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Journal of Medical Systems
Published in: Journal of Medical Systems 3/2012

01-06-2012 | ORIGINAL PAPER

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

Authors: Chihung Lin, I-Chun Lin, Jinsheng Roan

Published in: Journal of Medical Systems | Issue 3/2012

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Abstract

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.
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Literature
1.
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.CrossRef
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.
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.CrossRef
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.CrossRef
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.
7.
Venkatesh, V., and Bala, H., Technology acceptance model 3 and a research agenda on interventions. Decision Sciences 39(2):273–315, 2008.CrossRef
8.
Cenfetelli, R. T., Inhibitors and enablers as dual factor concepts in technology usage. J. Assoc. Inform. Systems 5(11–12):472–492, 2004.
9.
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.CrossRef
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.CrossRef
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.
13.
Lapointe, L., and Rivard, S., A multilevel model of resistance to information technology implementation. MIS Quarterly 29(3):461–491, 2005.
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.
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.CrossRef
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.CrossRef
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.
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.
19.
Davis, F. D., Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quart 13(3):319–340, 1989.CrossRef
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.CrossRef
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.
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.CrossRef
23.
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.CrossRef
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.CrossRef
26.
27.
King, W. R., and He, J., A meta-analysis of the technology acceptance model. Inf. Manage. 43(6):740–755, 2006.CrossRef
28.
Taylor, S., and Todd, P., Assessing IT usage: the role of prior experience. MIS Quart 19(4):561–570, 1995.CrossRef
29.
Taylor, S., and Todd, P., Understanding information technology usage: a test of competing models. Inf. Syst. Res. 6(2):144–176, 1995.CrossRef
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.CrossRef
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.
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.
33.
Ajzen, I., and Fishbein, M., Understanding attitudes and predicting social behavior. Prentice Hall, Englewood Cliffs, NJ, 1980.
34.
Fishbein, M., and Ajzen, I., Belief, attitude, intention and behavior: an introduction to theory and research. Addison-Wesley, Reading, MA, 1975.
35.
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.CrossRef
37.
38.
39.
Duck, J. D., Managing change: the art of balancing. Harv. Bus. Rev. 71(6):109–118, 1993.
40.
King, N., and Anderson, N., Innovation and change in organizations. Routledge, London & New York, 1995.
41.
Luecke, R., Harvard Business Essentials:Managing Change and Transition. Harvard Business School Press, Boston, Mass, 2003.
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.CrossRef
43.
Gray, J. L., and Stark, F. A., Organizational behavior concepts and applications (3, rdth edition. Charles E. Merrill Publishing. Co., Columbus, Ohio, 1984.
44.
Robbins, S. P., Organizational behavior, 6th edition. Prentice-Hall, NY, 1992.
45.
Joshi, K., A model of users' perspective on change: the case of information systems technology implementation. MIS Quart 15(2):229–242, 1991.CrossRef
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.
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.CrossRef
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.CrossRef
49.
Judson, A. S., Changing behavior in organizations: minimizing resistance to change, 1st edition. B. Blackwell, Cambridge, Mass., USA, 1991.
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.CrossRef
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.CrossRef
52.
Randeree, E., Exploring physician adoption of EMRs: a multi-case analysis. Journal of Medical System 31(6):489–496, 2007.CrossRef
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.CrossRef
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.CrossRef
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.
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.CrossRef
57.
Bollen, K. A., Structural equations with latent variables. John Wiley & Sons, New York, 1989.MATH
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.CrossRef
59.
Leavitt, H. J., Managerial psychology (3, rdth edition. University of Chicago Press, Chicago, 1975.
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.
Metadata
Title
Barriers to Physicians’ Adoption of Healthcare Information Technology: An Empirical Study on Multiple Hospitals
Authors
Chihung Lin
I-Chun Lin
Jinsheng Roan
Publication date
01-06-2012
Publisher
Springer US
Published in
Journal of Medical Systems / Issue 3/2012
Print ISSN: 0148-5598
Electronic ISSN: 1573-689X
DOI
https://doi.org/10.1007/s10916-011-9656-7

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