health Information Management Journal (INOHIM), 12(2), 109–116. https://doi.org/10.47007/inohim.v12i2.586
  4. Ayalew, M. B. (2017). Self-Medication Practice in Ethiopia: A Systematic Review. Patient Preference and Adherence, 11, 401–413. https://doi.org/10.2147/PPA.S131496
  5. Badan Pusat Statistik. (2025). Persentase Penduduk yang Mengobati Sendiri Selama Sebulan Terakhir (Persen). Badan Pusat Statistik. https://www.bps.go.id/id/statistics-table/2/MTk3NCMy/persentase-penduduk-yang-mengobati-sendiri-selama-sebulan-terakhir.html
  6. Candra, S., Frederica, E., Putri, H. A., & Loang, O. K. (2025). The UTAUT Approach to Indonesia’s Behavioral Intention to Use Mobile Health Apps. Journal of Science and Technology Policy Management, 16(5), 892–911. https://doi.org/10.1108/JSTPM-10-2022-0175
  7. Chayomchai, A., Phonsiri, W., Junjit, A., Boongapim, R., & Suwannapusit, U. (2020). Factors affecting acceptance and use of online technology in Thai people during COVID-19 quarantine time. Management Science Letters, 10(13), 3009–3016.
  8. Eysenbach, G. (2001). What Is e-Health? Journal of Medical Internet Research, 3(2), e20. doi:10.2196/jmir.3.2.e20
  9. Faul, F., Erdfelder, E., Lang, A.-G., & Buchner, A. (2007). G*Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39(2), 175–191. https://doi.org/10.3758/BF03193146
  10. Fauzi, R., & Febriyani, W. (2024). Analysis of Factors Influencing the Acceptance of Health Monitoring Applications Using UTAUT2: A Case Study in Sukabumi Regency. 2024 International Conference on Digital Business and Technology Management (ICONDBTM), 1–6. https://doi.org/10.1109/ICONDBTM64135.2024.11122842
  11. Fitrianie, S., Horsch, C., Beun, R. J., Griffioen-Both, F., & Brinkman, W.-P. (2021). Factors Affecting User’s Behavioral Intention and Use of a Mobile-Phone-Delivered Cognitive Behavioral Therapy for Insomnia: A Small-Scale UTAUT Analysis. Journal of Medical Systems, 45(12), 110. https://doi.org/10.1007/s10916-021-01785-w
  12. Fornell, C., & Larcker, D. F. (1981). Evaluating Structural Equation Models With Unobservable Variables and Measurement Error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104
  13. 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
  14. Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to Use and How to Report the Results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203
  15. Harahap, N. A., Khairunnisa, K., & Tanuwijaya, J. (2017). Patient Knowledge and Rationality of Self-Medication in Three Pharmacies of Panyabungan City, Indonesia. Jurnal Sains Farmasi & Klinis, 3(2), 186–192. https://doi.org/10.29208/jsfk.2017.3.2.124
  16. Haris, M. S., & Lisnasari, B. R. W. (2024). Self-Medication Practice, Attitude, and Knowledge of Health Science Students in Indonesia: A Cross Sectional Study. Pharmaceutical Journal of Indonesia, 10(1), 8–17. https://doi.org/10.21776/ub.pji.2024.010.01.2
  17. Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135.
  18. Kasulkar, A. A., & Gupta, M. (2015). Self Medication Practices Among Medical Students of a Private Institute. Indian Journal of Pharmaceutical Sciences, 77(2), 178–182.
  19. Kwee, V., Istijanto, I., & Widjojo, H. (2022). Understanding the Determinants of m-Health Adoption in Indonesia. Jurnal Manajemen Teori dan Terapan, 15(3), 408–422. https://doi.org/10.20473/jmtt.v15i3.40142
  20. Laka, L. C. P., Soegiantoro, D. H., & Atmaja, S. P. (2025). Drug Information and Counselling Services in Self-Medication at Apotek X Sleman. Jurnal EduHealth, 16(2), 796–809. https://ejournal.seaninstitute.or.id/index.php/healt/article/view/6686?utm
  21. Meisari, W. A., Widyaningrum, N., Muhlizardy, M., Tri Utami, R., & Aulia, S. N. (2025). Determinants of Public Interest in Telemedicine Use With UTAUT and Social Cognitive Model Approaches. Jurnal Ilmiah Kesehatan (JIKA), 7(1), 137–152. https://doi.org/10.36590/jika.v7i1.1271
  22. Mensah, I. K., Zeng, G., & Mwakapesa, D. S. (2022). The Behavioral Intention to Adopt Mobile Health Services: The Moderating Impact of Mobile Self-Efficacy. Frontiers in Public Health, 10, 1020474. https://doi.org/10.3389/fpubh.2022.1020474
  23. Octavius, G. S., & Antonio, F. (2021). Antecedents of Intention to Adopt Mobile Health (mHealth) Application and Its Impact on Intention to Recommend: An Evidence From Indonesian Customers. International Journal of Telemedicine and Applications, 2021, 6698627. https://doi.org/10.1155/2021/6698627
  24. Sarstedt, M., Ringle, C. M., & Hair, J. F. (2021). Partial Least Squares Structural Equation Modeling. In C. Homburg, M. Klarmann, & A. Vomberg (Eds.), Handbook of Market Research (pp. 587–632). Springer. https://doi.org/10.1007/978-3-319-57413-4_15
  25. Thanthrige, A., Lu, B., Sako, Z., & Wickramasinghe, N. (2025). Determinants of Health Care Technology Adoption Using an Integrated Unified Theory of Acceptance and Use of Technology and Task Technology Fit Model: Systematic Review and Meta-Analysis. Journal of Medical Internet Research, 27, e64524. https:doi:10.2196/64524
  26. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
  27. World Health Organization. (2025). Global Strategy on Digital Health 2020–2027. World Health Organization. https://www.who.int/publications/i/item/9789240116870