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Determinants of Telemedicine Acceptance in Selected Public Hospitals in Malaysia: Clinical Perspective

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Abstract

The purpose of this study is to explore the determinants of telemedicine acceptance in selected public hospitals in Malaysia and to investigate the effect of health culture on the relationship between these determinants and telemedicine acceptance. Data were gathered by means of a survey of physicians and nurses as the main group of users of telemedicine technology from hospitals that are currently using telemedicine technology. The results indicated that government policies, top management support, perception of usefulness and computer self-efficiency have a positive and significant impact on telemedicine acceptance by public hospitals in Malaysia. The results also confirmed the moderating role of health culture on the relationship between government policies as well as perceived usefulness on telemedicine acceptance by Malaysian hospitals. The results are useful for decision-makers as well as managers to recognize the potential role of telemedicine and assist in the process of implementation, adoption and utilization, and, therefore, spread the usage of telemedicine technology in more hospitals in the country.

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References

  1. ITU., Impact of telecommunications in health care and other social services. [Report], 1998.

  2. Sicotte, C., Denis, J. L., Lehoux, P., and Champagne, F., The computer-based patient record challenges towards timeless and spaceless medical practice. Journal of Medical Systems 22: 237–256, 1998.

    Article  Google Scholar 

  3. Menachemi, N., Burke, D. E., and Ayers, D. J., Factors Affecting the Adoption of Telemedicine—A Multiple Adopter Perspective. Journal of Medical Systems 28(6): 616-632, 2004.

    Article  Google Scholar 

  4. Reid, J. A., Telemedicine Primer: Understanding the Issue. Billings, Montana: Artcraft, 1996.

    Google Scholar 

  5. Nessa, A., Ameen, M. A., Ullah, S., and Sup Kwak, K., Applicability of Telemedicine in Bangladesh : Current Status and Future Prospects. Third 2008 International Conference on Convergence and Hybrid Information Technology, 2008.

  6. Bashshur, R. L., Telemedicine and the health care system. In: Bashshur, R. L., Sanders, J., & Shannon, G. W. (eds). Telemedicine: Theory and Practice. Springfield, IL: Charles Thomas, 265-290, 1997.

    Google Scholar 

  7. Berg, M., Patient care information systems and health care work: a sociotechnical approach. International Journal of Medical Informatics 55: 87-101, 1999.

    Article  Google Scholar 

  8. Broens, T. H., Huis in't Veld, R. M., Vollenbroek-Hutten, M. M., Hermens, H. J., Van Halteren, A. T., and Nieuwenhuis, L. J., Determinants of successful telemedicine implementations: a literature study. Journal of Telemedicine and Telecare 13: 303-9, 2007.

  9. Obstfelder, A., Engeseth, K. H., and Wynn, R., Characteristics of successfully implemented telemedical applications. Implementation Science 2: 309-326, 2007.

    Article  Google Scholar 

  10. Yellowlees, P., Successful development of telemedicine systems-seven core principles. Journal of Telemedicine and Telecare 3: 215-222, 1997.

    Article  Google Scholar 

  11. Keen, P., Shaping the future: Business design through information technology. Boston, MA: Havard Business School Press, 1991.

    Google Scholar 

  12. Giuse, D. A., and Kuhn, K. A., Health information systems challenges: the Heidelberg conference and the future. International Journal of Medical Informatics 69(2-3): 105-114, 2003.

    Article  Google Scholar 

  13. Boonchai, K., Supasit, P., and Stuart, M. S., 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, 2009.

    Article  Google Scholar 

  14. Maarop, N., and Win, K. T., The interplay of environmental factors in the acceptance of teleconsultation technology: A mixed methods study. Open International Journal of Informatics 1: 46-58, 2012.

    Google Scholar 

  15. Yi, M. Y., Jackson, J. D., Park, J. S., and Probst, J. C., Understanding information technology acceptance by individual professionals: Toward an integrative view. Information & Management 43(3): 350-363, 2006.

    Article  Google Scholar 

  16. Hu, P. J. H., Chau, P. Y. K., and Sheng, O. R. L., Adoption of Telemedicine Technology by Health Care Organizations: An Exploratory study. Journal of Organizational Computing and Electronic Commerce 12(3): 197-221, 2002.

    Article  Google Scholar 

  17. Maarop, N., and Win, K. T., 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. Journal of Medical Systems 36: 2881–2892, 2012.

    Article  Google Scholar 

  18. Hung, M. C., and Jen, W. Y., The adoption of mobile health management services: An empirical study. Journal of Medical Systems 36(3): 1381-1388, 2012.

    Article  Google Scholar 

  19. Hu, P. J. H., Chau, P. Y. K., Sheng, O. R. L., and Tom, K. Y., Examining the technology acceptance model using physician acceptance of telemedicine technology,. Journal of Management Information system 16(2): 91-112, 1999.

    Google Scholar 

  20. Mitchell, B. R., Mitchell, J. G., & Disney, A. P., User adoption issues in renal telemedicine. Journal of Telemedicine and Telecare 2(2): 81-86, 1996.

    Article  Google Scholar 

  21. Maarop, N., Win, K. T., Masrom, M., and Hazara Singh, S. S, Exploring Factors That Affect Teleconsultation Adoption: In The Case Of Malaysia. PACIS 2011 Proceedings. Paper 124, 2011.

  22. Straub, D., Loch, K., and Hill, C., Transfer of Information Technology to Developing Countries: A Test of Cultural Influence Modeling in the Arab World,. Journal of Global Information Management 9(4): 6-28, 2001.

    Article  Google Scholar 

  23. Kifle, M., Mbarika, V., and Tan, J., Telemedicine transfer in Sub-Saharan Africa:Investigating infrastructure and culture. Proceedings of the 9th International Conference on Social Implications of Computers in Developing Countries, May 2007, Sao Paulo, Brazil, 2007.

  24. Meso, P., Musa, P., and Mbarika, V., Towards a Model of Consumer Use of Mobil Information and Communication Technology in LDCs: The Case of Sub-Saharan Africa. Information system Journal 15(2): 119-146, 2005.

    Article  Google Scholar 

  25. Datta, P., and Mbarika, V. W. A., Are we there yet?: an exploratory relationship between national information infrastructure expenditures, infrastructure development, and service-sector productivity. Proceedings of the 37 th Hawaii International Conference on System Sciences, 2004.

  26. Li, Y. C., Chang, I. C., Hung, W. F., and Fu, H. K., The Critical Factors Affecting Hospital Adoption of Mobile Nursing Technologies in Taiwan. Proceedings of the 38 th Hawaii International Conference on System Sciences, 2005.

  27. Hsiao, S. J., Li, Y. C., Chen, Y. L., and Ko, H. C., Critical factors for the adoption of mobile nursing information systems in Taiwan: the nursing department administrators’ perspective. Journal of medical systems 33(5): 369-377, 2009.

    Article  Google Scholar 

  28. Shaqrah, A. A., Adoption of Telemedicine among Health Care Services:The Strategic Adoption. Journal of e-Health Management http://www.ibimapublishing.com/journals/JEHM/jehm.html Vol. 2010 (2010), Article ID 523631, 19 pages, 2010.

  29. Judi, H. M., Razak, A. A., Sha'ari, N., and Mohamed, H., Feasibility and Critical Success Factors in Implementing Telemedicine,. Information Technology Journal 8(3): 326-332, 2009.

    Article  Google Scholar 

  30. Buchauer, A., Pohl, U., Kurzel, N., and Haux, R., Mobilizing a health professional's workstation—results of an evaluation study. International journal of medical informatics 54(2): 105-114, 1999.

    Article  Google Scholar 

  31. Liu, C. F., Key Factors Influencing the Intention of Telecare Adoption: An Institutional Perspective. Telemedicine and e-Health 17(4), 288-293, 2011.

    Article  Google Scholar 

  32. Moore, S. K., Extending healthcare's reach. Spectrum, IEEE 39(1): 66-71, 2002.

    Article  Google Scholar 

  33. Chintakovid, T., Factors Affecting End Users? Intrinsic Motivation to Use Software, 2007.

  34. Davis, F. D., Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340, 1989.

  35. Venkatesh, V., Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information systems research 11(4): 342-365, 2000.

    Article  Google Scholar 

  36. Chau, P. Y. K., and Hu, P. J. H., Investigating healthcare professionals' decisions to accept telemedicine technology: an empirical test of competing theories. Information & Management 39(4): 297-311, 2002.

    Article  Google Scholar 

  37. Taylor, S., and Todd, P. A., Understanding information technology usage: A test of competing models. Information systems research 6(2): 144-176, 1995.

    Article  Google Scholar 

  38. Croteau, A. M., and Vieru, D., Telemedicine adoption by different groups of physicians. Proceedings of the 35 th Hawaii International Conference on System Sciences, 2002.

  39. Kifle, M., Payton, F. C., Mbarika, V., and Meso, P. Transfer and Adoption of Advanced Information Technology Solutions in Resource-Poor Environments: The Case of Telemedicine Systems Adoption in Ethiopia. Telemedicine and e-Health 16(3): 327-343, 2010.

    Article  Google Scholar 

  40. Harrison, D. A., Mykytyn, P. P., and Rienenschneider, C. K., Executive decisions about IT adoption in small business: theory and empirical tests. Information Systems Research 8(2): 171–195, 1997.

    Article  Google Scholar 

  41. Lakhanpal, B., Assessing the factors related to microcomputer usage by middle managers. International Journal of Information Management 14: 39–50, 1994.

    Article  Google Scholar 

  42. Rothwell, R., The characteristics of successful innovators and technically progressive firms. R&D Management 7(3): 191-206, 1997.

    Article  Google Scholar 

  43. Kraemer, K. L., and King, J. L., Computing policies and problems: a stage theory approach. Telecommunications Policy, 5(3), 198–215, 1981.

    Article  Google Scholar 

  44. Moreton, R., Transforming the organization: the contribution of the information systems function. Journal of Strategic Information Systems 4(2): 149–163, 1995.

    Article  Google Scholar 

  45. Rogers, E. M., Diffusion of Innovations, third ed., The Free Press, New York, 1983.

  46. Montealegre, R., A temporal model of institutional interventions for information technology adoption in less developed countries. Journal of Management Information Systems 16(1): 207–232, 1999.

    Google Scholar 

  47. Munene, J. C.,The institutional environment and managerial innovations: a qualitative study of selected Nigerian firms. Journal of Occupational and Organizational Psychology 68: 291–300, 1995.

    Article  Google Scholar 

  48. Orlikowski, W. J., CASE as organizational change. MIS Quarterly 17(3): 309–340, 1993.

    Article  Google Scholar 

  49. Orlikowski, W. J., and Robey, D., Information technology and the structuring of organizations. Information Systems Research 2(2): 143–169, 1991.

    Article  Google Scholar 

  50. Jarvenpaa, L., and Leidner, D. E., An information company in Mexico: extending the resource-based view of the firm to a developing country context. Information Systems Research 9(4): 342–361, 1998.

    Article  Google Scholar 

  51. Kuan, K. K. Y., and Chau, P. Y. K., A perception-based model for EDI adoption in small businesses using a technology–organization–environment framework. Information & Management 38: 507–521, 2001.

    Article  Google Scholar 

  52. Mehrtens, J., Cragg, P. B., and Mills, A. M., A model of Internet adoption by SMEs. Information & Management 39: 165–176, 2001.

    Article  Google Scholar 

  53. Burton-Jones, A., and Hubona, G. S., The mediation of external variables in the technology acceptance model. Information & Management 43(6): 706-717, 2006.

    Article  Google Scholar 

  54. Goldfarb, A., and Prince, J. T., Internet Adoption and Usage Patterns are Different: Implications for the Digital Divide. Information Economics and Policy 20(1): 2-15, 2008.

    Article  Google Scholar 

  55. Aas, I. H. M., Working with telemedicine: user characteristics and attitudes. Journal of telemedicine and telecare 6(1): 66-68, 2000.

    Article  Google Scholar 

  56. Yu, P., Li, H., and Gagnon, M. P., Health IT acceptance factors in long-term care facilities: A cross-sectional survey. International journal of medical informatics 78(4): 219-229, 2009.

    Article  Google Scholar 

  57. WHO., Telemedicine: opportunities and developments in Member States: report on the second global survey on eHealth (Global Observatory for eHealth Series, 2), 2009.

  58. Hailey, D., and Crowe, B., A profile of success and failure in telehealth—evidence and opinion from the Successes and Failures in Telehealth conferences. Journal of telemedicine and telecare 9(2): 22-24, 2003.

    Article  Google Scholar 

  59. Lu, Y. C., Xiao, Y., Sears, A., and Jacko, J. A., A review and a framework of handheld computer adoption in healthcare,. International Journal of Medical Informatics, 74: 409-422, 2005.

    Article  Google Scholar 

  60. Lewis, W., Agarwal, R., and Sambamurthy, V., Sources of influence on beliefs about information technology use: An empirical study of knowledge workers. MIS quarterly 27(4): 657-678, 2003.

    Google Scholar 

  61. Gagnon, M. P., Desmartis, M., Labrecque, M., Car, J., Pagliari, C., Pluye, P. et al., Systematic Review of Factors Influencing the Adoption of Information and Communication Technologies by Healthcare Professionals. Journal of Medical Systems 36: 241-277, 2012.

    Article  Google Scholar 

  62. Lee, H. W., Ramayah, T., and Zakaria, N., External Factors in Hospital Information System (HIS) Adoption Model: A Case on Malaysia. Journal of medical systems 36(4): 2129-40, 2011.

    Article  Google Scholar 

  63. Mezni, H., and Zeribi-Benslimane, O., Determinants of the individual acceptance of the telemedicine. 3 rd international Conference on Information and Communication Technologies: From Theory to Applications , 1-4, 2008.

  64. Venkatesh, V., and Davis, F. D., A model of the antecedents of perceived ease of use: Development and test. Decision Sciences 27(3): 451-481, 1996.

    Article  Google Scholar 

  65. Mathieson, K., Predicting user intention: Comparing the technology acceptance model with theory of planned behavior. Information Systems Research 2(3): 173-191, 1991.

    Article  Google Scholar 

  66. Aggelidis, V. P. and Chatzoglou, P. D., Using a modified technology acceptance model in hospitals. International Journal of Medical Informatics 78(2): 115-126, 2009.

    Article  Google Scholar 

  67. Compeau, D. R., and Higgins, C. A., Computer self-efficacy: Development of a measure and initial test. MIS quarterly June: 189-211, 1995.

  68. Blake, R. R., & Mouton, J. S., The Managerial Grid III. Houston: Gulf Publishing, 1985.

    Google Scholar 

  69. Egan, T. M., Yang, B., and Bartlett, K. R., The effects of organizational learning culture and job satisfaction on motivation to transfer learning and turnover intention. Human Resource Development Quarterly 15(3): 279-301, 2004.

    Article  Google Scholar 

  70. Hoffman, N., and Klepper, R., Assimilating New Technologies: The Roke of Organizaional Culture. Information Management System 7(13): 1-7, 2000.

    Google Scholar 

  71. Tuggle, F. D., and Shaw, N. C., The effect of Organizational Culture on the Implementation of Knowledge Management. Florida Artificial Intelligence Research Symposium (FLARS), Orlando, FI, 2000.

    Google Scholar 

  72. Bangert, D., and Doctor, R., Implementing Store-and-Forward Telemedicine: Organizational Issues. Telemedicine Journal and e-Health 6(3): 355- 360, 2000.

    Article  Google Scholar 

  73. Petter, S., Straub, D. and Rai, A., Specifying Formative Constructs in Information Systems Research. MIS Quarterly 31: 623-656, 2007.

    Google Scholar 

  74. Whitten, P., Sypher, B. D., and Patterson, J. D., Transcending the technology of telemedicine: an analysis of telemedicine in North Carolina. Health Communication 12(2): 109-135, 2000.

    Article  Google Scholar 

  75. Patel, R. N., and Antonarakis, G. S. (2013). Factors influencing the adoption and implementation of teledentistry in the UK, with a focus on orthodontics. Community Dentistry and Oral Epidemiology 41(5): 424-431.

    Google Scholar 

  76. Ringle, C. M., Wende, S., and Will, A., “SmartPLS 2.0”, Retrieved December, 5, 2012. Access on: http://www.smartpls.de, 2005.

  77. Efron, B., and Tibshirani, R. J., An Introduction to the Bootstrap. New York: Chapman & Hall, 1993.

    Book  MATH  Google Scholar 

  78. Wetzels, M., Odekerken-Schroder, G., and van Oppen, C., Using PLS Path Modeling for Assessing Hierarchical Construct Models: Guidelines and Empirical Illustration. MIS Quarterly 33(1): 177-195, 2009.

    Google Scholar 

  79. Hair, J. F., Hult, G. T. M., Ringle, C. M., and Sarstedt, M. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). Thousand Oaks: Sage, 2013.

    Google Scholar 

  80. Barclay, D., Higgins, C. and Thompson, R., The partial least squares (PLS) approach to causal modelling: Personal computer adoption and use as an illustration. Technology Studies 2(2): 285–309, 1995.

    Google Scholar 

  81. Chin, W. W., The partial least squares approach to structural equation modelling. In G. A. Marcoulides (Ed.), Modern methods for business research (pp. 295–336). Mahwah, NJ: Lawrence Erlbaum Associates, Inc, 1998.

    Google Scholar 

  82. Podsakoff, P. M., and Organ, D. W., Self-reports in organizational research: Problems and prospects. Journal of Management 12(4): 531–544, 1986.

    Article  Google Scholar 

  83. Igbaria, M., Gumaraes, T., and Gordon, B. D., Testing the determinants of microcomputer usage via a Structural Equation Model. Journal of Management Information Systems 11(4): 87-114, 1995.

    Google Scholar 

  84. Henseler, J., Ringle, C. M., and Sinkovics, R. R., The use of partial least squares path modeling in international marketing. Advances in International Marketing 20, 277–320, 2009.

    Google Scholar 

  85. Straub, D., Boudreau, M.-C., and Gefen, D., Validation guidelines for IS positivist research. Communications of Association for Information Systems 13(24): 380–427, 2004.

    Google Scholar 

  86. 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.

    Article  Google Scholar 

  87. Gefen, D., and Straub, D. W., A Practical Guide to Factorial Validity Using PLS-Graph: Tutorial and Annotated Example. Communications of the Association for Information Systems 16(5): 91-109, 2005.

    Google Scholar 

  88. Stone, M., Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society 36(2): 111–147, 1974.

    MATH  Google Scholar 

  89. Geisser, S., The predictive sample reuse method with applications. Journal of the American Statistical Association 70(350): 320–328, 1975.

    Article  MATH  Google Scholar 

  90. Chin, W. W., How to write up and report PLS analyses. In V. E. Vinzi, W. W. Chin, J. Henseler, & H. Wang (Eds.), Handbook of partial least squares: Concepts, methods and applications in marketing and related fields (pp. 655–690). Berlin: Springer, 2010.

    Chapter  Google Scholar 

  91. Al-Qirim, N., Championing telemedicine adoption and utilization in healthcare organizations in New Zealand. International journal of medical informatics 76(1): 42-54, 2007.

    Article  Google Scholar 

  92. Marakas, G., Yi, M., and Johnson, R., The multilevel and multifaceted character of computer self-efficacy: Toward clarification of the construct and an integrative framework for research. Information Systems Research 9(2): 126-163, 1998.

    Article  Google Scholar 

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Authors and Affiliations

  1. University of Malaya, Kuala Lumpur, Malaysia

    Suhaiza Zailani

  2. Universiti Sains Malaysia, Penang, Malaysia

    Mina Sayyah Gilani, Davoud Nikbin & Mohammad Iranmanesh

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  1. Suhaiza Zailani
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  2. Mina Sayyah Gilani
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Correspondence to Suhaiza Zailani.

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Zailani, S., Gilani, M.S., Nikbin, D. et al. Determinants of Telemedicine Acceptance in Selected Public Hospitals in Malaysia: Clinical Perspective. J Med Syst 38, 111 (2014). https://doi.org/10.1007/s10916-014-0111-4

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