Beyond Technology: Social Support, Risk, and Economic Value in Physicians’ Telemedicine Adoption in Indonesia
DOI:
https://doi.org/10.30953/thmt.v10.615Keywords:
Actual use behavior, economic value, perceived digital risk, self-efficacy, social support, telemedicineAbstract
Objectives: Physician technology adoption is critical to improving healthcare delivery. This study examines the direct impact of economic value, perceived risk, and social support on the actual use of technology by physicians’ in Indonesia, extending the traditional Technology Acceptance Model (TAM) by focusing on actual use rather than intention. It also tests self-efficacy as a moderating factor.
Methods: A cross-sectional survey was conducted with 244 physicians. The proposed model integrates core TAM constructs with self-efficacy as a moderator. The relationships were tested using partial least squares structural equation modeling (PLS-SEM).
Results: The model shows that economic value and social support positively influence physicians’ actual use, while perceived risk has a negative effect. Self-efficacy strengthens the impact of social support but does not moderate the effects of economic value or perceived risk. These findings underline the critical role of peer and superior support in driving real usage behavior when physicians feel confident.
Conclusion: This study contributes novel evidence by directly measuring actual use, which is less explored in TAM research. The findings highlight the need to strengthen supportive environments and build physicians’ confidence to boost technology adoption. Future research should test this model across broader healthcare contexts and over time.
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References
Tabaeeian RA, Hajrahimi B, Khoshfetrat A. A systematic review of telemedicine systems use barriers: primary health
care providers’ perspective. J Sci Technol Policy Manag. 2024;15:610–35.
Chakraborty I, Ilavarasan PV, Edirippulige S. What helps agetech startups scale-up? Some insights. Int Entrepreneurship Manag J. 2025 Dec 1;21(1). https://doi.org/10.1007/ s11365-025-01081-w
Balogun AY. Strengthening compliance with data privacy regulations in U.S. healthcare cybersecurity. Asian J Res Comput Sci. 2025 Jan 18;18(1):154–73. https://doi.org/10.9734/ajrcos/2025/ v18i1555
Sengupta A, Sarkar S, Bhattacherjee A. The relationship between telemedicine tools and physician satisfaction, quality of care, and patient visits during the COVID-19 pandemic. Int J Med Inform. 2024 Oct 1;190. https://doi.org/10.1016/j.ijmedinf.2024.105541
Orsayeva R, Berestova A, Krasilnikova V, Timoshin A. Telemedicine during COVID- 19: features of legal regulation
in the field of administrative liability for errors committed by medical institutions. Egypt J Forensic Sci. 2025;15. https://doi.org/10.1186/s41935-025-00443-3
Susanti R, Syarkani Y, Ratna D, Handayani S, Waluyo I. 3 Indonesian Manual Manipulative Association.
Lee AT, Ramasamy RK, Subbarao A. 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 (Switzerland). 2025;13. https://doi.org/10.3390/healthcare13030250
Bîlbîie A, Puiu AI, Mihăilă V, Burcea M. Investigating physicians’ adoption of telemedicine in romania using technology acceptance model (TAM). Healthcare (Switzerland). 2024 Aug1;12(15).
Senthilrajah T, Ahangama S. The Sri Lankan enigma: demystifying public healthcare information systems acceptance. BMC Health Serv Res. 2025 Dec 1;25(1). https://doi.org/10.1186/s12913-024-12173-8.
Wang Z, Wang Y, Zeng Y, Su J, Li Z. An investigation into the acceptance of intelligent care systems: an extended technology acceptance model (TAM). Sci Rep. 2025 Dec 1;15(1). https:// doi.org/10.1038/s41598-025-02746-w
Hussain A, Zhiqiang M, Li M, Jameel A, Kanwel S, Ahmad S, et al. The mediating effects of perceived usefulness and perceived ease of use on nurses’ intentions to adopt advancedtechnology. BMC Nurs. 2025 Dec 1;24(1). https://doi.org/10.1186/s12912-024-02648-8
Tan SH, Wong CK, Yap YY, Tan SK. Factors influencing telemedicine adoption among physicians in the Malaysian
healthcare system: a revisit. Digit Health. 2024 Jan 1;10. https://doi.org/10.1177/20552076241257050
Mutiah F, Sibuea H, Candra M. Telemedicine regulation in Indonesia: legal frameworks, challenges, and future directions. Journal Multidisiplin Indonesia. 2025;4(4):242–51. https://doi.org/10.58344/jmi.v4i4.2267
Sugandi MS, Mulyana D, Elita RFM, Rusmana A. Lessons learned from the pandemic in indonesia: enhancing
doctor-patient medical communication to improve diagnostic certainty in telemedicine. J Law Sustain Dev. 2023 Nov 14;11(11):e2076. https://doi.org/10.55908/sdgs.v11i11.2076
Amir AS. User experience in accessing and utilizing digital health systems: a phenomenological study exploring the lived experiences of medical practitioners and patients on telemedicine platforms in Indonesia. J Digit Health Innov MedTechnol. 2025;1.
Cheng Q, Fattah RA, Susilo D, Satrya A, Haemmerli M, Kosen S, et al. Determinants of healthcare utilization under the Indonesian national health insurance system—a cross-sectional study. BMC Health Serv Res. 2025 Dec 1;25(1). https://doi.org/10.1186/s12913-024-11951-8
Mahendradhata Y, Andayani NLPE, Hasri ET, Arifi MD, Siahaan RGM, Solikha DA, et al. The capacity of the
Indonesian healthcare system to respond to COVID-19. Front Public Health. 2021 Jul 7;9. https://doi.org/10.3389/fpubh.2021.649819
Husin L, Idris F, Liberty IA. The role of BPJS Kesehatan in improving health access equity in Indonesia. Public Health Policy Admin J. 2025. Available from: https://publichealthadminjournal.org/index.php/publichealthadmin
Tian Y, Chan TJ. Predictors of mobile payment use applications from the extended technology acceptance model: does self-efficacy and trust matter? Sage Open. 2024 Oct 1;14(4). https://doi.org/10.1177/21582440241292525
Chaudhary P, Payal, Nain P, Pooja, Rana P, Verma P, et al. Perceived risk of infection, ethical challenges and
motivational factors among frontline nurses in Covid-19 pandemic: prerequisites and lessons for future pandemic. BMC Nurs. 2024 Dec 1;23(1). https://doi.org/10.1186/s12912-023-01653-7
Hodson N, Powell BJ, Nilsen P, Beidas RS. How can a behavioral economics lens contribute to implementation science? Implement Sci. 2024 Dec 1;19(1). https://doi.org/10.1186/s13012-024-01362-y
Chen SY, Kuo HY, Chang SH. Perceptions of ChatGPT in healthcare: usefulness, trust, and risk. Front Public Health. 2024;12. https://doi.org/10.3389/fpubh.2024.1457131
Lerch SP, Hänggi R, Bussmann Y, Lörwald A. A model of contributors to a trusting patient-physician relationship:
a critical review using a systematic search strategy. BMC Prim Care. 2024 Dec 1;25(1). https://doi.org/10.1186/
s12875-024-02435-z
Kim BJ, Kim MJ. The influence of work overload on cybersecurity behavior: a moderated mediation model of psychological contract breach, burnout, and self-efficacy in AI learning such as ChatGPT. Technol Soc. 2024 Jun 1;77. https://doi.org/10.1016/j.techsoc.2024.102543
Yang P, Xu R, Le Y. Factors influencing sports performance: a multi-dimensional analysis of coaching quality, athlete well-being, training intensity, and nutrition with self-efficacy mediation and cultural values moderation. Heliyon. 2024 Sep 15;10(17). https://doi.org/10.1016/j.heliyon.2024.e36646
Sarstedt M, Ringle CM, Hair JF. Partial least squares structural equation modeling. In: Handbook of market research. Springer International Publishing; 2021. p. 1–47.
Shaarani I, Jounblat M, Jounblat H, Ghanem A, Mansour R, Taleb R. Developing and validating a tool to assess
telemedicine acceptance among physicians during pandemic using a technology acceptance model. Telemed
e-Health. 2023 Jun 1;29(6):903–11. https://doi.org/10.1089/tmj.2022.0348
Stoumpos AI, Kitsios F, Talias MA. Digital transformation in healthcare: technology acceptance and its applications. Int J Environ Res Public Health. 2023 Feb 1;20(4).
Alsahli S, Hor SY, Lam MK. Physicians’ acceptance and adoption of mobile health applications during the COVID-19 pandemic in Saudi Arabia: extending the unified theory of acceptance and use of technology model.
Health Inf Manag J. 2024. https://doi.org/10.1177/18333583241300534
de Blanes Sebastián MG, Antonovica A, Sarmiento Guede JR. What are the leading factors for using Spanish peerto-peer mobile payment platform Bizum? The applied analysis of the UTAUT2 model. Technol Forecast
Soc Change. 2023 Feb 1;187. https://doi.org/10.1016/j.techfore.2022.122235
Meng D, Guo Z. Influence of doctor-patient trust on the adoption of mobile medical applications during the epidemic: a UTAUT-based analysis. Front Public Health. 2024;12. https://doi.org/10.3389/fpubh.2024.1414125
Ram S, Sheth JN. Consumer resistance to innovations: the marketing problem and its solutions. J Consum Market.
;6(2):5. https://doi.org/10.1108/EUM0000000002542
Kaur P, Dhir A, Singh N, Sahu G, Almotairi M. An innovation resistance theory perspective on mobile payment solutions. J Retail Consum Serv. 2020 Jul 1;55. https://doi.org/10.1016/j.jretconser.2020.102059
Henseler J, Ringle CM, Sarstedt M. A new criterion for assessing discriminant validity in variance-based structural equation modeling. J Acad Mark Sci. 2015 Jan 1;43(1):115–35. https://doi.org/10.1007/s11747-014-0403-8
Ringle CM, Sarstedt M, Sinkovics N, Sinkovics RR. A perspective on using partial least squares structural equation
modelling in data articles. Data Brief. 2023 Jun 1;48. https://doi.org/10.1016/j.dib.2023.109074
Hair JF, Risher JJ, Sarstedt M, Ringle CM. When to use and how to report the results of PLS-SEM. Eur Bus Rev. 2019;34:2–24. https://doi.org/10.1016/j.dib.2023.109074
Dann D, Teubner T, Adam MTP, Weinhardt C. Where the host is part of the deal: social and economic value in the platform economy. Electron Commer Res Appl. 2020 Mar 1;40. https://doi.org/10.1016/j.elerap.2019.100923
Marafon DL, Basso K, Espartel LB, de Barcellos MD, Rech E. Perceived risk and intention to use internet banking:
the effects of self-confidence and risk acceptance. Int J Bank Market. 2018;36(2):277–89. https://doi.org/10.1108/
IJBM-11-2016-0166
Li W, Guo J, Liu W, Tu J, Tang Q. Effect of older adults willingness on telemedicine usage: an integrated approach basedon technology acceptance and decomposed theory of planned behavior model. BMC Geriatr. 2024 Dec 1;24(1). https://doi.org/10.1186/s12877-024-05361-y
Bashir L, Madhavaiah C. Trust, social influence, self-efficacy, perceived risk and internet banking acceptance:
an extension of technology acceptance model in Indian context. Metamorphosis. 2015;14(1):25–38. https://doi.org/10.1177/0972622520150105
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Copyright (c) 2025 Elika Setiawaty, PhD, Hartoyo Hartoyo, PhD, Rita Nurmalina, PhD, Lilik Noor Yuliati, PhD

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