Journal of Medical Systems

, Volume 36, Issue 3, pp 1965–1977 | Cite as

Barriers to Physicians’ Adoption of Healthcare Information Technology: An Empirical Study on Multiple Hospitals

  • Chihung Lin
  • I-Chun Lin
  • Jinsheng Roan


Prior research on technology usage had largely overlooked the issue of user resistance or barriers to technology acceptance. Prior research on the Electronic Medical Records had largely focused on technical issues but rarely on managerial issues. Such oversight prevented a better understanding of users’ resistance to new technologies and the antecedents of technology rejection. Incorporating the enablers and the inhibitors of technology usage intention, this study explores physicians’ reactions towards the electronic medical record. The main focus is on the barriers, perceived threat and perceived inequity. 115 physicians from 6 hospitals participated in the questionnaire survey. Structural Equation Modeling was employed to verify the measurement scale and research hypotheses. According to the results, perceived threat shows a direct and negative effect on perceived usefulness and behavioral intentions, as well as an indirect effect on behavioral intentions via perceived usefulness. Perceived inequity reveals a direct and positive effect on perceived threat, and it also shows a direct and negative effect on perceived usefulness. Besides, perceived inequity reveals an indirect effect on behavioral intentions via perceived usefulness with perceived threat as the inhibitor. The research finding presents a better insight into physicians’ rejection and the antecedents of such outcome. For the healthcare industry understanding the factors contributing to physicians’ technology acceptance is important as to ensure a smooth implementation of any new technology. The results of this study can also provide change managers reference to a smooth IT introduction into an organization. In addition, our proposed measurement scale can be applied as a diagnostic tool for them to better understand the status quo within their organizations and users’ reactions to technology acceptance. By doing so, barriers to physicians’ acceptance can be identified earlier and more effectively before leading to technology rejection.


Perceived threat Perceived inequity Barriers to technology acceptance Inhibitors Technology rejection 


  1. 1.
    Miller, R. H., and Sim, I., Physicians’ use of electronic medical records: barriers and solutions. Health Aff. 23(2):116–126, 2004. doi: 10.1377/hlthaff.23.2.116.CrossRefGoogle Scholar
  2. 2.
    Chiasson, M. W., and Davidson, E., Pushing the contextual envelope: developing and diffusing IS theory for health information systems research. Inf. Organ. 14(3):155–188, 2004.CrossRefGoogle Scholar
  3. 3.
    Cenfetelli, R. T., & Schwarz, A. (2010). Identifying and testing the inhibitors of technology usage intentions. Inf. Syst. Res., Articles in Advance, 1-19. doi: 10.1287/isre.1100.0295
  4. 4.
    Davis, F. D., Bagozzi, R. P., and Warshaw, P. R., User acceptance of computer technology: a comparison of two theoretical models. Manage. Sci. 35(8):982–1003, 1989.CrossRefGoogle Scholar
  5. 5.
    Venkatesh, V., and Davis, F. D., A theoretical extension of the technology acceptance model: four longitudinal field studies. Manage. Sci. 46(2):186–204, 2000.CrossRefGoogle Scholar
  6. 6.
    Venkatesh, V., Morris, M. G., Davis, G. B., and Davis, F. D., User acceptance of information technology: toward a unified view. MIS Quarterly 27(3):425–478, 2003.Google Scholar
  7. 7.
    Venkatesh, V., and Bala, H., Technology acceptance model 3 and a research agenda on interventions. Decision Sciences 39(2):273–315, 2008.CrossRefGoogle Scholar
  8. 8.
    Cenfetelli, R. T., Inhibitors and enablers as dual factor concepts in technology usage. J. Assoc. Inform. Systems 5(11–12):472–492, 2004.Google Scholar
  9. 9.
    Yarbrough, A. K., and Smith, T. B., Technology acceptance among physicians: a new take on TAM. Med. Care Res. Rev. 64(6):650–672, 2007. doi: 10.1177/1077558707305942.CrossRefGoogle Scholar
  10. 10.
    Bhattacherjee, A., and Hikmet, N., Physicians' resistance toward healthcare information technology: a theoretical model and empirical test. Eur. J. Inf. Syst. 16(6):725–737, 2007.CrossRefGoogle Scholar
  11. 11.
    Lyytinen, K., Expectation failure concept and systems analysts view of information-system failures - results of an exploratory study. Inf. Manage. 14(1):45–56, 1988.CrossRefGoogle Scholar
  12. 12.
    Beaudry, A., and Pinsonneault, A., Understanding user responses to information technology: A coping model of user adaptation. MIS Quarterly 29(3):493–524, 2005.Google Scholar
  13. 13.
    Lapointe, L., and Rivard, S., A multilevel model of resistance to information technology implementation. MIS Quarterly 29(3):461–491, 2005.Google Scholar
  14. 14.
    Bhattacherjee, A., & Hikmet, N. (2008). Enabelers and inhibitors of healthcare information technology adoption: toward a dual-factor model. Americas Conference on Information Systems (AMCIS), Proceedings of the Fourteenth Americas Conference on Information Systems, Toronto, ON, Canada August 14th-17th 2008, 1-8.Google Scholar
  15. 15.
    Ma, Q., and Liu, L., The technology acceptance model: A meta-analysis of empirical findings. Journal of Organizational and End User Computing 16(1):59–72, 2004.CrossRefGoogle Scholar
  16. 16.
    Mathieson, K., Peacock, E., and Chin, W., Extending the technology acceptance model: the influence of perceived user resources. Database for Advances in Information Systems 32(3):86–113, 2001.CrossRefGoogle Scholar
  17. 17.
    Moore, G. C. & Benbasat, I. (1995). Integrating diffusion of innovations and theory of reasoned action models to predict utilization of information technology by end-users. Proceedings of the first IFIP WG 8.6 working conference on the diffusion and adoption of information technology, Oslo, Norway. 132-146.Google Scholar
  18. 18.
    Kaplan, B. (1987). The influence of medical values and practices on medical computer applications, Proceedings, MEDCOMP’82: The First IEEE Computer Society International Conference on Medical Computer Science/Computational Medicine. Silver Spring, MD.: IEEE Computer Society Press; 1982: 83-88. Reprinted in: Anderson, J. G., & Jay, S.J. (Eds.), Use and impact of computers in clinical medicine. Springer, New York, 39-50.Google Scholar
  19. 19.
    Davis, F. D., Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quart 13(3):319–340, 1989.CrossRefGoogle Scholar
  20. 20.
    Chau, P. Y. K., and Hu, P. J. H., Information technology acceptance by individual professionals: a model comparison approach. Decision Sciences 32(4):699–719, 2001.CrossRefGoogle Scholar
  21. 21.
    Chau, P. Y. K., and Hu, P. J. H., Examining a model of information technology acceptance by individual professionals: an exploratory study. J. Manage. Inf. Syst. 18(4):191–229, 2002.Google Scholar
  22. 22.
    Chau, P. Y. K., and Hu, P. J. H., Investigating healthcare professionals’ decisions to accept telemedicine technology: an empirical test of competing theories. Inf. Manage. 39(4):297–311, 2002.CrossRefGoogle Scholar
  23. 23.
    Chismar, W.G., & Wiley-Patton, S. (2003). Does the extended technology acceptance model apply to physicians? Proceedings of the 36th Hawaii International Conference on System Sciences, 160-167. doi: 10.1109/HICSS.2003.1174354
  24. 24.
    Ash, J., Gorman, P., Lavelle, M., Payne, T., Massaro, T., Frantz, G., and Lyman, J., A cross-site qualitative study of physician order entry. J. Am. Med. Inf. Assoc. 10:188–200, 2003. doi: 10.1197/jamia.M770.CrossRefGoogle Scholar
  25. 25.
    Ash, J. S., Gorman, P. N., Seshadri, V., and Hersh, W. R., Computerized physician order entry in U.S. hospitals: results of a 2002 survey. J. Am. Med. Inf. Assoc. 11(2):95–99, 2004. doi: 10.1197/jamia.M1427.CrossRefGoogle Scholar
  26. 26.
    Berger, R., and Kichak, J., Computerized physician order entry: helpful or harmful? J. Am. Med. Inf. Assoc. 11(2):100–103, 2004. doi: 10.1197/ Scholar
  27. 27.
    King, W. R., and He, J., A meta-analysis of the technology acceptance model. Inf. Manage. 43(6):740–755, 2006.CrossRefGoogle Scholar
  28. 28.
    Taylor, S., and Todd, P., Assessing IT usage: the role of prior experience. MIS Quart 19(4):561–570, 1995.CrossRefGoogle Scholar
  29. 29.
    Taylor, S., and Todd, P., Understanding information technology usage: a test of competing models. Inf. Syst. Res. 6(2):144–176, 1995.CrossRefGoogle Scholar
  30. 30.
    Venkatesh, V., Determinants of perceived ease of use: integrating control, intrinsic motivation, and emotion into the technology acceptance model. Inf. Syst. Res. 11(4):342–365, 2000.CrossRefGoogle Scholar
  31. 31.
    Hu, P., Chau, P., Sheng, O., and Tam, K., Examining the technology acceptance model using physician acceptance of telemedicine technology. J. Manage. Inf. Syst. 16(2):91–113, 1999.Google Scholar
  32. 32.
    Lee, Y., Kozar, K. A., and Larsen, K. R. T., The technology acceptance model: past, present and future. Communications of the Association for Information Systems 12:752–780, 2003.Google Scholar
  33. 33.
    Ajzen, I., and Fishbein, M., Understanding attitudes and predicting social behavior. Prentice Hall, Englewood Cliffs, NJ, 1980.Google Scholar
  34. 34.
    Fishbein, M., and Ajzen, I., Belief, attitude, intention and behavior: an introduction to theory and research. Addison-Wesley, Reading, MA, 1975.Google Scholar
  35. 35.
    Keil, M., Beranek, P. M., and Konsynski, B. R., Usefulness and ease of use: field study evidence regarding task considerations. Decision Support Systems 13(1):75–91, 1995. doi: 10.1016/0167-9236(94) Scholar
  36. 36.
    Chau, P. Y. K., and Hu, P. J. H., Investigating healthcare professionals’ decisions to accept telemedicine technology: an empirical test of competing theories. Inf. Manage. 39:297–311, 2002.CrossRefGoogle Scholar
  37. 37.
    Liu, L., and Ma, Q., The impact of service level on the acceptance of application service oriented medical records. Inf. Manage. 42(8):1121–1135, 2005. doi: 10.1016/ Scholar
  38. 38.
    Lee, S. M., Kim, I., Rhee, S., and Trimi, S., The role of exogenous factors in technology acceptance: the case of object-oriented technology. Inf. Manage. 43(4):469–480, 2006. doi: 10.1016/ Scholar
  39. 39.
    Duck, J. D., Managing change: the art of balancing. Harv. Bus. Rev. 71(6):109–118, 1993.Google Scholar
  40. 40.
    King, N., and Anderson, N., Innovation and change in organizations. Routledge, London & New York, 1995.Google Scholar
  41. 41.
    Luecke, R., Harvard Business Essentials:Managing Change and Transition. Harvard Business School Press, Boston, Mass, 2003.Google Scholar
  42. 42.
    Diamond, M. A., Resistance to change: a psychoanalytic critique of Argyris and Schon's contributions to organization theory and intervention. Journal of Management Studies 23(5):543–562, 1986.CrossRefGoogle Scholar
  43. 43.
    Gray, J. L., and Stark, F. A., Organizational behavior concepts and applications (3, rdth edition. Charles E. Merrill Publishing. Co., Columbus, Ohio, 1984.Google Scholar
  44. 44.
    Robbins, S. P., Organizational behavior, 6th edition. Prentice-Hall, NY, 1992.Google Scholar
  45. 45.
    Joshi, K., A model of users' perspective on change: the case of information systems technology implementation. MIS Quart 15(2):229–242, 1991.CrossRefGoogle Scholar
  46. 46.
    Lin, T. C., Sun, P. C., and Hsu, J. C., The determinants of information system resistance behavior: an empirical study based on theory of planned behavior. Journal of e-Business 2(2):1–26, 2000.Google Scholar
  47. 47.
    Boonstra, A., and Broekhuis, M., Barriers to the acceptance of electronic medical records by physicians from systematic review to taxonomy and interventions. BMC Health Serv. Res. 10:231–248, 2010. doi: 10.1186/1472-6963-10-231.CrossRefGoogle Scholar
  48. 48.
    Brown, S. H., and Coney, R. D., Changes in physicians' computer anxiety and attitudes related to clinical information system use. J. Am. Med. Inform. Assoc. 1(5):381–394, 1994.CrossRefGoogle Scholar
  49. 49.
    Judson, A. S., Changing behavior in organizations: minimizing resistance to change, 1st edition. B. Blackwell, Cambridge, Mass., USA, 1991.Google Scholar
  50. 50.
    Lærum, H., Ellingsen, G., and Faxvaag, A., Doctors’ use of electronic medical records sSystems in hospitals: cross sectional survey. Br. Med. J. 323:1344–1348, 2001.CrossRefGoogle Scholar
  51. 51.
    Kemper, A. R., Uren, R. L., and Clark, S. J., Adoption of electronic health records in primary care pediatric practices. Pediatrics 118(1):20–24, 2006.CrossRefGoogle Scholar
  52. 52.
    Randeree, E., Exploring physician adoption of EMRs: a multi-case analysis. Journal of Medical System 31(6):489–496, 2007.CrossRefGoogle Scholar
  53. 53.
    Ludwick, D. A., and Doucette, J., Primary care physicians’ experience with electronic medical records: barriers to implementation in a fee-for-service environment. International Journal of Telemedicine and Applications 2009:1–9, 2009. doi: 10.1155/2009/853524.CrossRefGoogle Scholar
  54. 54.
    Bagozzi, R. P., and Yi, Y., On the evaluation of structural equation models. Journal of the Academy of Marketing Science 16(1):74–94, 1988.CrossRefGoogle Scholar
  55. 55.
    Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., and Tatham, R. L., Multivariate data analysis, 6th edition. Pearson Education, New Jersey, 2006.Google Scholar
  56. 56.
    Fornell, C., and Larcker, D. F., Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research 18(1):39–50, 1981.CrossRefGoogle Scholar
  57. 57.
    Bollen, K. A., Structural equations with latent variables. John Wiley & Sons, New York, 1989.MATHGoogle Scholar
  58. 58.
    Campbell, E. M., Sittig, D. F., Ash, J. S., Guappone, K. P., and Dykstra, R. H., Types of unintended consequences related to computerized provider order entry. J. Am. Med. Inform. Assoc. 13(5):547–556, 2006.CrossRefGoogle Scholar
  59. 59.
    Leavitt, H. J., Managerial psychology (3, rdth edition. University of Chicago Press, Chicago, 1975.Google Scholar
  60. 60.
    Dansky, K. H., Gamm, L. D., Vasey, J. J., and Barsukiewicz, C. K., Electronic medical records: are physicians ready? Journal of Healthcare Management 44(6):440–454, 1999.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Department of Information ManagementNational Chung Cheng UniversityChiayi CountyR.O.C.
  2. 2.Department of Computer Science and information ManagementHung Kuang UniversityTaichungTaiwanR.O.C.

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