ABSTRACT Prior studies have not explored physician’s attitudes toward, and behavior and willingness to accept an e-health care system. However, physicians can induce demand for their services. The development of the high-tech asthma care mobile service (ACMS) in Taiwan provided a means of exploring key factors in a physician’s choice of using an ACMS. The study was based on the technology acceptance model (TAM) and integrated “subjective norm,” “innovativeness,” and “managerial support” to understand and predict physicians’ attitudes and behavioral intentions toward adopting high-tech healthcare systems such as the ACMS. Of 700 questionnaires distributed to physicians with experience using ACMS, 504 completed returns were received (a 72% response rate). The data were analyzed using the structural equation modeling (SEM) method. The results of the study showed that the model selected to explain and predict utilization of the ACMS had high explanatory power and was a good-fit model. The most critical factor that affected behavioral intentions related to ACMS was user attitude, followed by perceived usefulness, managerial support, subjective norm, perceived ease of use, and innovativeness.
Cite this paper
Yan, H. and Wang, M. (2012) What factors affect physicians’ decisions to use an e-health care system?. Health, 4, 1023-1028. doi: 10.4236/health.2012.411156.
 Lin, S. P. and Yang, H. Y. (2009) Exploring key factors in the choice of e-health using an asthma care mobile service model. Telemedicine and e-Health, 15, 884-890.
 Liu, W.T., Huang, C.D., Wang, C.H., Lee, K.Y., Lin, S.M. and Kuo, H.P (2011) A mobile telephone-based interactive self-care system improves asthma control. European Respiratory Journal, 37, 310-317.
 Tulu, B, Burkhard, R.J. and Horan, T. A. (2006) Information systems and health care: A user-driven approach to personal health records. Communications of the Association for Information Systems, 18, 641-656.
 Davis, F.D. (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13, 319-340. doi:10.2307/249008
 Lin, S. P. (2011) Determinants of adoption of mobile healthcare service. International Journal of Mobile Communications, 9, 298-315. doi:10.1504/IJMC.2011.040608
 Bhatti, T. (2007) Exploring factors influencing the adoption of mobile commerce. Journal of Internet Banking and Commerce, 12, 1-13.
 Venkatesh, V. and Davis, F.D. (2000) A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46, 186-204.
 Yang, K.C.C. (2005) Exploring factors affecting the adoption of mobile commerce in Singapore. Telematics and Informatics, 22, 257-277.
 McFarland, D.J. and Hamilton, D. (2006) Adding contextual specificity to the technology acceptance model. Computers in Human Behavior, 22, 427-447.
 Yi, M.Y., Jackson, J.D., Park, J.S. and Probst, J.C. (2006) Understanding information technology acceptance by individual professionals: Toward an integrative view. Information & Management, 43, 350-363.
 Robinson, J.L., Marshall, G.W. and Stamps, M.B. (2005) Sales force use of technology: Antecedents to technology acceptance. Journal of Business Research, 58, 1623-1631.
 Schepers J. and Wetzels, M. (2007) A meta-analysis of the technology acceptance model: Investigating subjective norm and moderation effects. Information & Management, 44, 90-103. doi:10.1016/j.im.2006.10.007
 Hartwick, J. and Barki, H. (1994) Explaining the role of user participation in information system use. Management Science, 40, 440-465. doi:10.1287/mnsc.40.4.440
 Chau, C.P.A.U.L.K. and Hu, P.J.H. (2002) Investigating healthcare professionals’ decisions to accept telemedicine technology: an empirical test of competing theories. Information & Management, 39, 297-311.
 Schaper, L.K. and Pervan, G.P. (2007) ICT and OTs: A model of information and communication technology acceptance and utilisation by occupational therapists. International Journal of Medical Informatics, 76, 212-221.
 Wu, J.H., Wang, S.C. and Lin, L.M. (2007) Mobile computing acceptance factors in the healthcare industry: A structural equation model. International Journal of Medical Informatics, 76, 66-77.
 Hurt, H.T., Joseph, K. and Cook, C.D. (1977) Scales for the measurement of innova-tiveness. Human Communication Research, 4, 58-65.
 Rice, T. H. and Labelle, R. J. (1989) Do physicians induce demand for medical services? Journal of Health Politics, Policy and Law, 14, 587-600.
 Nunnally, J.C. (1978) Psychonometric theory. McGraw-Hill, New York.
 Bagozzi, R.P. and Yi, Y. (1988) On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16, 74-94. doi:10.1007/BF02723327
 Hair, J.F., Black, B., Babin, B., Anderson, R.E. and Tatham, R.L. (2006) Multivariate data analysis. Pearson Education Inc., Upper Saddle River.
 Richard, K. (2007) An empirical examination of patient-physician portal acceptance. European Journal of Information Systems, 16, 751-761.