Journal of Medical Systems

, 39:172 | Cite as

Determinants of RFID Adoption in Malaysia’s Healthcare Industry: Occupational Level as a Moderator

  • Suhaiza Zailani
  • Mohammad Iranmanesh
  • Davoud Nikbin
  • Jameson Khoo Cheong Beng
Education & Training
Part of the following topical collections:
  1. Education & Training


With today’s highly competitive market in the healthcare industry, Radio Frequency Identification (RFID) is a technology that can be applied by hospitals to improve operational efficiency and to gain a competitive advantage over their competitors. The purpose of this study is to investigate the factors that may effect RFID adoption in Malaysia’s healthcare industry. In addition, the moderating role of occupational level was tested. Data was collected from 223 managers as well as healthcare and supporting staffs. This data was analyzed using the partial least squares technique. The results show that perceived ease of use and usefulness, government policy, top management support, and security and privacy concerns have an effect on the intent to adopt RFID in hospitals. There is a wide gap between managers and healthcare staff in terms of the factors that influence RFID adoption. The results of this study will help decision makers as well as managers in the healthcare industry to better understand the determinants of RFID adoption. Additionally, it will assist in the process of RFID adoption, and therefore, spread the usage of RFID technology in more hospitals.


RFID Healthcare Adoption Malaysia 


  1. 1.
    Berman, A., Reducing medication errors through naming, labeling, and packaging. J. Med. Syst. 28(1):9–29, 2004.PubMedCrossRefGoogle Scholar
  2. 2.
    Crawford, S. Y., Cohen, M. R., and Tafesse, E., Systems factors in the reporting of serious medication errors in hospitals. J. Med. Syst. 27(6):543–551, 2003.PubMedCrossRefGoogle Scholar
  3. 3.
    Walton, S. M., The pharmacist shortage and medication errors: Issues and evidence. J. Med. Syst. 28(1):63–69, 2004.PubMedCrossRefGoogle Scholar
  4. 4.
    Chen, P. J., Chen, Y. F., Chai, S. K., and Huang, Y. F., Implementation of an RFID-based management system for operation room. Machine Learning and Cybernetics, 2933–2938, 2009.Google Scholar
  5. 5.
    Lai, C. L., Chien, S. W., Chang, L. H., Chen, S. C., and Fang, K., Enhancing medication safety and healthcare for inpatients using RFID. Portland International Center for Management of Engineering Technology 2007, Proceedings on 7th PICMET 2007, 2783–2790, 2007.Google Scholar
  6. 6.
    Lim, S. H., and Koh, C. E., RFID implementation strategy: Perceived risks and organizational fits. Ind. Manag. Data Syst. 109(8):1017–1036, 2009.CrossRefGoogle Scholar
  7. 7.
    Loebbecke, C., and Palmer, J., RFID in the fashion industry: Kaufhof department stores AG and Gerry Weber International AG, fashion manufacturer. MIS Q. Exec. 5(2):15–25, 2006.Google Scholar
  8. 8.
    Castro, L., Lefebvre, E., and Lefebvre, L. A., Adding intelligence to mobile asset management in hospitals: The true value of RFID. J. Med. Syst. 37(5):1–17, 2013.CrossRefGoogle Scholar
  9. 9.
    Vanany, I., and Shaharoun, A. B. M., Barriers and critical success factors towards RFID technology adoption in South-East Asian Healthcare Industry, Proceedings of the 9th Asia Pacific Industrial Engineering & Management System Conference 148–154, 2008.Google Scholar
  10. 10.
    Wickboldt, A. K., and Piramuthu, S., Patient safety through RFID: Vulnerabilities in recently proposed grouping protocols. J. Med. Syst. 36(2):431–435, 2012.PubMedCrossRefGoogle Scholar
  11. 11.
    Chee, H. L., and Barraclough, S., Health care in Malaysia: the dynamics of provision, financing and access. Taylor & Francis, Routledge, 2007.Google Scholar
  12. 12.
    Chang, I., Factors affecting the adoption of electronic signature: Executives’ perspective of hospital information department. Decis. Support. Syst. 44(1):350–359, 2007.CrossRefGoogle Scholar
  13. 13.
    Tsai, M. C., Lee, W., and Wu, H. C., Determinants of RFID adoption intention: Evidence from Taiwanese retail chains. Inf. Manag. 47(5):255–261, 2010.CrossRefGoogle Scholar
  14. 14.
    Ramanathan, R., Ramanathan, U., and Ko, L. W. L., Adoption of RFID technologies in UK logistics: Moderating roles of size, barcode experience and government support. Expert Syst. Appl. 41(1):230–236, 2014.CrossRefGoogle Scholar
  15. 15.
    Wang, Y. M., Wang, Y. S., and Yang, Y. F., Understanding the determinants of RFID adoption in the manufacturing industry. Technol Forecast Soc Chang 77(5):803–815, 2010.CrossRefGoogle Scholar
  16. 16.
    Perez, M. M., Cabrero-Canosa, M., Hermida, J. V., Garcia, L. C., Gomez, D. L., Vazquez, G. G., and Herranz, I. M., Application of RFID technology in patient tracking and medication traceability in emergency care. J. Med. Syst. 36(6):3983–3993, 2012.CrossRefGoogle Scholar
  17. 17.
    Shim, H., Uh, Y., Lee, S. H., and Yoon, Y. R., A new specimen management system using RFID technology. J. Med. Syst. 35(6):1403–1412, 2011.PubMedCrossRefGoogle Scholar
  18. 18.
    Hawrylak, P. J., Schimke, N., Hale, J., and Papa, M., Security risks associated with radio frequency identification in medical environments. J. Med. Syst. 36(6):3491–3505, 2012.PubMedCrossRefGoogle Scholar
  19. 19.
    Rosenbaum, B. P., Radio frequency identification (RFID) in health care: Privacy and security concerns limiting adoption. J. Med. Syst. 38(3):1–6, 2014.CrossRefGoogle Scholar
  20. 20.
    Lai, H. M., Lin, I. C., and Tseng, L. T., High-level managers’ considerations for RFID adoption in hospitals: An empirical study in Taiwan. J. Med. Syst. 38(2):1–17, 2014.CrossRefGoogle Scholar
  21. 21.
    Chong, Y. L. C., and Chan, F. T. S., Structural equation modeling for multi-stage analysis on radio frequency identification (RFID) diffusion in the health care industry. Expert Syst. Appl. 39(10):8645–8654, 2012.CrossRefGoogle Scholar
  22. 22.
    Yao, W., Chu, C. H., and Li, Z., The adoption and implementation of RFID technologies in healthcare: A literature review. J. Med. Syst. 36(6):3507–3525, 2012.PubMedCrossRefGoogle Scholar
  23. 23.
    Yu, Y. C., Hou, T. W., and Chiang, T. C., Low cost RFID real lightweight binding proof protocol for medication errors and patient safety. J. Med. Syst. 36(2):823–828, 2012.PubMedCrossRefGoogle Scholar
  24. 24.
    Healthcare Purchasing News, RFID improves medical device management in surgery unit, Retrieved February, 2014. Access on:, 2006.
  25. 25.
    Wang, S. W., Chen, W. H., Ong, C. S., Liu, L., and Chuang, Y. W., RFID application in hospitals: A case study on a demonstration RFID project in a Taiwan hospital. In: Proceedings of the 39th Annual Hawaii International Conference on System Sciences (HICSS’06), 2006.Google Scholar
  26. 26.
    Vinjumur, J. K., Becker, E., Ferdous, S., Galatas, G., and Makedon, F., Web based medicine intake tracking application, Proceedings of the 3rd International Conference on Pervasive Technologies Related To Assistive Environments, 2010.Google Scholar
  27. 27.
    Crooker, K., Baldwin, D., and Chalasani, S., RFID technology as sustaining or disruptive innovation: Applications in the healthcare industry. Eur. J. Sci. Res. 37(1):160–178, 2009.Google Scholar
  28. 28.
    Tzeng, S. F., Chen, W. H., and Pai, F. Y., Evaluating the business value of RFID: Evidence from five case studies. Int. J. Prod. Econ. 112(2):601–613, 2008.CrossRefGoogle Scholar
  29. 29.
    Davis, F. D., Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q., 319–340, 1989.Google Scholar
  30. 30.
    Lee, H. W., Ramayah, T., and Zakaria, N., External factors in hospital information system (HIS) adoption model: A case on Malaysia. J. Med. Syst. 36(4):2129–2140, 2011.PubMedCrossRefGoogle Scholar
  31. 31.
    Hung, M. C., and Jen, W. Y., The adoption of mobile health management services: An empirical study. J. Med. Syst. 36(3):1381–1388, 2012.PubMedCrossRefGoogle Scholar
  32. 32.
    Saade, R., and Bahli, B., The impact of cognitive absorption on perceived usefulness and perceived ease of use in on-line learning: an extension of the technology acceptance model. Inf. Manag. 42(2):317–327, 2005.CrossRefGoogle Scholar
  33. 33.
    Wu, J. H., and Wang, S. C., What drives mobile commerce? An empirical evaluation of the revised technology acceptance model. Inf. Manag. 42(5):719–729, 2005.CrossRefGoogle Scholar
  34. 34.
    Kulviwat, S., Buner, G. C., and Kumar, A., Toward a unifies theory of consumer acceptance technology. Psychol. Mark. 24(12):1059–1084, 2007.CrossRefGoogle Scholar
  35. 35.
    Davis, F. D., Bagozzi, R. P., and Warshaw, P. R., User acceptance of computer technology: A comparison of two theoretical models. Manag. Sci. 35(8):982–1004, 1989.CrossRefGoogle Scholar
  36. 36.
    Yen, D. C., Wu, C. S., Cheng, F. F., and Huang, Y. W., Determinants of users’ intention to adopt wireless technology: An empirical study by integrating TTF with TAM. Comput. Hum. Behav. 26(5):906–915, 2010.CrossRefGoogle Scholar
  37. 37.
    Hu, P. J. H., Chau, P. Y. K., and Sheng, O. R. L., Adoption of telemedicine technology by health care organizations: An exploratory study. J. Organ. Comput. Electron. Commer. 12(3):197–221, 2002.Google Scholar
  38. 38.
    Zailani, S., Gilani, M. S., Nikbin, D., and Iranmanesh, M., Determinants of telemedicine acceptance in selected public hospitals in Malaysia: Clinical perspective. J. Med. Syst. 38(9):111–123, 2014.PubMedCrossRefGoogle Scholar
  39. 39.
    Rogers, E. M., Diffusion of preventive innovations. Addict. Behav. 27(6):989–993, 2002.PubMedCrossRefGoogle Scholar
  40. 40.
    Riquelme, H., and Rios, R. E., The moderating effect of gender in the adoption of mobile banking. Int. J. Bank Mark. 28(5):328–341, 2010.CrossRefGoogle Scholar
  41. 41.
    Rogers, E. M., Diffusion of innovations, 4th edition. Free Press, New York, 1995.Google Scholar
  42. 42.
    Lu, Y., Yang, S., Chau, P. Y. K., and Cao, Y., Dynamics between the trust transfer process and intention to use mobile payment services: A cross-environment perspective. Inf. Manag. 48(8):393–403, 2011.CrossRefGoogle Scholar
  43. 43.
    Chang, I. C., Hwang, H. G., Yen, D. C., and Lian, J. W., Critical factors for adopting PACS in Taiwan: Views of radiology department directors. Decis. Support. Syst. 42(2):1042–1053, 2006.CrossRefGoogle Scholar
  44. 44.
    Tornatzky, L. G., and Fleischer, M., The process of technological innovation. Lexington Books, Fleisher, 1990.Google Scholar
  45. 45.
    Scupola, A., The adoption of Internet commerce by SMEs in the South of Italy: An environmental, technological and organizational perspective. J. Glob. Inf. Technol. Manag. 6(1):52–71, 2003.Google Scholar
  46. 46.
    Lin, C. Y., and Ho, Y. H., RFID technology adoption and supply chain performance: An empirical study in China’s logistics industry. Supply Chain Manag. Int. J. 14(5):369–378, 2009.CrossRefGoogle Scholar
  47. 47.
    Hossain, M.A., Quaddus, M., Impact of external environmental factors on RFID adoption in Australian livestock industry: An exploratory study, In Proceedings of the Pacific Asia Conference on Information Systems (PACIS), 1735–1742, 2010.Google Scholar
  48. 48.
    Chang, I. C., Hwang, H. G., Hung, M. C., Lin, M. H., and Yen, D. C., Factors affecting the adoption of electronic signature: Executives’ perspective of hospital information department. Decis. Support. Syst. 44(1):350–359, 2007.CrossRefGoogle Scholar
  49. 49.
    Lin, H. F., and Lee, G. G., Impact of organizational learning and knowledge management factors on e-business adoption. Manag. Decis. 43(2):171–188, 2005.CrossRefGoogle Scholar
  50. 50.
    Lee, S., and Kim, K., Factors affecting the implementation success of internet-based information systems. Comput. Hum. Behav. 23(4):1853–1880, 2007.CrossRefGoogle Scholar
  51. 51.
    Lewis, W., Agarwal, R., and Sambamurthy, V., Sources of influence on beliefs about information technology use: An empirical study of knowledge workers. MIS Q. 27(4):657–678, 2003.Google Scholar
  52. 52.
    Judi, H. M., Razak, A. A., Sha’ari, N., and Mohamed, H., Feasibility and critical success factors in implementing telemedicine. Inf. Technol. J. 8(3):326–332, 2009.CrossRefGoogle Scholar
  53. 53.
    Liu, C. F., Key factors influencing the intention of telecare adoption: An institutional perspective. Telemed. E-Health 17(4):288–293, 2011.CrossRefGoogle Scholar
  54. 54.
    Brown, I., and Russell, J., Radio frequency identification technology: An exploratory study on adoption in the South African retail sector. Int. J. Inf. Manag. 27(4):250–265, 2007.CrossRefGoogle Scholar
  55. 55.
    Azevedo, S. G., and Ferreira, J. J., Radio frequency identification: A case study of healthcare organisations. Int. J. Secur. Netw. 5(2–3):147–155, 2010.CrossRefGoogle Scholar
  56. 56.
    Nahas, H. A., and Deogun, J. S., Radio frequency identification applications in smart hospitals. In: Proceedings of the 20th IEEE International Sysmosium on Computer-Based Medical Systems. IEEE Computer Society, 337–342, 2007.Google Scholar
  57. 57.
    Yao, W., Chu, C. H., and Li, Z., The use of RFID in healthcare benefits and barriers. Proceeding of IEEE International Conference on RFID technology and applications (RFID-TA). IEEE Xplore Press, Guangzhou, pp. 128–134, 2010.Google Scholar
  58. 58.
    Chun, S. G., and Chung, D., Hospital administrators’ perception on the adoption of RFID: Empirical study including privacy/security. Issues Inf. Syst. 10(2):390–399, 2009.Google Scholar
  59. 59.
    Fisher, J. A., and Monahan, T., Tracking the social dimensions of RFID systems in hospitals. Int. J. Med. Inform. 77(3):176–183, 2008.PubMedCrossRefGoogle Scholar
  60. 60.
    Yu, W. D., Ray, P., and Motoc, T., WISH: A wireless mobile multimedia information system in healthcare using RFID. Telemed. J. E-Health 14(4):362–370, 2008.PubMedCrossRefGoogle Scholar
  61. 61.
    Aggelidis, V. P., and Chatzoglou, P. D., Using a modified technology acceptance model in hospitals. Int. J. Med. Inf. 78(2):115–126, 2009.CrossRefGoogle Scholar
  62. 62.
    Hakim, H., Renouf, R., and Enderle, J., Passive RFID asset monitoring system in hospital environments. In: Proceedings of the IEEE 32nd Annual Northeast Bioengineering Conference, Easton, PA, USA. 217–218, 2006.Google Scholar
  63. 63.
    Glabman, M., Room for tracking. RFID technology finds the way. Mater. Manag. Health Care 13(5):26–28, 2004.PubMedGoogle Scholar
  64. 64.
    Petter, S., Straub, D., and Rai, A., Specifying formative constructs in information systems research. MIS Q. 31:623–656, 2007.Google Scholar
  65. 65.
    Venkatesh, V., Determinates 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
  66. 66.
    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. Telemed. E-Health 16(3):327–343, 2010.CrossRefGoogle Scholar
  67. 67.
    Ramayah, T., and Lo, M. C., Impact of shared beliefs on “perceived usefulness” and “ease of use” in the implementation of enterprise resource planning system. Manage. Res. News 30(6):420–431, 2007.CrossRefGoogle Scholar
  68. 68.
    Shaqrah, A. A., Adoption of telemedicine among health care services: The strategic adoption. J. E-Health Manag., 1–19, 2010.Google Scholar
  69. 69.
    Hair, J. F., Ringle, C. M., and Sarstedt, M., PLS-SEM: Indeed a silver bullet. J. Mark. Theory Pract. 19(2):139–151, 2011.CrossRefGoogle Scholar
  70. 70.
    Henseler, J., Ringle, C. M., and Sinkovics, R. R., The use of partial least squares path modeling in international marketing. Adv. Int. Mark. 20:277–319, 2009.Google Scholar
  71. 71.
    Hair, J. F., Hult, G. T. M., Ringle, C. M., and Sarstedt, M., A primer on partial least squares structural equation modeling (PLS-SEM). Sage, Thousand Oaks, 2013.Google Scholar
  72. 72.
    Hair, J. F., William, C. B., Barry, J. B., and Rolph, E. A., Multivariate data analysis. Prentice Hall, Englewood Cliffs, 2010.Google Scholar
  73. 73.
    Fornell, C., and Larcker, D. F., Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 18(1):39–50, 1981.CrossRefGoogle Scholar
  74. 74.
    Hiar, J. F., Sarstedt, M., Ringle, C. M., and Mena, J. A., An assessment of the use of partial least squares structural equation modeling in marketing research. J. Acad. Mark. Sci. 40(30):414–433, 2012.CrossRefGoogle Scholar
  75. 75.
    Stone, M., Cross-validatory choice and assessment of statistical predictions. J. R. Stat. Soc. 36(2):111–147, 1974.Google Scholar
  76. 76.
    Geisser, S., The predictive sample reuse method with applications. J. Am. Stat. Assoc. 70(350):320–328, 1975.CrossRefGoogle Scholar
  77. 77.
    Chin, W. W., How to write up and report PLS analyses. In: Vinzi, V. E., Chin, W. W., Henseler, J., and Wang, H. (Eds.), Handbook of partial least squares: concepts, methods and applications in marketing and related fields. Springer, Berlin, pp. 655–690, 2010.CrossRefGoogle Scholar
  78. 78.
    Wetzels, M., Odekerken-Schroder, G., and van Oppen, C., Using PLS path modeling for assessing hierarchical construct models: Guidelines and empirical illustration. MIS Q. 33(1):177–195, 2009.Google Scholar
  79. 79.
    Jeyaraj, A., Rottman, J. W., and Lacity, M. C., A review of the predictors, linkages, and biases in IT innovation adoption research. J. Inf. Technol. 21(1):1–23, 2006.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Suhaiza Zailani
    • 1
  • Mohammad Iranmanesh
    • 1
  • Davoud Nikbin
    • 2
  • Jameson Khoo Cheong Beng
    • 3
  1. 1.University of MalayaKuala LumpurMalaysia
  2. 2.Faculty of Business, Multimedia UniversityMelakaMalaysia
  3. 3.Universiti Sains MalaysiaPenangMalaysia

Personalised recommendations