SINTA

0.0

Impact

Scholar

6

H-Index

Journal of Health Innovation and Environmental Education

an Open Access Journal

User Insights: Understanding the Acceptance and Utilization of the National Health Insurance Mobile Application

Pratiwi Hanjani Putri
Syarif Hidayatullah State Islamic University Jakarta ROR
Indonesia
prtanjanipr@gmail.com
Michelle Steenvoorden
University of Copenhagen ROR
Denmark
Share

  • Purpose of the study: to analyze the factors that influence user acceptance of the National Health Insurance Mobile application using the Unification Theory Model of Technology Acceptance and Use.

    Methodology: The population of this study was users of the National Health Insurance Mobile application residing in Jakarta, Bogor, Depok, Tangerang, and Bekasi. Questionnaires were distributed online and offline, and the sample collection technique used multi-stage purposive sampling. This study employed quantitative methods and CB-SEM analysis techniques. Data analysis was carried out using AMOS version 24.

    Main Findings: The results showed the rejection of seven of the twelve hypotheses tested. The results of this study are expected to provide recommendations for the development of a National Health Insurance Mobile application for the Social Health Insurance Administration Agency.

    Novelty/Originality of this study: This study offers new insights into how users accept and utilize the National Health Insurance mobile application by integrating behavioral, experiential, and system-related factors in a single analytical model. It advances current knowledge by identifying specific user-driven barriers and facilitators, providing evidence-based directions for improving digital health service adoption and optimizing user engagement.

  • How to cite

    User Insights: Understanding the Acceptance and Utilization of the National Health Insurance Mobile Application. (2025). Journal of Health Innovation and Environmental Education, 2(1), 102-112. https://doi.org/10.37251/jhiee.v2i1.2321

    More Citation Formats

  • 123
    Abstract views
    161
    Downloads

    Metrics — Badges

    1. A. Kutnjak, “Covid-19 Accelerates Digital Transformation in Industries : Challenges , Issues , Barriers and Problems in Transformation,” IEEE Access, vol. 9, pp. 79373–79388, 2021, doi: 10.1109/ACCESS.2021.3084801. DOI: https://doi.org/10.1109/ACCESS.2021.3084801
    2. P. Limna, “The Digital Transformation of Healthcare in The Digital Economy: A Systematic Review,” Int. J. Adv. Heal. Sci. Technol., vol. 3, no. 2, pp. 127–132, 2023. DOI: https://doi.org/10.35882/ijahst.v3i2.244
    3. L. Lazuardi, G. Y. Sanjaya, P. Bahjuri, R. G. M. Siahaan, L. Achmad, and H. Wulandari, “Interoperability of Health Digitalization: Case Study on Use of Information Technology for Maternal and Child Health Services in Indonesia Research Method,” in 24th International Conference on Business Information Systems, 2021, pp. 317–327. DOI: https://doi.org/10.52825/bis.v1i.53
    4. F. A. AL-Muttaqin and R. A. Nugroho, “Effectiveness of Digital-Based Public Service Innovation: Case Study of Population Services in Indonesia’s Local Government,” J. Anal. Kebijak. dan Pelayan Publik, vol. 11, no. 1, pp. 1–16, 2025. DOI: https://doi.org/10.31947/jakpp.v11i1.11802
    5. H. Mumtaz et al., “Current challenges and potential solutions to the use of digital health technologies in evidence generation: a narrative review,” Front. Digit. Heal., no. September, pp. 1–8, 2023, doi: 10.3389/fdgth.2023.1203945. DOI: https://doi.org/10.3389/fdgth.2023.1203945
    6. Ö. Aydin and E. Karaarslan, “OpenAI ChatGPT Generated Literature Review : Digital Twin in Healthcare,” Emerg. Comput. Technol., vol. 2, pp. 22–31, 2022. DOI: https://doi.org/10.2139/ssrn.4308687
    7. S. Soraya, T. Syamanta, H. Saidi, R. Bakkol, and M. Greg, “Impact of the National Health Insurance Program (JKN) on Access to Public Health Services : A Comprehensive Analysis,” J. Ilmu Pendidik. dan Hum., vol. 12, no. 3, pp. 133–151, 2023. DOI: https://doi.org/10.35335/jiph.v12i3.7
    8. I. Efendy, M. Nyorong, A. Amirah, and F. Sari, “National Health Insurance (JKN) Mobile Application Use Towards Satisfaction of Participants of the Health Social Security Implementing Agency (BPJS) in Madani Hospital in Medan City,” J. Med. Heal. Stud., vol. 3, no. 1, pp. 26–34, 2022, doi: 10.32996/jmhs. DOI: https://doi.org/10.32996/jmhs.2022.3.1.4
    9. R. Panjaitan and M. Hutajulu, “Assessing The Impact of Mobile Health Service Quality By The Social Security Administering Body on The Satisfaction of National Health Insurance Participants,” J. Ekon., vol. 13, no. 01, pp. 2625–2633, 2024, doi: 10.54209/ekonomi.v13i01. DOI: https://doi.org/10.54209/ekonomi.v13i01
    10. M. Samudra and A. Amalia, “Effectiveness of Implementing JKN Mobile Application on Accessibility of Health Services and Economic Welfare in Surabaya,” MAP Obs. J. Penelit. Adm. Publik, vol. 05, no. 02, pp. 12–20, 2025.
    11. M. Sihotang and H. Simangunsong, “Assessing the Impact of the National Health Insurance Program (JKN) on Healthcare Access in City Medan,” Law Econ., vol. 16, no. 3, pp. 187–201, 2022. DOI: https://doi.org/10.35335/laweco.v16i3.60
    12. R. Widuri, H. Marlina, O. Dewi, H. Ismainar, and N. Rany, “Analysis of Universal Health Coverage (UHC) Achievements from Triangular Policy Analysis Aspect in Pelalawan Regency,” Int. J. Multidiscip. Approach Res. Sci., vol. 2, no. 02, pp. 854–873, 2024. DOI: https://doi.org/10.59653/ijmars.v2i02.760
    13. P. Akweongo, D. D. Gadeka, G. Aryeetey, J. Sumboh, J. M. K. Aheto, and M. Aikins, “Does mobile renewal make health insurance more responsive to clients ? A case study of the National Health Insurance Scheme in Ghana,” BMJ Glob. Heal., vol. 7, pp. 1–11, 2023, doi: 10.1136/bmjgh-2022-011440. DOI: https://doi.org/10.1136/bmjgh-2022-011440
    14. P. Esmaeilzadeh, T. Mirzaei, and S. Dharanikota, “Patients ’ Perceptions Toward Human – Artificial Intelligence Interaction in Health Care : Experimental Study,” J. Med. Internet Res., vol. 23, no. 11, pp. 1–21, 2021, doi: 10.2196/25856. DOI: https://doi.org/10.2196/25856
    15. T. T. U. Nguyen, P. Van Nguyen, H. T. N. Huynh, G. Q. Truong, and L. Do, “Unlocking e-government adoption: Exploring the role of perceived usefulness, ease of use, trust, and social media engagement in Vietnam,” J. Open Innov. Technol. Mark. Complex., vol. 10, no. 2, pp. 1–10, 2024, doi: 10.1016/j.joitmc.2024.100291. DOI: https://doi.org/10.1016/j.joitmc.2024.100291
    16. S. Balaskas, A. Panagiotarou, and M. Rigou, “The Influence of Trustworthiness and Technology Acceptance Factors on the Usage of e-Government Services during COVID-19 : A Case Study of Post COVID-19 Greece,” Adm. Sci., vol. 12, pp. 1–25, 2022. DOI: https://doi.org/10.3390/admsci12040129
    17. M. I. Alnajjar, “The Impact of Unified Medical Insurance System Implementation (Nphies) on Healthcare Service Quality: Applied Research Case Study in Arrawdha General Hospital Dammam, Saudi Arabia,” Am. J. Med. Sci. Innov., vol. 3, no. 1, pp. 118–133, 2024, doi: https://doi.org/10.54536/ajmsi.v3i1.2630. DOI: https://doi.org/10.54536/ajmsi.v3i1.2630
    18. D. Erku et al., “Digital Health Interventions to Improve Access to and Quality of Primary Health Care Services : A Scoping Review,” Int. J. Environ. Res. Public Heal. Rev., vol. 20, pp. 1–15, 2023, doi: https://doi.org/10.3390/ijerph20196854. DOI: https://doi.org/10.3390/ijerph20196854
    19. S. S. Binyamin and B. A. Zafar, “Proposing a mobile apps acceptance model for users in the health area : A systematic literature review and meta-analysis,” Heal. Informatics Journa, vol. 00, no. 0, pp. 1–27, 2021, doi: 10.1177/1460458220976737. DOI: https://doi.org/10.1177/1460458220976737
    20. C. Wang and H. Qi, “Influencing Factors of Acceptance and Use Behavior of Mobile Health Application Users : Systematic Review,” Healthcare, vol. 9, pp. 1–13, 2021, doi: https://doi.org/10.3390/healthcare9030357. DOI: https://doi.org/10.3390/healthcare9030357
    21. N. K. Putri, A. D. Laksono, and N. Rohmah, “Predictors of national health insurance membership among the poor with different education levels in Indonesia,” BMC Public Health, vol. 23, no. 373, pp. 1–8, 2023. DOI: https://doi.org/10.1186/s12889-023-15292-9
    22. B. Vadivel, S. Alam, I. Nikpoo, and B. Ajanil, “The Impact of Low Socioeconomic Background on a Child’s Educational Achievements,” Educ. Res. Int., vol. 2023, pp. 1–11, 2023, doi: 10.1155/2023/6565088. DOI: https://doi.org/10.1155/2023/6565088
    23. J. Wang, X. Li, P. Wang, Q. Liu, Z. Deng, and J. Wang, “Research Trend of the Unified Theory of Acceptance and Use of Technology Theory : A Bibliometric Analysis,” Sustain. Artic., vol. 10, pp. 1–20, 2022. DOI: https://doi.org/10.3390/su14010010
    24. A. Strzelecki, “Students ’ Acceptance of ChatGPT in Higher Education : An Extended Unified Theory of Acceptance and Use of Technology,” Innov. High. Educ., vol. 49, no. 2, pp. 223–245, 2024, doi: 10.1007/s10755-023-09686-1. DOI: https://doi.org/10.1007/s10755-023-09686-1
    25. I. Fedorko, R. Bačik, and B. Gavurova, “Effort expectancy and social influence factors as main determinants of performance expectancy using electronic banking,” Banks Bank Syst., vol. 16, no. 2, pp. 26–37, 2021, doi: 10.21511/bbs.16(2).2021.03. DOI: https://doi.org/10.21511/bbs.16(2).2021.03
    26. N. Rizkalla, H. Tannady, and R. Bernando, “Analysis of the influence of performance expectancy , effort expectancy , social influence , and attitude toward behavior on intention to adopt live.on,” Multidiscip. Rev., vol. 15, no. 2023, pp. 1–9, 2024. DOI: https://doi.org/10.31893/multirev.2023spe017
    27. D. Tao, Z. Chen, M. Qin, and M. Cheng, “Modeling Consumer Acceptance and Usage Behaviors of m-Health : An Integrated Model of Self-Determination Theory , Task – Technology Fit , and the Technology Acceptance Model,” Healthcare, vol. 11, pp. 1–17, 2023. DOI: https://doi.org/10.3390/healthcare11111550
    28. A. T. Lee, R. K. Ramasamy, and A. Subbarao, “Understanding Psychosocial Barriers to Healthcare Technology Adoption : A Review of TAM Technology Acceptance Model and Unified Theory of Acceptance and Use of Technology and UTAUT Frameworks,” Healthcare, vol. 13, no. 250, pp. 1–35, 2025. DOI: https://doi.org/10.3390/healthcare13030250
    29. Y. Ma and M. Luo, “Older people ’ s intention to use medical apps during the COVID-19 pandemic in China: an application of the Unified Theory of Acceptance and Use of Technology (UTAUT) model and the Technology of Acceptance Model (TAM),” Ageing Soc., vol. 44, pp. 1515–1532, 2024, doi: 10.1017/S0144686X22000423. DOI: https://doi.org/10.1017/S0144686X22000423
    30. Y. Liu, X. Lu, G. Zhao, C. Li, and J. Shi, “Adoption of mobile health services using the unified theory of acceptance and use of technology model: Self-efficacy and privacy concerns,” Front. Psychol., vol. 13, no. August, pp. 1–20, 2022, doi: 10.3389/fpsyg.2022.944976. DOI: https://doi.org/10.3389/fpsyg.2022.944976
    31. A. M. Kaihlanen et al., “Towards digital health equity - a qualitative study of the challenges experienced by vulnerable groups in using digital health services in the COVID-19 era,” BMC Health Serv. Res., vol. 22, no. 188, pp. 1–12, 2022, doi: 10.1186/s12913-022-07584-4. DOI: https://doi.org/10.1186/s12913-022-07584-4
    32. S. Li et al., “Research on user ’ s highly sensitive privacy disclosure intention in home intelligent health service system : A perspective from trust enhancement mechanism,” Digit. Heal., vol. 9, no. 99, pp. 1–99, 2023, doi: 10.1177/20552076231219444. DOI: https://doi.org/10.1177/20552076231219444
    33. J. Kittur, “Conducting Quantitative Research Study: A Step-by-Step Process,” J. Eng. Educ. Transform., vol. 36, no. 4, pp. 100–112, 2023, doi: 10.16920/jeet/2023/v36i4/23120. DOI: https://doi.org/10.16920/jeet/2023/v36i4/23120
    34. A. Ghanad, “An Overview of Quantitative Research Methods,” Int. J. Multidiscip. Res. Anal., vol. 06, no. 08, pp. 3794–3803, 2023, doi: 10.47191/ijmra/v6-i8-52. DOI: https://doi.org/10.47191/ijmra/v6-i8-52
    35. Z. Nadya, R. Pustika, and U. T. Indonesia, “The Importance of Family Motivation For Student To Study Online During The Covid-19,” J. English Lang. Teach. Learn., vol. 2, no. 2, pp. 86–89, 2021. DOI: https://doi.org/10.33365/jeltl.v2i2.1214
    36. A. Gumantan, R. A. Nugroho, and R. Yuliandra, “Journal Sport Area Learning during the covid-19 pandemic : Analysis of e-learning on sports education students,” J. Sport Area, vol. 6, no. 1, pp. 51–58, 2021. DOI: https://doi.org/10.25299/sportarea.2021.vol6(1).5397
    37. D. Hossan, Z. Dato’ Mansor, and N. S. Jaharuddin, “Research Population and Sampling in Quantitative Study,” Int. J. Bus. Technopreneursh., vol. 13, no. 3, pp. 209–222, 2023, doi: 10.58915/ijbt.v13i3.263. DOI: https://doi.org/10.58915/ijbt.v13i3.263
    38. F. B. Thomas, “The Role of Purposive Sampling Technique as a Tool for Informal Choices in a Social Sciences in Research Methods,” Just Agric. Multidiscip. e-Newsletter, vol. 2, no. 5, pp. 1–8, 2022, doi: 10.4236/ce.2012.38205. DOI: https://doi.org/10.4236/ce.2012.38205
    39. M. A. Fauzi, “Partial least square structural equation modelling (PLS- SEM) in knowledge management studies : Knowledge sharing in virtual communities,” Knowl. Manag. E-Learning, vol. 14, no. 1, pp. 103–124, 2022. DOI: https://doi.org/10.34105/j.kmel.2022.14.007
    40. W. B. T. S. Putra, “Problems , Common Beliefs and Procedures on the Use of Partial Least Squares Structural Equation Modeling in Business Research,” South Asian J. Soc. Stud. Econ., vol. 14, no. 1, pp. 1–20, 2022, doi: 10.9734/SAJSSE/2022/v14i130367. DOI: https://doi.org/10.9734/sajsse/2022/v14i130367
    41. E. N. Boateng et al., “Effect of mobile phone ‑ based health insurance contribution payment system on retention of coverage in the National Health Insurance Scheme in Ghana: an evaluation study,” BMC Health Serv. Res., vol. 23, no. 239, pp. 1–9, 2023, doi: 10.1186/s12913-023-09236-7. DOI: https://doi.org/10.1186/s12913-023-09236-7
    42. M. M. Rahman, “Sample Size Determination For Survey Research and Non-Probability Sampling Techniques: A Review and Set of Recommendations,” J. Entrep. Bus. Econ., vol. 11, no. 1, pp. 42–62, 2023, [Online]. Available: www.scientificia.com
    43. A. Keisyafa, D. N. Sunarya, S. M. Aghniya, and S. P. Maula, “Analysis of Student’s Awareness of Sustainable Diet in Reducing Carbon Footprint to Support Sustainable Development Goals (SDGs) 2030,” ASEAN J. Agric. Food Eng., vol. 3, no. 1, pp. 67–74, 2024.
    44. R. N. Rosiyana, M. Agustin, I. K. Iskandar, and S. Luckyardi, “A New Digital markeing Area for E-Commerce Business,” Int. J. Res. Appl. Technol., vol. 1, no. 2, pp. 370–381, 2021. DOI: https://doi.org/10.34010/injuratech.v1i2.6765
    45. C. Rofiah and S. Suhermin, “Importance of Performance Expectancy , Effort Expectancy , Social Influence on Behavioral Intention and Actual Usage e-Healthcare Application in Indonesia,” Int. J. Res. Anal. Rev., vol. 9, no. 3, pp. 98–106, 2022.
    46. P. Priyastomo, U. Nimran, A. Prasetya, and T. Noerman, “The Effect of Performance Expectancy, Effort Expectancy, Facilitating Conditions on Behavioral Intention to Use and Their Impact on Usage Behavior Moderated by Trust: a Study on BRISPOT Application Users in the Small and Medium Business Segment of Bank BR,” Manag., vol. 3, no. 291, pp. 1–11, 2025, doi: 10.62486/agma2025291. DOI: https://doi.org/10.62486/agma2025291
    47. J. Kim, I. Choi, and Q. Li, “Customer Satisfaction of Recommender System : Examining Accuracy and Diversity in Several Types of Recommendation Approaches,” Sustainability, vol. 13, pp. 1–20, 2021, doi: https://doi.org/10.3390/su13116165. DOI: https://doi.org/10.3390/su13116165
    48. K. Hidayat and M. I. Idrus, “The effect of relationship marketing towards switching barrier , customer satisfaction , and customer trust on bank customers,” J. Innov. Entrep., vol. 12, no. 29, pp. 1–16, 2023, doi: 10.1186/s13731-023-00270-7. DOI: https://doi.org/10.1186/s13731-023-00270-7
    49. S. F. Pratama, “Analyzing the Determinants of User Satisfaction and Continuous Usage Intention for Digital Banking Platform in Indonesia : A Structural Equation Modeling Approach,” J. Digit. Mark. Digit. Curr., vol. 1, no. 3, pp. 267–285, 2024, doi: 10.47738/jdmdc.v1i3.21. DOI: https://doi.org/10.47738/jdmdc.v1i3.21
    50. X. Lin, A. Al, M. Id, Q. Yang, and M. Masukujjaman, “Examining the effect of logistics service quality on customer satisfaction and re-use intention,” PLoS One, vol. 18, no. 5, pp. 1–24, 2023, doi: 10.1371/journal.pone.0286382. DOI: https://doi.org/10.1371/journal.pone.0286382