Unveiling Barriers to Telehealth Adoption among Employees with Metabolic Syndrome: Innovation Resistance Theory Perspective
-
Rafi Amani Muflih Rahardi
*
Universitas Muhammadiyah Surakarta, Surakarta,
Indonesia
- Chieviog Igiobye Isoagi Universitas Muhammadiyah Surakarta, Surakarta, Indonesia
- Muflihuun Retno Ningsih Universitas Muhammadiyah Surakarta, Surakarta, Indonesia (*) Corresponding Author
Abstract
This study explores the key barriers hindering the adoption of telehealth services among employees diagnosed with Metabolic Syndrome (MetS), using the Innovation Resistance Theory (IRT) framework. The focus on office employees is justified, as this group is highly susceptible to MetS due to sedentary work routines that elevate cardiometabolic risks, making telehealth a potentially valuable tool for continuous monitoring and disease management. A quantitative approach was employed with data collected from 400 office-based employees with MetS in Indonesia via an online survey. Constructs related to usage, value, risk, tradition, and image barriers were measured and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that all five barriers significantly and negatively influence the intention to use telehealth, with usage barrier (β = −0.274; p = 0.001) and image barrier (β = −0.196; p = 0.000) exerting the strongest effects. The model demonstrates strong explanatory power, with R² values of 0.600 for intention to use and 0.515 for actual usage. Furthermore, intention to use was found to significantly predict actual usage and mediate the effects of resistance barriers on adoption behavior. These findings highlight the need to enhance system usability, increase perceived value, and build a credible telehealth image. This research contributes to telehealth literature by validating IRT in a chronic disease context and offering strategic insights for service developers, healthcare policymakers, and digital health marketers aiming to reduce resistance and boost adoption.
Downloads
References
Aji, P. T., & Ramadani, L. (2024). Patients’ Acceptance of Telemedicine Technology: The Influence of User Behavior and Socio-Cultural Dimensions. Journal of Information Systems Engineering and Business Intelligence, 10(1), 81–93. https://doi.org/10.20473/jisebi.10.1.81-93
Alexandra, S., Handayani, P. W. P. W., & Azzahro, F. (2021). Indonesian hospital telemedicine acceptance model: the influence of user behavior and technological dimensions. Heliyon, 7(12). https://doi.org/10.1016/j.heliyon.2021.e08599
Alfian, S. D., Sania, J. A., Aini, D. Q., Khoiry, Q. A., Griselda, M., Ausi, Y., Zakiyah, N., Puspitasari, I. M., Suwantika, A. A., Mahfud, M., Aji, S., Abdulah, R., & Kassianos, A. P. (2024). Evaluation of usability and user feedback to guide telepharmacy application development in Indonesia: a mixed-methods study. BMC Medical Informatics and Decision Making, 24(1), 1–14. https://doi.org/10.1186/s12911-024-02494-3
Alghamdi, S. M., Aldhahir, A. M., Alqahtani, J. S., Siraj, R. A., Alsulayyim, A. S., Almojaibel, A. A., Alhotye, M., Alanazi, A. M., & Alqarni, A. A. (2022). Healthcare Providers’ Perception and Barriers Concerning the Use of Telehealth Applications in Saudi Arabia: A Cross-Sectional Study. Healthcare (Switzerland), 10(8). https://doi.org/10.3390/healthcare10081527
Alkhuzaimi, F., Rainey, D., Wilson, C. B., & Bloomfield, J. (2024). The impact of mobile health interventions on service users’ health outcomes and the role of health professions: a systematic review of systematic reviews—protocol. Systematic Reviews, 13(1), 1–8. https://doi.org/10.1186/s13643-024-02624-y
Alviani, R., Purwandari, B., Eitiveni, I., & Purwaningsih, M. (2023). Factors Affecting Adoption of Telemedicine for Virtual Healthcare Services in Indonesia. Journal of Information Systems Engineering and Business Intelligence, 9(1), 47–69. https://doi.org/10.20473/jisebi.9.1.47-69
Amelia, R., & Simanjuntak, M. (2024). The Role of Perceived Usefulness, Social Influence, Data Security, and Consumer Trust Toward Satisfaction of Telemedicine. Malaysian Journal of Consumer and Family Economics, 33, 126–156. https://doi.org/10.60016/majcafe.v33.05
Ameyaw, E. K., Amoah, P. A., Asiama, A. A., & Agbadi, P. (2025). m-Health knowledge and usage in maternal healthcare delivery: Perspectives and experiences of healthcare practitioners in Ghana. BMJ Open, 15(1), 1–13. https://doi.org/10.1136/bmjopen-2024-092746
Candra, S., Frederica, E., Putri, H. A., & Loang, O. K. (2024). 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
Chakraborty, D., Singu, H. B., & Patre, S. (2022). Fitness Apps’s purchase behaviour: Amalgamation of Stimulus-Organism-Behaviour-Consequence framework (S–O–B–C) and the innovation resistance theory (IRT). Journal of Retailing and Consumer Services, 67(5), 103033. https://doi.org/10.1016/j.jretconser.2022.103033
Chuang, L. M., & Huang, S. H. (2025). AI-Supported Healthcare Technology Resistance and Behavioral Intention: A Serial Mediation Empirical Study on the JD-R Model and Employee Engagement. Systems, 13(4). https://doi.org/10.3390/systems13040268
Crowe, J., Nikolic-Khatatbeh, J., & Li, R. (2024). The digital health divide: Understanding telehealth adoption across racial lines in rural Illinois. SSM - Population Health, 26(12), 101665. https://doi.org/10.1016/j.ssmph.2024.101665
Drewry, J. L., Shutske, J. M., Trechter, D., Luck, B. D., & Pitman, L. (2019). Assessment of digital technology adoption and access barriers among crop, dairy and livestock producers in Wisconsin. Computers and Electronics in Agriculture, 165(3), 104960. https://doi.org/10.1016/j.compag.2019.104960
Endalamaw, A., Zewdie, A., Wolka, E., & Assefa, Y. (2025). A scoping review of digital health technologies in multimorbidity management: mechanisms, outcomes, challenges, and strategies. BMC Health Services Research, 25(1). https://doi.org/10.1186/s12913-025-12548-5
Frishammar, J., Essén, A., Bergström, F., & Ekman, T. (2023). Digital health platforms for the elderly? Key adoption and usage barriers and ways to address them. Technological Forecasting and Social Change, 189(11). https://doi.org/10.1016/j.techfore.2023.122319
Gajarawala, S. N., & Pelkowski, J. N. (2021). Telehealth Benefits and Barriers. Journal for Nurse Practitioners, 17(2), 218–221. https://doi.org/10.1016/j.nurpra.2020.09.013
Hair, J., & Alamer, A. (2022). Partial Least Squares Structural Equation Modeling (PLS-SEM) in second language and education research: Guidelines using an applied example. Research Methods in Applied Linguistics, 1(3), 100027. https://doi.org/https://doi.org/10.1016/j.rmal.2022.100027
Han, L., & Han, X. (2023). The influence of price value on purchase intention among patients with chronic diseases in medical e-commerce during the COVID-19 pandemic in China. Frontiers in Public Health, 11(5). https://doi.org/10.3389/fpubh.2023.1081196
Hartono, S., Triantono, H. B., Hartono, H., Sulistyo, M. H., & Shahudin, F. (2024). The User Experience Analysis of Digital Health Tracking Application in Indonesia. Journal of Computer Science, 20(9), 1099–1109. https://doi.org/10.3844/JCSSP.2024.1099.1109
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. https://doi.org/10.1007/s11747-014-0403-8
Hezer, B., Massey, E. K., Reinders, M. E. J., Tielen, M., Van De Wetering, J., Hesselink, D. A., & Van Den Hoogen, M. W. F. (2024). Telemedicine for Kidney Transplant Recipients: Current State, Advantages, and Barriers. Transplantation, 108(2), 409–420. https://doi.org/10.1097/TP.0000000000004660
Hudiyawati, D., Nur Imamah, I., Sulastri, S., Musa, M., Dewi, S. M. S., & Astuti, L. (2025). An Educational Program to Improve Self-Care in Heart Failure Patients. Jurnal Berita Ilmu Keperawatan, 18(2), 185–192. https://doi.org/10.23917/bik.v18i2.7262
Jeilani, A., & Hussein, A. (2025). Impact of digital health technologies adoption on healthcare workers’ performance and workload: perspective with DOI and TOE models. BMC Health Services Research, 25(1), 271. https://doi.org/10.1186/s12913-025-12414-4
Kaur, P., Dhir, A., Singh, N., Sahu, G., & Almotairi, M. (2020). An innovation resistance theory perspective on mobile payment solutions. Journal of Retailing and Consumer Services, 55(6), 102059. https://doi.org/10.1016/j.jretconser.2020.102059
Kautish, P., Siddiqui, M., Siddiqui, A., Sharma, V., & Alshibani, S. M. (2023). Technology-enabled cure and care: An application of innovation resistance theory to telemedicine apps in an emerging market context. Technological Forecasting and Social Change, 192(3), 122558. https://doi.org/10.1016/j.techfore.2023.122558
Kristinawati, B., Mardana, N. W., & Wijayanti, N. W. D. (2024). Sustaining Quality of Life with Education-Based Treatment for Hypertension Patients During COVID-19 Adaptation. Journal of Holistic Nursing, 42(2), S118–S125. https://doi.org/10.1177/08980101231217358
Leong, L. Y., Hew, T. S., Ooi, K. B., & Lin, B. (2021). A meta-analysis of consumer innovation resistance: is there a cultural invariance? Industrial Management and Data Systems, 121(8), 1784–1823. https://doi.org/10.1108/IMDS-12-2020-0741
Leppe Zamora, J., Leppe Zamora, M., Roa-Alcaino, S., & Sarmiento, O. L. (2025). Sedentary Behavior, Physical Activity, and Health of Workers in Chile According to the National Health Survey-2017. Epidemiologia, 6(1), 1–14. https://doi.org/10.3390/epidemiologia6010015
Listyorini, P. I. (2021). Hospital Patient Loyalty: In terms of Brand Image, Service Quality, Patient Satisfaction, and Trust. Science and Technology, 1(6), 124–127. https://doi.org/https://doi.org/10.47701/icohetech.v1i1.1103
Liu, Y., Wang, R., Huang, R., Cao, Y., Wang, J., & Feng, D. (2021). Influencing factors and their relationships of risk perception and decision-making behaviour of polypharmacy in patients with chronic diseases: A qualitative descriptive study. BMJ Open, 11(4). https://doi.org/10.1136/bmjopen-2020-043557
Maleka, N. H., & Matli, W. (2024). A review of telehealth during the COVID-19 emergency situation in the public health sector: challenges and opportunities. Journal of Science and Technology Policy Management, 15(4), 707–724. https://doi.org/10.1108/JSTPM-08-2021-0126
Meltwater, W. A. S. &. (2024). 2024 Indonesia Digital Report. https://wearesocial.com/id/blog/2024/01/digital-2024/
Monico, L. B., Eastlick, M., Michero, D., Pielsticker, P., & Glasner, S. (2025). Overcoming barriers to traditional care delivery and pharmacy challenges: a qualitative study of buprenorphine, telehealth, and a digital therapeutic for opioid use disorder. Substance Abuse Treatment, Prevention, and Policy, 20(1), 8. https://doi.org/10.1186/s13011-024-00631-9
Najikh, A. U., & Permatasari, H. (2023). Faktor Risiko Sindrom Metabolik Pada Pekerja: Sistematik Review. Jurnal Riset Kesehatan Poltekkes Depkes Bandung, 15(2), 281–293. https://doi.org/10.34011/juriskesbdg.v15i2.2204
Listyorini, P. I. (2021). Hospital Patient Loyalty: In terms of Brand Image, Service Quality, Patient Satisfaction, and Trust. Science and Technology, 1(6), 124–127. https://doi.org/https://doi.org/10.47701/icohetech.v1i1.1103
Osingada, C. P., McMorris, B. J., Tracy, M. F., Nakasujja, N., Ngabirano, T. D., & Porta, C. M. (2025). Patient perceptions and predictors of intention to use telehealth for follow-up care: a mixed methods study among adults living with HIV in Kampala, Uganda. BMC Health Services Research, 25(1). https://doi.org/10.1186/s12913-025-12636-6
Paradilla, M., Nurfitriani, N., & Awawiriam, S. (2022). The Effect of Brand Image on Loyalty through General Patient Satisfaction as an Intervening Variable in Makassar City Hospital. Journal of Asian Multicultural Research for Medical and Health Science Study, 3(1), 67–75. https://doi.org/10.47616/jamrmhss.v3i1.239
Park, H. S., Jeong, S., Chung, H. young, Soh, J. Y., Hyun, Y. H., Bang, S. H., & Kim, H. S. (2022). Use of video-based telehealth services using a mobile app for workers in underserved areas during the COVID-19 pandemic: A prospective observational study. International Journal of Medical Informatics, 166(2), 104844. https://doi.org/10.1016/j.ijmedinf.2022.104844
Park, K. S., & Hwang, S. Y. (2024). Effects of a Remote Physical Activity Improvement Program on Male Office Workers with Metabolic Syndrome in Their 30s and 40s with Sedentary Behavior: A Randomized Controlled Trial. Asian Nursing Research, 18(2), 81–88. https://doi.org/10.1016/j.anr.2024.01.006
Parthasarathi, A., George, T., Kalimuth, M. B., Jayasimha, S., Kaleem Ullah, M., Patil, R., Nair, A., Pai, U., Inbarani, E., Jacob, A. G., Chandy, V. J., John, O., Sudarsanam, T. D., & Mahesh, P. A. (2024). Exploring the potential of telemedicine for improved primary healthcare in India: a comprehensive review. The Lancet Regional Health - Southeast Asia, 27, 100431. https://doi.org/10.1016/j.lansea.2024.100431
Praswati, A. N., & Ningsih, P. R. (2024). Brand love Toward Brand Loyalty of Telehealth Application’s. Benefit: Jurnal Manajemen Dan Bisnis, 9(1). https://doi.org/https://doi.org/10.23917/benefit.v9i1.5571
Purwanti, O. S., Nurani, P., & Wulandari, A. U. (2021). Effect of video education about hypoglycemia on knowledge of diabetes mellitus patients and their families. Journal of Medicinal and Chemical Sciences, 4(3), 267–278. https://doi.org/10.26655/JMCHEMSCI.2021.3.7
Putinagari, P. R., & Aprilianty, F. (2021). Barriers Analysis on Consumer Usage Intention of Freelance Marketplace in Indonesia. Malaysian Journal of Social Sciences and Humanities (MJSSH), 6(8), 578–589. https://doi.org/10.47405/mjssh.v6i8.982
Putri, E., Praswati, A. N., Muna, N., & Sari, N. P. (2022). E-Finance Transformation: A Study of M-Wallet Adoption in Indonesia. Jurnal Ekonomi Pembangunan: Kajian Masalah Ekonomi Dan Pembangunan, 23(1), 123–134. https://doi.org/10.23917/jep.v23i1.15469
Ram, S., & Sheth, J. N. (1989). Consumer Resistance to Innovations: The Marketing Problem and its solutions. Journal of Consumer Marketing, 6(2), 5–14. https://doi.org/10.1108/EUM0000000002542
Schlottke, J. A., Alvarez-Risco, A., Bertiche, E. L., López, R. B., Torletti, C. V., del Hoyo, F. Y., Del-Aguila-Arcentales, S., Mejia, C. R., Rojas-Osorio, M., Davies, N. M., & Yáñez, J. A. (2024). Acceptance factors of telemedicine in times of COVID-19: Case Argentina. Pharmacia, 71(Who 2021), 1–10. https://doi.org/10.3897/PHARMACIA.71.E113509
Scott Kruse, C., Karem, P., Shifflett, K., Vegi, L., Ravi, K., & Brooks, M. (2018). Evaluating barriers to adopting telemedicine worldwide: A systematic review. Journal of Telemedicine and Telecare, 24(1), 4–12. https://doi.org/10.1177/1357633X16674087
Seppänen, S., Ukko, J., & Saunila, M. (2025). Understanding determinants of digital transformation and digitizing management functions in incumbent SMEs. Digital Business, 5(1). https://doi.org/10.1016/j.digbus.2025.100106
Sobaih, A. E. E., Chaibi, A., Brini, R., & Abdelghani Ibrahim, T. M. (2025). Unlocking Patient Resistance to AI in Healthcare: A Psychological Exploration. European Journal of Investigation in Health, Psychology and Education, 15(1), 1–27. https://doi.org/10.3390/ejihpe15010006
Talwar, M., Corazza, L., Bodhi, R., & Malibari, A. (2024). Why do consumers resist digital innovations? An innovation resistance theory perspective. International Journal of Emerging Markets, 19(11), 4327–4342. https://doi.org/10.1108/IJOEM-03-2022-0529
Tian, Y., Wang, X., Hu, Z., Yu, X., Shao, M., Zhang, C., Zhang, D., Shan, W., Chang, C., Zhang, C., Nie, Y., Zheng, C., Cao, X., Pei, X., Zhang, Y., Tuerdi, N., & Wang, Z. (2025). Design, rationale, and characterization of the mobile health based occupational cardiovascular risk intervention study (mHealth-OPEN study). American Heart Journal, 284, 32–41. https://doi.org/10.1016/j.ahj.2025.02.005
Van De Werken, H. A., Rohrbach, P. J., & Bolman, C. A. W. W. (2025). Explaining intention and use of Mhealth with the unified theory of acceptance and use of technology. Acta Psychologica, 254(September 2024), 104819. https://doi.org/10.1016/j.actpsy.2025.104819
Van De Wiele, M. D., Hastings, A. M., Evans, M. D., O’Connell, M. S., & Flynn, P. M. (2025). Using the Unified Theory of Acceptance and Use of Technology (UTAUT) to predict the behavioral intent of teledentistry utilization amongst United States adults. Journal of Telemedicine and Telecare, 31(2), 286–296. https://doi.org/10.1177/1357633X231182405
Wang, K.-C., Huang, P.-S., Wang, Y.-L., Hoe, Z.-Y., Noor, A. A., & Chen, P.-T. (2025). Bridging the gap: Healthcare practitioners’ insights on overcoming Telemedicine barriers and optimal strategies for implementation. Technology in Society, 82, 102899. https://doi.org/https://doi.org/10.1016/j.techsoc.2025.102899
Wasi Abbas, M., Nawaz Tahir, H., Jaffar, N., Raza, G., Aman Ali, S., & Jabeen, R. (2024). Facilitators and barriers in acceptance of telemedicine among healthcare providers in Pakistan: A cross-sectional survey. Journal of Medicine Access, 8. https://doi.org/10.1177/27550834241266413
Xue, Y., Zhang, X., Zhang, Y., & Luo, E. (2024). Understanding the Barriers to Consumer Purchasing of Electric Vehicles: The Innovation Resistance Theory. Sustainability (Switzerland) , 16(6), 1–19. https://doi.org/10.3390/su16062420
Yuniartika, W., & Hidayati, D. A. N. (2021). Improving knowledge of diabetes mellitus patients using booklet. Journal of Medicinal and Chemical Sciences, 4(3), 238–245. https://doi.org/10.26655/JMCHEMSCI.2021.3.4
Zhang, M., Liu, B., Ke, W., Cai, Y., Zhang, L., Huang, W., Yan, X., & Chen, H. (2024). Correlation analysis between occupational stress and metabolic syndrome in workers of a petrochemical enterprise: based on two assessment models of occupational stress. BMC Public Health, 24(1), 1–14. https://doi.org/10.1186/s12889-024-18305-3
Zobair, K. M., Sanzogni, L., & Sandhu, K. (2020). Telemedicine healthcare service adoption barriers in rural Bangladesh. Australasian Journal of Information Systems, 24, 1–24. https://doi.org/10.3127/ajis.v24i0.2165
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Unveiling Barriers to Telehealth Adoption among Employees with Metabolic Syndrome: Innovation Resistance Theory Perspective
Pages: 679-691
Copyright (c) 2025 Rafi Amani Muflih Rahardi, Chieviog Igiobye Isoagi, Muflihuun Retno Ningsih
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
