Research Article
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Year 2024, Volume: 9 Issue: 3, 346 - 367, 27.11.2024
https://doi.org/10.31201/ijhmt.1568873

Abstract

References

  • Abdekhoda, M., Ahmadi, M., Gohari, M., & Noruzi, A. (2015). The effects of organizational contextual factors on physicians’ attitude toward adoption of Electronic Medical Records. Journal of Biomedical Informatics 53: 174-179. https://doi.org/10.1016/j.jbi.2014.10.008
  • Aggelidis, V. P., & Chatzoglou, P. D. (2009). Using a Modified Technology Acceptance Model in Hospitals. International Journal of Medical Informatics, 78(2): 115-126.
  • AlQudah, A., Salloum, S. A., & Shaalan, K. (2021). The Role of Technology Acceptance in Healthcare to Mitigate COVID-19 Outbreak. Emerging Technologies During the Era of COVID-19 Pandemic. I. A.-E.-S. In: Arpaci (Dü.) Emerging Technologies During the Era of COVİD-19 Pandemic (Volume 348). Springer, Cham. doi:https://doi.org/10.1007/978-3-030-67716-9_14
  • Alsyouf, A., Ishak, A. K., Lutfi, A., Alhazmi, F. N., & Al-Okaily, M. (2022). The Role of Personality and Top Management Support in Continuance Intention to Use Electronic Health Record Systems among Nurses. International Journal of Environmental Research Public Health 19(22).
  • Atilla, A., & Seyhan, F. (2022). An Academic Examination Of The Development Of Health Informatics. Süleyman Demirel University Visionary Journal,, 13(34), 364-381.
  • Avaner, T., & Fedai, R. (2017). Digitalization in Health Services: Using Information Systems in Health Administration. Suleyman Demirel University The Journal of Faculty of Economics and Administrative Sciences, 22(15), 1533-1542.
  • Başak, E., Gümüşsoy, Ç. A., & Çalışır, F. (2015). Examining the Factors Affecting PDA Acceptance Among Physicians: An Extend Tevhnology Acceptance Model. Journal of Healthcare Engineering 6(3): 399-418.
  • Boontarig, W., Chutimaskul, W., Chongsuphajaisiddhi, V., & B. Papasratorn. (2012). Factors influencing the Thai elderly intention to use smartphone for e-Health services. 2012 IEEE Symposium on Humanities, Science and Engineering Research. Kuala Lumpur, Malaysia,.
  • Breil, B., Salewski, C., & Apolinário-Hagen, J. (2022). Comparing the Acceptance of Mobile Hypertension Apps for Disease Management Among Patients Versus Clinical Use Among Physicians: Cross-sectional Survey. JMIR Cardio 6(1). doi:10.2196/31617
  • Cobelli, N., & Blasi, S. (2024). Combining topic modeling and bibliometric analysis to understand the evolution of technological innovation adoption in the healthcare industry. European Journal of Innovation Management 27(9).
  • Çalışkan, S. (2017). Antecedents of Consumer Intention to Use Personal Health Technologies:Revisiting the Technology Acceptance Model. PhD Dissertation. İstanbul: Boğaziçi University Diel, S., Doctor, E., Reith, R., C. B., & Eymann, T. (2023). Examining supporting and constraining factors of physicians’ acceptance of telemedical online consultations: a survey study. BMC Health Services Research 23. doi:https://doi.org/10.1186/s12913-023-10032-6
  • Dönmez, S., & Uğurluoğlu, Ö. (2017). Stakeholder Analysis In Health Care Organisations. Int. Journal of Management Economics and Business, 13(1), 223-245.
  • Dupont, D., Beresniak, A., Sundgren, M., Schmidt, A., Ainsworth, J., Coorevits, P., . . . Moor, G. D. (2017). Business analysis for a sustainable, multi-stakeholder ecosystem for leveraging the Electronic Health Records for Clinical Research (EHR4CR) platform in Europe,. International Journal of Medical Informatics, 97, 341-352.
  • Grood, C., Raissi, A., Kuran, Y., & Sanatana, M. J. (2016). Adoption of e-Health Technology By Pysicians: A Scoping Review. Journal of Multidisciplinary Healthcare 9: 335-344. doi:doi:10.2147/JMDH.S103881
  • Heselmans, A., Aertgeerts, B., Donceel, P., Geens, S., Velde, S. V., & Ramaekers, D. (2012). Family Physicians’ Perceptions and Use of Electronic Clinical Decision Support During the First Year of Implementation. Journal of Medical Systems 36: 3677-3684.
  • Holden, R. J., & Karsh, B.-T. (2010). The Technology Acceptance Model: Its past and future in health care. Journal of Biomedical Informatics 43(1): 159-172. doi:https://doi.org/10.1016/j.jbi.2009.07.002
  • Hoque, M. R., Albar, A., & Alam, J. (2016). Factors infuencing physicians' acceptance of e-Health in developing country: An empirical study. International Journal of Healthcare Information Systems and Informatics 11(1): 58 - 70.
  • Hossain, A., Quaresma, R., & Rahman, H. (2019). Investigating factors influencing the physicians’ adoption of electronic health record (EHR) in healthcare system of Bangladesh: An empirical study,. International Journal of Information Management 44:76-87. doi:https://doi.org/10.1016/j.ijinfomgt.2018.09.016
  • Howitt, D., & Cramer, D. (2011). Introduction to SPSS Statistics in Psychology: For Version 19 and Earlier (5. b.). London: Pearson Education Limited.
  • Jalali, M. S., Landman, A., & Gordon, W. J. (2021). Telemedicine, privacy, and information security in the age of COVID-19. Journal of the American Medical Informatics Association 28(3): 671–672. doi:https://doi.org/10.1093/jamia/ocaa310
  • Kayserili, A., & Tefiroglu, E. C. (2023). Evaluation of Digital Healthcare Services by Hospital Administrators. Abant Journal of Health Sciences and Technologies, 3(2), 26-38.
  • Kissi, J., Dai, B., Dogbe, C. S., Banahene, J., & Ernest, O. (2020). Predictive factors of physicians’ satisfaction with telemedicine services acceptance. Health Informatics Journal 3: 1866-1880. doi:doi:10.1177/1460458219892162
  • Lind, D., Marchal, W., & Wathen, S. (2006). Basic Statistics for Business and Economics (5. b.). United States: McGraw-Hill Companies.
  • Liu, L., Cruz, A. M., Rincon, A. R., Buttar, V., Ranson, Q., & Goertzen, D. (2015). What factors determine therapists’ acceptance of new technologies for rehabilitation – a study using the Unified Theory of Acceptance and Use of Technology (UTAUT). Disability and Rehabilitation 37(5): 447-455. doi:https://doi.org/10.3109/09638288.2014.923529
  • McKillup, S. (2012). Statistics Explained: An Introductory Guide for Life Scientists (2. b.). United States: Cambridge University Press.
  • Orhan, S., Gümüş, M., & Kızılkaya, E. (2021). Health Information System in Turkey. International Journal of Social, Humanities and Administrative Sciences, 7(39), 645-653.
  • Penney, E. K., Agyei, J., Boadi, E. K., Abrokwah, E., & Ofori-Boafo, R. (2021). Understanding Factors That Influence Consumer Intention to Use Mobile Money Services: An Application of UTAUT2 With Perceived Risk and Trust. Sage Open 11(3).
  • Sema, F. D., Kebede, A. G., Soworsu, G. Z., Mengistu, T. T., Assen, H. E., Muche, E. A., . . . Seid, A. M. (2024). Perception of Healthcare Professionals towards Electronic-Prescribing at University of Gondar Comprehensive Specialized Hospital, Northwest Ethiopia: A Cross-Sectional Study. BioMed Research International. doi:10.1155/2024/6553470
  • Sergueeva, K., Shaw, N., & Lee, S. H. (2019). Understanding the barriers and factors associated with consumer adoption of wearable technology devices in managing personal health. Canadian Journal of Administrative Sciences 37(1): 45-60.
  • Steininger, K., Stiglbauer, B., Baumgartner, B., & Bernhard Engleder. (2014). Factors Explaining Physicians' Acceptance of Electronic Health Records. 47th Hawaii International Conference on System Sciences. Waikoloa, HI, USA, pp. 2768-2777. doi:doi: 10.1109/HICS
  • Tabachnick, B., & Fidell, L. S. (2013). Using Multivariate Statistics. Boston: Pearson İnternatioanal Edition.
  • Taherdoost, H. (2017). Determining Sample Size; How to Calculate Survey Sample Size . International Journal of Economics and Management Systems 2: 237-239. https://ssrn.com/abstract=3224205
  • The Ministry of Health (2021). Health Statistics Yearbook 2019. Ankara: The Ministry of Health, General Directorate of Health Information Systems. Ministry of Health Publication No: 1185, https://dosyasb.saglik.gov.tr/Eklenti/40564/0/saglik-istatistikleri-yilligi-2019pdf.pdf
  • Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: toward a unified view. MIS Quarterly 27(3): 425–478.
  • Venkatesh, V., Thong, J., & Xu, X. (2012). Consumer Acceptance and Use of Information Tevhnology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Querterly, 36(1): 157-178.
  • Wu, P., Zhang, R., Luan, J., & Zhu, M. (2022). Factors affecting physicians using mobile health applications: an empirical study. BMC Health Services Research 22(24). doi:https://doi.org/10.1186/s12913-021-07339-7
  • Yen, P.-Y., McAlearney, A. S., Sieck, C. J., Hefner, J. L., & Huerta, T. R. (2017). Health Information Technology (HIT) Adaptation: Refocusing on the Journey to Successful HIT Implementation. JMIR Medical Informatics 5(3). https://medinform.jmir.org/2017/3/e28
  • Yu, C.-W., Chao, C.-M., Chang, C.-F., Chen, R.-J., Chen, P.-C., & Liu, a. Y.-X. (2021, Chen-Wei Yu, Cheng-Min Chao https://orcid.org/0000-0003-4). Exploring Behavioral Intention to Use a Mobile Health Education Website: An Extension of the UTAUT 2 Model . Sage Open 11(4). doi:https://doi.org/10.1177/21582440211055721
  • Zhou, T. (2012). Examining mobile banking user adoption from the perspectives of trust and flow experience. Information Technology and Management 13: 27-37. doi:https://doi.org/10.1007/s10799-011-0111-8

An Examination of Factors Influencing Physicians' Acceptance and Use of the e-Nabız System

Year 2024, Volume: 9 Issue: 3, 346 - 367, 27.11.2024
https://doi.org/10.31201/ijhmt.1568873

Abstract

Aim: This study aims to identify the factors influencing physicians' acceptance and use of the e-Nabız system by comparing two models.
Methods: Conducted with 388 physicians from university hospitals across Turkey, the study utilized an online survey based on the Unified Theory of Acceptance and Use of Technology (UTAUT) scale. Descriptive analyses, frequency and percentage distributions, reliability analysis, confirmatory factor analysis, and structural equation modelling were applied to the collected data.
Results: In Model 1, the behavioral intention was influenced by performance expectancy and social influence, while usage behavior was shaped by social influence, facilitating conditions, and behavioral intention. Model 1 accounted for 75% of the variance in behavioral intention and 69% in usage behavior. In contrast, Model 2 identified performance expectancy, anxiety, habit, personal technology innovativeness, and workflow as significant predictors of behavioral intention. Usage behavior in Model 2 was influenced by habit, facilitating conditions, anxiety, personal technology innovativeness, workflow, and behavioral intention, explaining 84% of the variance in behavioral intention and 85% in usage behavior.
Conclusion: The findings indicate that Model 2 provides a more comprehensive explanation of the factors affecting the acceptance and use of the system. To enhance acceptance and usage, the study suggests focusing on anxiety management, emphasizing performance benefits, aligning the system with workflow, educating users about new technologies, and implementing incentives to foster habitual use. Future research should explore other technology acceptance models in various healthcare information systems to deepen understanding.

Ethical Statement

This study was approved by Ethics Committee of Karabuk University, Social Sciences and Humanities Research Ethics Committee (Approval date: 15/11/2019; Number: E.47083)

Thanks

We would like to thank the physicians who participated in the study.

References

  • Abdekhoda, M., Ahmadi, M., Gohari, M., & Noruzi, A. (2015). The effects of organizational contextual factors on physicians’ attitude toward adoption of Electronic Medical Records. Journal of Biomedical Informatics 53: 174-179. https://doi.org/10.1016/j.jbi.2014.10.008
  • Aggelidis, V. P., & Chatzoglou, P. D. (2009). Using a Modified Technology Acceptance Model in Hospitals. International Journal of Medical Informatics, 78(2): 115-126.
  • AlQudah, A., Salloum, S. A., & Shaalan, K. (2021). The Role of Technology Acceptance in Healthcare to Mitigate COVID-19 Outbreak. Emerging Technologies During the Era of COVID-19 Pandemic. I. A.-E.-S. In: Arpaci (Dü.) Emerging Technologies During the Era of COVİD-19 Pandemic (Volume 348). Springer, Cham. doi:https://doi.org/10.1007/978-3-030-67716-9_14
  • Alsyouf, A., Ishak, A. K., Lutfi, A., Alhazmi, F. N., & Al-Okaily, M. (2022). The Role of Personality and Top Management Support in Continuance Intention to Use Electronic Health Record Systems among Nurses. International Journal of Environmental Research Public Health 19(22).
  • Atilla, A., & Seyhan, F. (2022). An Academic Examination Of The Development Of Health Informatics. Süleyman Demirel University Visionary Journal,, 13(34), 364-381.
  • Avaner, T., & Fedai, R. (2017). Digitalization in Health Services: Using Information Systems in Health Administration. Suleyman Demirel University The Journal of Faculty of Economics and Administrative Sciences, 22(15), 1533-1542.
  • Başak, E., Gümüşsoy, Ç. A., & Çalışır, F. (2015). Examining the Factors Affecting PDA Acceptance Among Physicians: An Extend Tevhnology Acceptance Model. Journal of Healthcare Engineering 6(3): 399-418.
  • Boontarig, W., Chutimaskul, W., Chongsuphajaisiddhi, V., & B. Papasratorn. (2012). Factors influencing the Thai elderly intention to use smartphone for e-Health services. 2012 IEEE Symposium on Humanities, Science and Engineering Research. Kuala Lumpur, Malaysia,.
  • Breil, B., Salewski, C., & Apolinário-Hagen, J. (2022). Comparing the Acceptance of Mobile Hypertension Apps for Disease Management Among Patients Versus Clinical Use Among Physicians: Cross-sectional Survey. JMIR Cardio 6(1). doi:10.2196/31617
  • Cobelli, N., & Blasi, S. (2024). Combining topic modeling and bibliometric analysis to understand the evolution of technological innovation adoption in the healthcare industry. European Journal of Innovation Management 27(9).
  • Çalışkan, S. (2017). Antecedents of Consumer Intention to Use Personal Health Technologies:Revisiting the Technology Acceptance Model. PhD Dissertation. İstanbul: Boğaziçi University Diel, S., Doctor, E., Reith, R., C. B., & Eymann, T. (2023). Examining supporting and constraining factors of physicians’ acceptance of telemedical online consultations: a survey study. BMC Health Services Research 23. doi:https://doi.org/10.1186/s12913-023-10032-6
  • Dönmez, S., & Uğurluoğlu, Ö. (2017). Stakeholder Analysis In Health Care Organisations. Int. Journal of Management Economics and Business, 13(1), 223-245.
  • Dupont, D., Beresniak, A., Sundgren, M., Schmidt, A., Ainsworth, J., Coorevits, P., . . . Moor, G. D. (2017). Business analysis for a sustainable, multi-stakeholder ecosystem for leveraging the Electronic Health Records for Clinical Research (EHR4CR) platform in Europe,. International Journal of Medical Informatics, 97, 341-352.
  • Grood, C., Raissi, A., Kuran, Y., & Sanatana, M. J. (2016). Adoption of e-Health Technology By Pysicians: A Scoping Review. Journal of Multidisciplinary Healthcare 9: 335-344. doi:doi:10.2147/JMDH.S103881
  • Heselmans, A., Aertgeerts, B., Donceel, P., Geens, S., Velde, S. V., & Ramaekers, D. (2012). Family Physicians’ Perceptions and Use of Electronic Clinical Decision Support During the First Year of Implementation. Journal of Medical Systems 36: 3677-3684.
  • Holden, R. J., & Karsh, B.-T. (2010). The Technology Acceptance Model: Its past and future in health care. Journal of Biomedical Informatics 43(1): 159-172. doi:https://doi.org/10.1016/j.jbi.2009.07.002
  • Hoque, M. R., Albar, A., & Alam, J. (2016). Factors infuencing physicians' acceptance of e-Health in developing country: An empirical study. International Journal of Healthcare Information Systems and Informatics 11(1): 58 - 70.
  • Hossain, A., Quaresma, R., & Rahman, H. (2019). Investigating factors influencing the physicians’ adoption of electronic health record (EHR) in healthcare system of Bangladesh: An empirical study,. International Journal of Information Management 44:76-87. doi:https://doi.org/10.1016/j.ijinfomgt.2018.09.016
  • Howitt, D., & Cramer, D. (2011). Introduction to SPSS Statistics in Psychology: For Version 19 and Earlier (5. b.). London: Pearson Education Limited.
  • Jalali, M. S., Landman, A., & Gordon, W. J. (2021). Telemedicine, privacy, and information security in the age of COVID-19. Journal of the American Medical Informatics Association 28(3): 671–672. doi:https://doi.org/10.1093/jamia/ocaa310
  • Kayserili, A., & Tefiroglu, E. C. (2023). Evaluation of Digital Healthcare Services by Hospital Administrators. Abant Journal of Health Sciences and Technologies, 3(2), 26-38.
  • Kissi, J., Dai, B., Dogbe, C. S., Banahene, J., & Ernest, O. (2020). Predictive factors of physicians’ satisfaction with telemedicine services acceptance. Health Informatics Journal 3: 1866-1880. doi:doi:10.1177/1460458219892162
  • Lind, D., Marchal, W., & Wathen, S. (2006). Basic Statistics for Business and Economics (5. b.). United States: McGraw-Hill Companies.
  • Liu, L., Cruz, A. M., Rincon, A. R., Buttar, V., Ranson, Q., & Goertzen, D. (2015). What factors determine therapists’ acceptance of new technologies for rehabilitation – a study using the Unified Theory of Acceptance and Use of Technology (UTAUT). Disability and Rehabilitation 37(5): 447-455. doi:https://doi.org/10.3109/09638288.2014.923529
  • McKillup, S. (2012). Statistics Explained: An Introductory Guide for Life Scientists (2. b.). United States: Cambridge University Press.
  • Orhan, S., Gümüş, M., & Kızılkaya, E. (2021). Health Information System in Turkey. International Journal of Social, Humanities and Administrative Sciences, 7(39), 645-653.
  • Penney, E. K., Agyei, J., Boadi, E. K., Abrokwah, E., & Ofori-Boafo, R. (2021). Understanding Factors That Influence Consumer Intention to Use Mobile Money Services: An Application of UTAUT2 With Perceived Risk and Trust. Sage Open 11(3).
  • Sema, F. D., Kebede, A. G., Soworsu, G. Z., Mengistu, T. T., Assen, H. E., Muche, E. A., . . . Seid, A. M. (2024). Perception of Healthcare Professionals towards Electronic-Prescribing at University of Gondar Comprehensive Specialized Hospital, Northwest Ethiopia: A Cross-Sectional Study. BioMed Research International. doi:10.1155/2024/6553470
  • Sergueeva, K., Shaw, N., & Lee, S. H. (2019). Understanding the barriers and factors associated with consumer adoption of wearable technology devices in managing personal health. Canadian Journal of Administrative Sciences 37(1): 45-60.
  • Steininger, K., Stiglbauer, B., Baumgartner, B., & Bernhard Engleder. (2014). Factors Explaining Physicians' Acceptance of Electronic Health Records. 47th Hawaii International Conference on System Sciences. Waikoloa, HI, USA, pp. 2768-2777. doi:doi: 10.1109/HICS
  • Tabachnick, B., & Fidell, L. S. (2013). Using Multivariate Statistics. Boston: Pearson İnternatioanal Edition.
  • Taherdoost, H. (2017). Determining Sample Size; How to Calculate Survey Sample Size . International Journal of Economics and Management Systems 2: 237-239. https://ssrn.com/abstract=3224205
  • The Ministry of Health (2021). Health Statistics Yearbook 2019. Ankara: The Ministry of Health, General Directorate of Health Information Systems. Ministry of Health Publication No: 1185, https://dosyasb.saglik.gov.tr/Eklenti/40564/0/saglik-istatistikleri-yilligi-2019pdf.pdf
  • Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: toward a unified view. MIS Quarterly 27(3): 425–478.
  • Venkatesh, V., Thong, J., & Xu, X. (2012). Consumer Acceptance and Use of Information Tevhnology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Querterly, 36(1): 157-178.
  • Wu, P., Zhang, R., Luan, J., & Zhu, M. (2022). Factors affecting physicians using mobile health applications: an empirical study. BMC Health Services Research 22(24). doi:https://doi.org/10.1186/s12913-021-07339-7
  • Yen, P.-Y., McAlearney, A. S., Sieck, C. J., Hefner, J. L., & Huerta, T. R. (2017). Health Information Technology (HIT) Adaptation: Refocusing on the Journey to Successful HIT Implementation. JMIR Medical Informatics 5(3). https://medinform.jmir.org/2017/3/e28
  • Yu, C.-W., Chao, C.-M., Chang, C.-F., Chen, R.-J., Chen, P.-C., & Liu, a. Y.-X. (2021, Chen-Wei Yu, Cheng-Min Chao https://orcid.org/0000-0003-4). Exploring Behavioral Intention to Use a Mobile Health Education Website: An Extension of the UTAUT 2 Model . Sage Open 11(4). doi:https://doi.org/10.1177/21582440211055721
  • Zhou, T. (2012). Examining mobile banking user adoption from the perspectives of trust and flow experience. Information Technology and Management 13: 27-37. doi:https://doi.org/10.1007/s10799-011-0111-8
There are 39 citations in total.

Details

Primary Language English
Subjects Health Informatics and Information Systems
Journal Section Makaleler
Authors

Mukadder Bektaş 0000-0002-7405-383X

Abdullah Karakaya 0000-0002-3214-6771

Early Pub Date November 22, 2024
Publication Date November 27, 2024
Submission Date October 17, 2024
Acceptance Date November 16, 2024
Published in Issue Year 2024 Volume: 9 Issue: 3

Cite

APA Bektaş, M., & Karakaya, A. (2024). An Examination of Factors Influencing Physicians’ Acceptance and Use of the e-Nabız System. International Journal of Health Management and Tourism, 9(3), 346-367. https://doi.org/10.31201/ijhmt.1568873