Telemedicine Adoption Among Nigerian Clinicians: Development and Validation of the Clinicians’ Telemedicine Adoption Model (CTAM).
Main Article Content
Abstract
This study explores the key factors that influence the adoption of telemedicine among Nigerian clinicians. It introduces the Clinicians’ Telemedicine Adoption Model (CTAM), an extension of the Unified Theory of Acceptance and Use of Technology (UTAUT), developed through a cross-sectional survey of 302 clinicians across eight government hospitals in Ondo State, Nigeria. Using SmartPLS 2.0 for structural equation modelling, the study examined relationships among Clinicians’ Telemedicine Adoption Model (CTAM) variables and analysed demographic factors including age, gender, and profession as potential moderators. The findings show that Clinicians’ Telemedicine Adoption Model (CTAM) accounts for 45% of the variance in clinicians’ behavioural intention to use telemedicine systems. Specifically, Performance Expectancy, Effort Expectancy, Facilitating Conditions, Social Influence, and Reinforcement Factors significantly influenced clinicians' intention to adopt telemedicine, consistent with the extended Unified Theory of Acceptance and Use of Technology (UTAUT) model. Furthermore, Human Factors were significantly moderated by younger clinicians, while the medical doctor profession significantly moderated the influence of Organisational Factors. Reinforcement Factors were also significantly moderated by age, gender, and profession. By understanding these determinants, hospital management boards and the Federal Ministry of Health can be better equipped to support the successful implementation of Nigeria’s national health ICT strategic framework, paving the way for more effective, technology-enhanced healthcare delivery in the near future.
Article Details
The Medical Research Archives grants authors the right to publish and reproduce the unrevised contribution in whole or in part at any time and in any form for any scholarly non-commercial purpose with the condition that all publications of the contribution include a full citation to the journal as published by the Medical Research Archives.
References
2. Agbeyangi, A. O., & Lukose, J. M. (2025). Telemedicine adoption and prospects in sub-Sahara Africa: a systematic review with a focus on South Africa, Kenya, and Nigeria. Paper presented at the Healthcare.
3. Ahmad, S., & Afthanorhan, W. M. A. B. W. (2014). The importance-performance matrix analysis in partial least square structural equation modeling (PLS-SEM) with smartpls 2.0 M3. International Journal of Mathematics Research, 3(1), 1.
4. Ajzen, I. (2011). The theory of planned behaviour: Reactions and reflections: Taylor & Francis.
5. AlAwadhi, S., & Morris, A. (2008). The Use of the UTAUT Model in the Adoption of E-government Services in Kuwait. Paper presented at the Hawaii International Conference on System Sciences, Proceedings of the 41st Annual.
6. Alibaygi, A., Karamidehkordi, M., & Pouya, M. (2012). Using the Delphi technique to assess cost-effectiveness of rural information and communications technologies (ICT) centers in Iran. Journal of Agricultural Extension and Rural Development, 4(20), 552-555.
7. Barclay, D., Higgins, C., & Thompson, R. (1995). The partial least squares (PLS) approach to casual modeling: personal computer adoption ans use as an Illustration.
8. Burton-Jones, A., & Hubona, G. S. (2006). The mediation of external variables in the technology acceptance model. Information & Management, 43(6), 706-717.
9. Chang, I.-C., & Hsu, H.-M. (2012). Predicting medical staff intention to use an online reporting system with modified unified theory of acceptance and use of technology. Telemedicine and e-Health, 18(1), 67-73.
10. Chang, I.-C., Hwang, H.-G., Hung, W.-F., & Li, Y.-C. (2007). Physicians’ acceptance of pharmacokinetics-based clinical decision support systems. Expert Systems with Applications, 33(2), 296-303.
11. Chang, M.-Y., Pang, C., Tarn, J. M., Liu, T.-S., & Yen, D. C. (2015). Exploring user acceptance of an e-hospital service: An empirical study in Taiwan. Computer Standards & Interfaces, 38, 35-43.
12. Chau, P. Y., & Hu, P. J. (2002). Examining a model of information technology acceptance by individual professionals: An exploratory study. Journal of management information systems, 18(4), 191-229.
13. Cimperman, M., Brenčič, M. M., & Trkman, P. (2016). Analyzing older users’ home telehealth services acceptance behavior—applying an Extended UTAUT model. International journal of medical informatics, 90, 22-31.
14. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340.
15. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: a comparison of two theoretical models. Management science, 35(8), 982-1003.
16. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace1. Journal of applied social psychology, 22(14), 1111-1132.
17. Dunnebeil, S., Sunyaev, A., Blohm, I., Leimeister, J. M., & Krcmar, H. (2012). Determinants of physicians' technology acceptance for e-health in ambulatory care. Int J Med Inform, 81(11), 746-760. doi:10.1016/j.ijmedinf.2012.02.002
18. Duyck, P., Pynoo, B., Devolder, P., Voet, T., Adang, L., Ovaere, D., & Vercruysse, J. (2010). Monitoring the PACS implementation process in a large university hospital—discrepancies between radiologists and physicians. Journal of Digital Imaging, 23(1), 73-80.
19. Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M., & Williams, M. D. (2017). Re-examining the Unified Theory of Acceptance and Use of Technology (UTAUT): Towards a Revised Theoretical Model. Information Systems Frontiers, 1-16.
20. Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M., & Williams, M. D. (2019). Re-examining the unified theory of acceptance and use of technology (UTAUT): Towards a revised theoretical model. Information Systems Frontiers, 21(3), 719-734.
21. Efron, B., & Tibshirani, R. J. (1994). An introduction to the bootstrap: CRC press.
22. Esmaeilzadeh, P., Sambasivan, M., & Kumar, N. (2010). The challenges and issues regarding e-health and health information technology trends in the healthcare sector E-business Technology and Strategy (pp. 23-37): Springer.
23. Fagan, M. H., Neill, S., & Wooldridge, B. R. (2008). Exploring the intention to use computers: An empirical investigation of the role of intrinsic motivation, extrinsic motivation, and perceived ease of use. Journal of Computer Information Systems, 48(3), 31-37.
24. Foon, Y. S., & Fah, B. C. Y. (2011). Internet banking adoption in Kuala Lumpur: an application of UTAUT model. International Journal of Business and Management, 6(4), 161.
25. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 39-50.
26. Gagnon, M.-P., Godin, G., Gagné, C., Fortin, J.-P., Lamothe, L., Reinharz, D., & Cloutier, A. (2003). An adaptation of the theory of interpersonal behaviour to the study of telemedicine adoption by physicians. International journal of medical informatics, 71(2), 103-115.
27. Gagnon, M.-P., Ouimet, M., Godin, G., Rousseau, M., Labrecque, M., Leduc, Y., & Abdeljelil, A. B. (2010). Study protocol Multi-level analysis of electronic health record adoption by health care professionals: A study protocol. Implementation Science, 30(5), 1-10.
28. Gagnon, M. P., Ghandour el, K., Talla, P. K., Simonyan, D., Godin, G., Labrecque, M., . . . Rousseau, M. (2014). Electronic health record acceptance by physicians: testing an integrated theoretical model. J Biomed Inform, 48, 17-27. doi:10.1016/j.jbi.2013.10.010
29. Gikandi, J. W., Morrow, D., & Davis, N. E. (2011). Online formative assessment in higher education: A review of the literature. Computers & Education, 57(4), 2333-2351. doi:10.1016/j.compedu.2011.0 6.004
30. Hailemariam, G., & Garfield, M. (2016). A Contextualized IT adoption and Use Model for e-health: The Case of Telemedicine at Black Lion Teaching Hospital, Ethiopia. AMCIS 2016 Proceedings. San Diego, 1-10.
31. Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing theory and Practice, 19(2), 139-152.
32. Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. (2012). An assessment of the use of partial least squares structural equation modeling in marketing research. Journal of the academy of marketing science, 40(3), 414-433.
33. Han, S., Mustonen, P., Seppanen, M., & Kallio, M. (2004). Physicians' behavior intentions regarding the use of mobile technology: an exploratory study. PACIS 2004 Proceedings. 49, 624-637.
34. Heart, T., & Kalderon, E. (2013). Older adults: are they ready to adopt health-related ICT? International journal of medical informatics, 82(11), e209-e231.
35. Holden, R. J., & Karsh, B.-T. (2010). The technology acceptance model: its past and its future in health care. Journal of biomedical informatics, 43(1), 159-172.
36. Holden, R. J., & Karsh, B. T. (2010). The technology acceptance model: its past and its future in health care. J Biomed Inform, 43(1), 159-172. doi:10.1016/j.jbi.2009.07.002
37. Hu, P. J., Chau, P. Y., Sheng, O. R. L., & Tam, K. Y. (1999). Examining the technology acceptance model using physician acceptance of telemedicine technology. Journal of management information systems, 91-112.
38. Ifinedo, P. (2012). Technology acceptance by health professionals in Canada: An analysis with a modified UTAUT model. Paper presented at the 45th International Conference on System Science (HICSS) Hawaii.
39. Isabalija, S. R., Mayoka, K. G., Rwashana, A. S., & Mbarika, V. W. (2011). Factors affecting adoption, implementation and sustainability of telemedicine information systems in Uganda. Journal of Health Informatics in Developing Countries, 5(2).
40. Karahanna, E., Straub, D. W., & Chervany, N. L. (1999). Information technology adoption across time: a cross-sectional comparison of pre-adoption and post-adoption beliefs. MIS quarterly, 183-213.
41. Kijsanayotin, B., Pannarunothai, S., & Speedie, S. M. (2009). Factors influencing health information technology adoption in Thailand's community health centers: Applying the UTAUT model. International journal of medical informatics, 78(6), 404-416.
42. Kissi, J., Dai, B., Dogbe, C. S., Banahene, J., & Ernest, O. (2020). Predictive factors of physicians’ satisfaction with telemedicine services acceptance. Health informatics journal, 26(3), 1866-1880.
43. Kohnke, A., Cole, M. L., & Bush, R. (2014). Incorporating UTAUT Predictors for Understanding Home Care Patients' and Clinician's Acceptance of Healthcare Telemedicine Equipment. Journal of technology management & innovation, 9(2), 29-41.
44. LapãO, L. V., & Lopes, M. (2013). Managing health systems in a globalized world: Telemedicine service improves access to pediatric cardiology in Cape Verde. Paper presented at the 2013 IST-Africa Conference & Exhibition.
45. Lasierra, N., Alesanco, A., Gilaberte, Y., Magallón, R., & García, J. (2012). Lessons learned after a three-year store and forward teledermatology experience using internet: Strengths and limitations. International journal of medical informatics, 81(5), 332-343.
46. Lee, H. W., Ramayah, T., & Zakaria, N. (2012). External factors in hospital information system (HIS) adoption model: a case on malaysia. Journal of medical systems, 36(4), 2129-2140.
47. Lin, H. S., & Stead, W. W. (2009). Computational Technology for Effective Health Care:: Immediate Steps and Strategic Directions: National Academies Press.
48. Lister, G., & Jakubowski, E. (2008). Public engagement in health policy: International lessons. Journal of Management & Marketing in Healthcare, 1(2), 154-165.
49. Maarop, N., & Win, K. T. (2012). Understanding the need of health care providers for teleconsultation and technological attributes in relation to the acceptance of teleconsultation in Malaysia: a mixed methods study. J Med Syst, 36(5), 2881-2892. doi:10.1007/s10916-011-9766-2
50. Macabasag, R. L. A., Magtubo, K. M. P., & Marcelo, P. G. F. (2016). Implementation of telemedicine services in lower-middle income countries: lessons for the Philippines. Journal of the International Society for Telemedicine and eHealth, 4, e24 (21-11).
51. Mansouri-Rad, P., Mahmood, M. A., Thompson, S. E., & Putnam, K. (2013). Culture Matters: Factors Affecting the Adoption of Telemedicine. Paper presented at the System Sciences (HICSS), 2013 46th Hawaii International Conference on.
52. Mars, M. (2013). Telemedicine and Advances in Urban and Rural Healthcare Delivery in Africa. Progress in cardiovascular diseases, 56(3), 326-335.
53. Mbarika, V. W. A., & Okoli, C. (2003). Telemedicine in sub-Saharan Africa: A proposed Delphi study. Paper presented at the 36th Annual Hawaii International Conference on System Sciences, 2003. Proceedings of the.
54. Mullan, F., & Frehywot, S. (2008). Non-physician clinicians in 47 sub-Saharan African countries. The Lancet, 370(9605), 2158-2163.
55. Murererehe, J., Uwambaye, P., Isyagi, M., Nyandwi, T., & Njunwa, K. (2017). Knowledge, attitude and practices of dental professionals in Rwanda towards the benefits and applications of teledentistry. Rwanda Journal, 4(1), 39-47.
56. Nazari, J. A., Herremans, I. M., Isaac, R. G., Manassian, A., & Kline, T. J. (2011).
57. Obi-Jeff, C., Garcia, C., Onuoha, O., Adewumi, F., David, W., Bamiduro, T., . . . Wonodi, C. (2021). Designing an SMS reminder intervention to improve vaccination uptake in Northern Nigeria: a qualitative study. BMC health services research, 21(1), 844.
58. Onyeabor, U. S., Okenwa, W. O., Onwuasoigwe, O., Lasebikan, O. A., Schaaf, T., Pinkwart, N., & Balzer, F. (2024). Telemedicine in the age of the pandemics: The prospects of web-based remote patient monitoring systems for orthopaedic ambulatory care management in the developing economies. Digital health, 10, 20552076241226964.
59. Organizational culture, climate and IC: an interaction analysis. Journal of Intellectual Capital, 12(2), 224-248.
60. Olver, I. N., & Selva-Nayagam, S. (2000). Evaluation of a telemedicine link between Darwin and Adelaide to facilitate cancer management. Telemedicine Journal, 6(2), 213-218.
61. Ozturk, M. A. (2011). Confirmatory Factor Analysis of the Educators' Attitudes toward Educational Research Scale. Educational Sciences: Theory and Practice, 11(2), 737-748.
62. Patel, R. N., & Antonarakis, G. S. (2013). Factors influencing the adoption and implementation of teledentistry in the UK, with a focus on orthodontics. Community Dent Oral Epidemiol, 41(5), 424-431. doi:10.1111/cdoe.12029
63. Peeters, J. M., de Veer, A. J., van der Hoek, L., & Francke, A. L. (2012). Factors influencing the adoption of home telecare by elderly or chronically ill people: a national survey. Journal of clinical nursing, 21(21-22), 3183-3193.
64. Peterson, R. A. (1994). A meta-analysis of Cronbach's coefficient alpha. Journal of consumer research, 21(2), 381-391.
65. Rho, M. J., Choi, I., & Lee, J. (2014). Predictive factors of telemedicine service acceptance and behavioral intention of physicians. International journal of medical informatics.
66. Rogers, E. M. (2004). A prospective and retrospective look at the diffusion model. Journal of Health Communication, 9(S1), 13-19.
67. Sagaro, G. G. G., BATTINENI, G., & AMENTA, F. (2019). A Review on Barriers to Sustainable Telemedicine Implementation in Ethiopia.
68. Saigí-Rubió, F., Torrent-Sellens, J., & Jiménez-Zarco, A. (2014). Drivers of telemedicine use: comparative evidence from samples of Spanish, Colombian and Bolivian physicians. Implementation Science, 9(1), 128.
69. Santos, J. R. A. (1999). Cronbach’s alpha: A tool for assessing the reliability of scales. Journal of extension, 37(2), 1-5.
70. Schaper, L., & Pervan, G. (2007). A model of information and communication technology acceptance and utilisation by occupational therapists. Stud. Health Technol. Inform, 130, 91-101.
71. Shibl, R., Lawley, M., & Debuse, J. (2013). Factors influencing decision support system acceptance. Decision Support Systems, 54(2), 953-961. doi:10.1016/j.dss.2012.09.018
72. Sitzia, J. (1999). How valid and reliable are patient satisfaction data? An analysis of 195 studies. International Journal for Quality in Health Care, 11(4), 319-328.
73. Skinner, B. F. (1963). Operant behavior. American Psychologist, 18(8), 503-515.
74. Susanto, T. D., & Aljoza, M. (2015). Individual acceptance of e-Government services in a developing country: Dimensions of perceived usefulness and perceived ease of use and the importance of trust and social influence. Procedia Computer Science, 72, 622-629.
75. Taylor, S., & Todd, P. (1995a). Assessing IT usage: The role of prior experience. MIS quarterly, 561-570.
76. Taylor, S., & Todd, P. (1995b). Decomposition and crossover effects in the theory of planned behavior: A study of consumer adoption intentions. International journal of research in marketing, 12(2), 137-155.
77. Ukaoha, K., & Egbokhare, F. (2012). Prospects and Challenges of Telemedicine in Nigeria,.
78. van Gurp, J., Soyannwo, O., Odebunmi, K., Dania, S., van Selm, M., van Leeuwen, E., . . . Hasselaar, J. (2015). Telemedicine’s potential to support good dying in Nigeria: a qualitative study. PloS one, 10(6), e0126820.
79. Venkatesh, V., Brown, S. A., & Bala, H. (2013). Bridging the qualitative-quantitative divide: Guidelines for conducting mixed methods research in information systems. MIS quarterly, 37(1), 21-54.
80. Venkatesh, V., Chan, F. K., & Thong, J. Y. (2012). Designing e-government services: Key service attributes and citizens’ preference structures. Journal of Operations Management, 30(1), 116-133.
81. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46(2), 186-204.
82. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 425-478.
83. Venkatesh, V., Morris, M. G., Sykes, T. A., & Ackerman, P. L. (2004). Individual reactions to new technologies in the workplace: the role of gender as a psychological construct. Journal of Applied Social Psychology, 34(3), 445-467.
84. Venkatesh, V., Sykes, T. A., & Zhang, X. (2011). 'Just what the doctor ordered': a revised UTAUT for EMR system adoption and use by doctors. Paper presented at the 44th International Conference on System Sciences (HICSS), Hawaii
85. Ward, R., Stevens, C., Brentnall, P., & Briddon, J. (2008). The attitudes of health care staff to information technology: a comprehensive review of the research literature. Health Information & Libraries Journal, 25(2), 81-97.
86. Xue, Y., Liang, H., Mbarika, V., Hauser, R., Schwager, P., & Getahun, M. K. (2015). Investigating the resistance to telemedicine in Ethiopia. International journal of medical informatics, 84(8), 537-547.
87. Yarbrough, A. K., & Smith, T. B. (2007). Technology acceptance among physicians: a new take on TAM. Medical Care Research and Review.
88. Yeow, P. H., & Loo, W. (2010). Acceptability of ATM and transit applications embedded in multipurpose smart identity card: An exploratory study in Malaysia. Applied Technology Integration in Governmental Organizations: New E-Government Research: New E-Government Research, 118.
89. Zailani, S., Gilani, M. S., Nikbin, D., & Iranmanesh, M. (2014). Determinants of telemedicine acceptance in selected public hospitals in Malaysia: Clinical perspective. Journal of medical systems, 38(9), 1-12.
90. Zakaria, N., Affendi, M., Yusof, S., & Zakaria, N. (2010). Managing ICT in healthcare organization: culture, challenges, and issues of technology adoption and implementation.
91. Zanaboni, P., Knarvik, U., & Wootton, R. (2014). Adoption of routine telemedicine in Norway: the current picture. Glob Health Action, 7, 22801. doi:10.3402/gha.v7.22801
92. Zanaboni, P., & Wootton, R. (2012). Adoption of telemedicine: from pilot stage to routine delivery. BMC medical informatics and decision making, 12(1), 1.