An analysis of the unified theory of acceptance and use of technology (UTAUT) to the adoption of electronic medical records in hospital settings
Abstract
Electronic Medical Records (EMRs) are increasingly recognized as vital tools for enhancing the efficiency, accuracy, and quality of healthcare delivery. Despite regulatory mandates in Indonesia, the adoption of EMRs remains uneven, particularly in rural healthcare settings. This study applied the Unified Theory of Acceptance and apply of Technology (UTAUT) to investigate the behavioral intention of healthcare professionals working in private hospitals to use electronic medical records. A quantitative, cross-sectional design was implemented involving 90 participants selected through purposive sampling in an Indonesian hospital. The study's data were gathered between October 2024 and January 2025 using a validated 18-item UTAUT-based questionnaire. Data analysis was conducted with SPSS and SmartPLS software. Results indicated that all four UTAUT construct – Performance Expectancy (β = 0.200, p = 0.016), Effort Expectancy (β = 0.353, p < 0.001), Social Influence (β = 0.291, p < 0.001), and Facilitating Conditions (β = 0.262, p = 0.008 – had statistically significant positive effects on Behavioral Intention. The model demonstrated moderate explanatory power (R² = 0.655) and strong predictive relevance (Q² = 0.512). These results validate the UTAUT model's suitability in this context and provide practical insights for strengthening EMR implementation strategies. Future research should consider longitudinal approaches and multi-site comparisons to enhance generalizability and policy relevance.
References
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[25] L. Rosa et al., “Challenging Cognitive Load Theory: The Role of Educational Neuroscience and Artificial Intelligence in Redefining Learning Efficacy,” Brain Sci, vol. 15, no. 2, p. 203, Feb. 2025, doi: 10.3390/BRAINSCI15020203.
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workers: Protocol for a systematic review and meta-analysis,” Syst Rev, vol. 8, no. 1, Jul. 2019, doi: 10.1186/S13643-019-1077-6.
[30] A. ; Alsyouf et al., “The Role of Personality and Top Management Support in Continuance Intention to Use Electronic Health Record Systems among Nurses,” International Journal of Environmental Research and Public Health 2022, Vol. 19, Page 11125, vol. 19, no. 17, p. 11125, Sep. 2022, doi: 10.3390/IJERPH191711125.
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[34] M. F. Wibowo, A. Pyle, E. Lim, J. W. Ohde, N. Liu, and J. Karlström, “Insights Into the Current and Future State of AI Adoption Within Health Systems in Southeast Asia: Cross-Sectional Qualitative Study,” J Med Internet Res 2025;27:e71591 https://www.jmir.org/2025/1/e71591, vol. 27, no. 1, p. e71591, Jun. 2025, doi: 10.2196/71591.
[35] I. J. Borges do Nascimento et al., “Barriers and facilitators to utilizing digital health technologies by healthcare professionals,” NPJ Digit Med, vol. 6, no. 1, p. 161, Dec. 2023, doi: 10.1038/S41746-023-00899-4.
[36] F. Z. B. A. Razak, A. A. Bakar, and W. S. W. Abdullah, “How perceived effort expectancy and social influence affects the continuance of intention to use e-government. A study of a Malaysian government service,” Electronic Government, vol. 13, no. 1, pp. 69–80, 2017, doi: 10.1504/EG.2017.083943.
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[2] A. I. Tavares, “eHealth, ICT and its relationship with self-reported health outcomes in the EU countries,” Int J Med Inform, vol. 112, pp. 104–113, Apr. 2018, doi: 10.1016/J.IJMEDINF.2018.01.014.
[3] B. E. Whitacre, “The Influence of the Degree of Rurality on EMR Adoption, by Physician Specialty,” Health Serv Res, vol. 52, no. 2, p. 616, Apr. 2016, doi: 10.1111/1475-6773.12510.
[4] T. Larasati, A. I. Fardiansyah, D. Saketi, and A. N. Dewiarti, “The Ethical and Legal Aspects of Health Policy on Electronic Medical Records in Indonesia,” Cepalo, vol. 8, no. 2, pp. 103–112, Oct. 2024, doi: 10.25041/CEPALO.V8NO2.3634.
[5] K. C. Derecho et al., “Technology adoption of electronic medical records in developing economies: A systematic review on physicians’ perspective,” Digit Health, vol. 10, p. 20552076231224604, Jan. 2024, doi: 10.1177/20552076231224605.
[6] A. Tolera, D. Firdisa, H. S. Roba, A. Motuma, M. Kitesa, and A. A. Abaerei, “Barriers to healthcare data quality and recommendations in public health facilities in Dire Dawa city administration, eastern Ethiopia: a qualitative study,” Front Digit Health, vol. 6, p. 1261031, Mar. 2024, doi: 10.3389/FDGTH.2024.1261031/BIBTEX.
[7] D. Abul-Fottouh, M. Y. Song, and A. Gruzd, “Examining algorithmic biases in YouTube’s recommendations of vaccine videos,” Int J Med Inform, vol. 140, p. 104175, Aug. 2020, doi: 10.1016/J.IJMEDINF.2020.104175.
[8] V. Venkatesh, M. G. Morris, G. B. Davis, and F. D. Davis, “User acceptance of information technology: Toward a unified view,” MIS Q, vol. 27, no. 3, pp. 425–478, 2003, doi: 10.2307/30036540.
[9] P. Cornu, S. Steurbaut, K. Gentens, R. Van de Velde, and A. G. Dupont, “Pilot evaluation of an optimized context-specific drug–drug interaction alerting system: A controlled pre-post study,” Int J Med Inform, vol. 84, no. 9, pp. 617–629, Sep. 2015, doi: 10.1016/J.IJMEDINF.2015.05.005.
[10] T. H. Nguyen, X. C. Le, and T. H. L. Vu, “An Extended Technology-Organization-Environment (TOE) Framework for Online Retailing Utilization in Digital Transformation: Empirical Evidence from Vietnam,” Journal of Open Innovation: Technology, Market, and Complexity, vol. 8, no. 4, p. 200, Dec. 2022, doi: 10.3390/JOITMC8040200.
[11] V. Kwee, I. Istijanto, and H. Widjojo, “Understanding the Determinants of m-Health Adoption in Indonesia,” Jurnal Manajemen Teori dan Terapan | Journal of Theory and Applied Management, vol. 15, no. 3, pp. 408–422, Dec. 2022, doi: 10.20473/jmtt.v15i3.40142.
[12] C. Maier, J. B. Thatcher, V. Grover, and Y. K. Dwivedi, “Cross-sectional research: A critical perspective, use cases, and recommendations for IS research,” Int J Inf Manage, vol. 70, p. 102625, Jun. 2023, doi: 10.1016/J.IJINFOMGT.2023.102625.
[13] M. A. Pourhoseingholi, M. Vahedi, and M. Rahimzadeh, “Sample size calculation in medical studies,” Gastroenterol Hepatol Bed Bench, vol. 6, no. 1, p. 14, 2013, Accessed: Nov. 13, 2025. [Online]. Available: https://pmc.ncbi.nlm.nih.gov/articles/PMC4017493/
[14] T. Handayani, ) Sudiana, J. Teknik, E. Sekolah, T. Teknologi, and N. Yogyakarta, “Analisis Penerapan Model UTAUT (Unified Theory of Acceptance and Use of Technology) terhadap Perilaku Pengguna Sistem Informasi (Studi Kasus: Sistem Informasi Akademik pada STTNAS Yogyakarta),” ReTII, 2015, Accessed: Jul. 03, 2025. [Online]. Available: https://journal.itny.ac.id/index.php/ReTII/article/view/406
[15] C. C. Chang, “The Role of Individual Factors in Users’ Intentions to Use Medical Tourism Mobile Apps,” Tourism and Hospitality 2022, Vol. 3, Pages 896-907, vol. 3, no. 4, pp. 896–907, Nov. 2022, doi: 10.3390/TOURHOSP3040057.
[16] E. W. Wuryaningsih, L. Lusmilasari, F. Haryanti, and B. Wahyuni, “Psychometric evaluation of the Indonesian Version of the Empathy Questionnaire for Children and Adolescents (EmQue-CA),” Belitung Nurs J, vol. 11, no. 3, pp. 363–369, 2025, doi: 10.33546/BNJ.3861.
[17] J. Hair and A. Alamer, “Partial Least Squares Structural Equation Modeling (PLS-SEM) in second language and
education research: Guidelines using an applied example,” Research Methods in Applied Linguistics, vol. 1, no. 3, p. 100027, Dec. 2022, doi: 10.1016/J.RMAL.2022.100027.
[18] J. Su, Y. Wang, H. Liu, Z. Zhang, Z. Wang, and Z. Li, “Investigating the factors influencing users’ adoption of artificial intelligence health assistants based on an extended UTAUT model,” Scientific Reports 2025 15:1, vol. 15, no. 1, pp. 1–19, May 2025, doi: 10.1038/s41598-025-01897-0.
[19] M. Kerbouche and I. Bouguesri, “A Structural Analysis of the Chinese Patriarchal Family Business Model:
What Happens in the Corridors of the Shrine?,” Economics and Business, vol. 34, no. 1, pp. 224–245, Feb. 2020, doi: 10.2478/EB-2020-0015.
[20] C. Saragih, C. Nafa Sari, B. Nurtjahyo Moch, and E. Muslim, “Adoption of Electronic Medical Record in Hospitals in Indonesia based on Technology Readiness and Acceptance Model,” ACM International Conference Proceeding Series, pp. 79–85, Sep. 2020, doi: 10.1145/3429551.3429565.
[21] S. G. Bybee et al., “A Secondary Data Analysis of Technology Access as a Determinant of Health and Impediment in Social Needs Screening and Referral Processes,” AJPM Focus, vol. 3, no. 2, p. 100189, Apr. 2024, doi: 10.1016/J.FOCUS.2024.100189.
[22] S. Kelly, S. A. Kaye, and O. Oviedo-Trespalacios, “What factors contribute to the acceptance of artificial intelligence? A systematic review,” Telematics and Informatics, vol. 77, p. 101925, Feb. 2023, doi: 10.1016/J.TELE.2022.101925.
[23] A. Rahimi, S. T. Liaw, J. Taggart, P. Ray, and H. Yu, “Validating an ontology-based algorithm to identify patients with Type 2 Diabetes Mellitus in Electronic Health Records,” Int J Med Inform, vol. 83, no. 10, pp. 768–778, Oct. 2014, doi: 10.1016/J.IJMEDINF.2014.06.002.
[24] E. Lettieri, “Uncertainty inclusion in budgeting technology adoption at a hospital level: Evidence from a multiple case study,” Health Policy (New York), vol. 93, no. 2–3, pp. 128–136, Dec. 2009, doi: 10.1016/J.HEALTHPOL.2009.07.002.
[25] L. Rosa et al., “Challenging Cognitive Load Theory: The Role of Educational Neuroscience and Artificial Intelligence in Redefining Learning Efficacy,” Brain Sci, vol. 15, no. 2, p. 203, Feb. 2025, doi: 10.3390/BRAINSCI15020203.
[26] A. A. Linus, G. A. Aladesusi, I. A. Monsur, and F. J. Elizabeth, “Perceived Usefulness, Ease of Use, And Intention to Utilize Online Tools for Learning Among College of Education Students,” Indonesian Journal of Multidiciplinary Research, vol. 5, no. 1, pp. 41–52, Mar. 2025, doi: 10.17509/IJOMR.V5I1.81387.
[27] T. C. Antonucci, K. J. Ajrouch, and J. A. Manalel, “Social Relations and Technology: Continuity, Context, and Change,” Innov Aging, vol. 1, no. 3, p. igx029, Nov. 2017, doi: 10.1093/GERONI/IGX029.
[28] S. Dünnebeil, A. Sunyaev, I. Blohm, J. M. Leimeister, and H. Krcmar, “Determinants of physicians’ technology acceptance for e-health in ambulatory care,” Int J Med Inform, vol. 81, no. 11, pp. 746–760, Nov. 2012, doi: 10.1016/J.IJMEDINF.2012.02.002.
[29] S. Cotterill et al., “The impact of social norms interventions on clinical behaviour change among health
workers: Protocol for a systematic review and meta-analysis,” Syst Rev, vol. 8, no. 1, Jul. 2019, doi: 10.1186/S13643-019-1077-6.
[30] A. ; Alsyouf et al., “The Role of Personality and Top Management Support in Continuance Intention to Use Electronic Health Record Systems among Nurses,” International Journal of Environmental Research and Public Health 2022, Vol. 19, Page 11125, vol. 19, no. 17, p. 11125, Sep. 2022, doi: 10.3390/IJERPH191711125.
[31] A. Mwogosi and S. Kibusi, “Unveiling barriers to EHR implementation for effective decision support in tanzanian primary healthcare: Insights from practitioners,” Health Informatics J, vol. 30, no. 4, Oct. 2024, doi: 10.1177/14604582241304698/SUPPL_FILE/SJ-PDF-1-JHI-10.1177_14604582241304698.PDF.
[32] V. Venkatesh, J. Y. L. Thong, and X. Xu, “Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology,” MIS Q, vol. 36, no. 1, pp. 157–178, 2012, doi: 10.2307/41410412.
[33] P. W. Handayani, R. Indriani, and A. A. Pinem, “Mobile health readiness factors: From the perspectives of mobile health users in Indonesia,” Inform Med Unlocked, vol. 24, p. 100590, Jan. 2021, doi: 10.1016/J.IMU.2021.100590.
[34] M. F. Wibowo, A. Pyle, E. Lim, J. W. Ohde, N. Liu, and J. Karlström, “Insights Into the Current and Future State of AI Adoption Within Health Systems in Southeast Asia: Cross-Sectional Qualitative Study,” J Med Internet Res 2025;27:e71591 https://www.jmir.org/2025/1/e71591, vol. 27, no. 1, p. e71591, Jun. 2025, doi: 10.2196/71591.
[35] I. J. Borges do Nascimento et al., “Barriers and facilitators to utilizing digital health technologies by healthcare professionals,” NPJ Digit Med, vol. 6, no. 1, p. 161, Dec. 2023, doi: 10.1038/S41746-023-00899-4.
[36] F. Z. B. A. Razak, A. A. Bakar, and W. S. W. Abdullah, “How perceived effort expectancy and social influence affects the continuance of intention to use e-government. A study of a Malaysian government service,” Electronic Government, vol. 13, no. 1, pp. 69–80, 2017, doi: 10.1504/EG.2017.083943.
[37] B. D’Exelle, R. Habraken, and A. Verschoor, “Effort and Social Comparison: Experimental Evidence from Uganda,” Econ Dev Cult Change, vol. 72, no. 4, pp. 1769–1793, Jul. 2024, doi: 10.1086/725231.
[38] M. Sarstedt, C. M. Ringle, and J. F. Hair, “Partial Least Squares Structural Equation Modeling,” Handbook of Market Research, pp. 1–47, 2021, doi: 10.1007/978-3-319-05542-8_15-2.
[39] S. D. Kim, “Application and Challenges of the Technology Acceptance Model in Elderly Healthcare: Insights from ChatGPT,” Technologies 2024, Vol. 12, Page 68, vol. 12, no. 5, p. 68, May 2024, doi: 10.3390/TECHNOLOGIES12050068.
[40] H. Lee, H. Ahn, T. G. Nguyen, S. W. Choi, and D. J. Kim, “Comparing the Self-Report and Measured Smartphone Usage of College Students: A Pilot Study,” Psychiatry Investig, vol. 14, no. 2, p. 198, Mar. 2017, doi: 10.4306/PI.2017.14.2.198.
Published
2025-11-18
How to Cite
GHOZALI, Muhammad Thesa et al.
An analysis of the unified theory of acceptance and use of technology (UTAUT) to the adoption of electronic medical records in hospital settings.
JURNAL ILMU KEFARMASIAN INDONESIA, [S.l.], v. 23, n. 2, p. 227-236, nov. 2025.
ISSN 2614-6495.
Available at: <http://jifi.farmasi.univpancasila.ac.id/index.php/jifi/article/view/1693>. Date accessed: 08 dec. 2025.
doi: https://doi.org/10.35814/jifi.v23i2.1693.
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