Testing a Model of Adoption and Continued Use of Personally Controlled Electronic Health Record (PCEHR) System Among Australian Consumers: A Preliminary Study

  • Jun XuEmail author
  • Xiangzhu Gao
  • Golam Sorwar
  • Nicky Antonius
  • John Hammond
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10898)


This study aims to investigate factors influencing adoption and continued use of PCEHR system among consumers (individual users) in Australia. The data collected via online questionnaire survey were analysed via a Structural Equation Modelling (SEM) approach. The results indicate that: (1) “External Factors & Influences” and “Individual Differences” are significant factors that influence “Perceived Benefits” of the PCEHR system, which in turn influence adoption of the PCEHR system; (2) “External Factors & Influences”, “Individual Differences”, and “PCEHR System Characteristics” are significant factors that influence “Perceived User Friendliness” of the PCEHR system, which in turn influence adoption of PCEHR system; (3) “Facilitating Factors” are significant factors that influence both “Realized Benefits” and “Realized User Friendliness”, which in turn influence continued use of PCEHR system; and (4) “Voluntariness of Adoption” and “Voluntariness of Continued Use” are significant factors that influence both adoption and continued use of the PCEHR system respectively.


Electronic Health Records PCEHR system Adoption Continued use Structural equation modelling Australia 


  1. 1.
    Agarwal, R., Prased, J.: Are individual differences germane to the acceptance of new information technologies. Decis. Sci. 30(2), 361–391 (1999)CrossRefGoogle Scholar
  2. 2.
    Ajzen, I., Fishbein, M.: Understanding Attitudes and Predicting Social Behavior, vol. 07632. Prentice-Hall Inc, Englewood Cliffs (1980)Google Scholar
  3. 3.
    Anderson, J.C., Gerbing, D.W.: Some methods for respecifying measurement models to obtain unidimensional construct measurement. J. Mark. Res. 19(4), 453–460 (1982)CrossRefGoogle Scholar
  4. 4.
    Anderson, J.C., Gerbing, D.W.: Structural equation modelling in practice: A review and recommended two-stepped approach. Psychol. Bull. 103(3), 411–423 (1988)CrossRefGoogle Scholar
  5. 5.
    Arbuckle, J.: Amos 21 User’s Guide, IBM (2012)Google Scholar
  6. 6.
    Australian Government: Budget 2017–2018: Budget Statements 2017–18 Budget Related Paper No. 1.10, Health Portfolio (2017), Accessed 14 May 2017.$File/2017-18_Health_PBS_Complete.pdf
  7. 7.
    Bagozzi, R.P., Yi, Y.: Specification, evaluation, and interpretation of structural equation models. J. Acad. Mark. Sci. 40(1), 8–34 (2012)CrossRefGoogle Scholar
  8. 8.
    Barrett, P.: Structural equation modeling: adjusting model fit. Pers. Individ. Differ. 42(5), 815–824 (2007)CrossRefGoogle Scholar
  9. 9.
    Bentler, P.M., Chou, C.P.: Practical issues in Structural Modelling. Sociol. Methods Res. 16(1), 78–117 (1999)CrossRefGoogle Scholar
  10. 10.
    Browne, M.W., Cudeck, R.: Alternative ways of assessing model fit. In: Bollen, K.A., Long, J.S. (eds.) Testing structural equation models. Sage, Newbury Park, CA (1993)Google Scholar
  11. 11.
    Breckler, S.J.: Applications of covariance structure modeling in psychology: Cause for concern? Psychol. Bull. 107(2), 260–273 (1990)CrossRefGoogle Scholar
  12. 12.
    Byrne, B.M.: Structural Equation Modeling With Amos: Basic Concepts, Applications, and Programming. Lawrence Erlbaum Associates, New York (2001)Google Scholar
  13. 13.
    Coyne, A.: Australia’s first opt-out e-health site to start trials this week, itnews, 27 January 2016. Accessed 22 Feb 2016.
  14. 14.
    Davis, F.D.: Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 13(3), 319–340 (1989)CrossRefGoogle Scholar
  15. 15.
    Davis, F.D., Bagozzi, R.P., Warshaw, P.R.: User acceptance of computer technology: a comparison of two theoretical models. Manag. Sci. 35(8), 982–1002 (1989)CrossRefGoogle Scholar
  16. 16.
    Department of Health and Aging: Expected Benefits of the National PCEHR System, May 2012Google Scholar
  17. 17.
    Department of Health: Patients to get new myHealth Record: $485 m ‘rescue’ package to reboot Labor’s e-health failures, Media Release, 10 May 2015. Accessed. 18 Feb 2016.
  18. 18.
    Garson, D.G.: Structural Equation Modeling. Statistical Associates Publishing, Asheboro (2012)Google Scholar
  19. 19.
    Goldstein, H., Bonnet, G., Rocher, T.: Multilevel structural equation models for the analysis of comparative data on educational performance. J. Educ. Behav. Stat. 32(3), 252–286 (2007)CrossRefGoogle Scholar
  20. 20.
    Hair, J.F., Black, W., Babin, B., Anderson, R.: Multivariate data analysis. Prentice Hall Inc, Upper Saddle River (2010)Google Scholar
  21. 21.
    Hair, J.F., Bush, R., Ortinau, D.: Marketing Research. McGraw-Hill Companies Incorporated, New York (2008)Google Scholar
  22. 22.
    Hartwick, J., Barki, H.: Explaining the role of user participation in information system use. Manag. Sci. 40(4), 440–465 (1994)CrossRefGoogle Scholar
  23. 23.
    Hox, J.J., Bechger, T.M.: An Introduction to Structural Equation Modeling. Fam. Sci. Rev. 11, 354–373 (1998)Google Scholar
  24. 24.
    Hu, L., Bentler, P.M.: Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Struct. Equ. Model. 6(1), 1–55 (1999)CrossRefGoogle Scholar
  25. 25.
    Jackson, D.L.: Revisiting sample size and number of parameter estimates: some support for the N: q hypothesis. Struct. Equ. Model. 10(1), 128–141 (2003)MathSciNetCrossRefGoogle Scholar
  26. 26.
    Jöreskog, K.G.: Testing structural equation models. In: Bollen, K.A., Long, J.S. (eds.) Testing structural equation models, pp. 294–316. SAGE Publications, Newbury Park (1993)Google Scholar
  27. 27.
    Jöreskog, K.G., Sorbom, D.: LISREL VI: Analysis of Linear Structural Relationship by the Method of Maximum Likelihood. National Education Services, Chicago (1984)Google Scholar
  28. 28.
    Jöreskog, K.G.: On chi-squares for the independence model and fit measures in LISREL (2004). Accessed. 10 Jul 2017.
  29. 29.
    Kerlin, J., Heath, J.: E-health scheme to be revived after panel review, The Australian Financial Review, 24 May 2014. Accessed 18 Feb 2016.
  30. 30.
    Kline, R.B.: Principles and practice of structural equation modeling. The Guilford Press, New York (2001)zbMATHGoogle Scholar
  31. 31.
    Liker, J.K., Sindi, A.: User acceptance of expert systems: a test of the theory of reasoned action. J. Eng. Tech. Manage. 14(2), 147–173 (1997)CrossRefGoogle Scholar
  32. 32.
    Lohman, T.: Australian e-health spending to top $2 billion in 2010, Computerworld, 15 April 2010. Accessed 30 Jun 2016.
  33. 33.
    Lucas, H.C., Spitler, V.K.: Technology use and performance: a field study of broker workstations. Decis. Sci. 30(2), 291–311 (1999)CrossRefGoogle Scholar
  34. 34.
    McQuade-Jones, B., Murphy, J., Novak, T., Lisa-Nicole, S.: Nurse practitioners and meaningful use: transforming health care. J. Nurs. Pract. 10(10), 763–768 (2014)CrossRefGoogle Scholar
  35. 35.
    McQuitty, S.: Statistical power and structural equation models in business research. J. Bus. Res. 57(2), 175–183 (2004)CrossRefGoogle Scholar
  36. 36.
    Manning, M., Munro, D.: The Survey Researcher’s SPSS Cookbook. Pearson Education Australia, Sydney (2007)Google Scholar
  37. 37.
    Mendelson, D., Wolf, G.: My electronic health record - Cui Bono: for whose benefit. J. Law Med. 24, 283–296 (2016)Google Scholar
  38. 38.
    Mendelson, D., Wolf, G.: Health privacy and confidentiality. In: Freckelton, I., Petersen, K. (eds.) Tensions and Traumas in Health Law. Federation Press, Sydney (2017)Google Scholar
  39. 39.
    Moore, G.C.: End-user computing and office automation: a diffusion of innovation perspectives. INFOR 25(3), 214–235 (1987)Google Scholar
  40. 40.
    Muhammad, I., Teoh, S.Y., Wickramasinghe, N.: The application of a sociotechnical analysis for the personally controlled electronic health record, In: Proceedings of PACIS 2012 (2012)Google Scholar
  41. 41.
    Nguyen, L., Bellucci, E., Nguyen, L.T.: Electronic health records implementation: an evaluation of information system impact and contingency factor. Int. J. Med. Inform. 83, 779–796 (2014)CrossRefGoogle Scholar
  42. 42.
    Partel, K.: Toward better implementation: Australia’s My Health Record, Deeble Institute Issues Brief, No. 13, 30/10/2015, pp. 1–20 (2015)Google Scholar
  43. 43.
    Rogers, E.M.: Diffusion of Innovations, 4th edn. The Free Press, New York (1985)Google Scholar
  44. 44.
    Sansom, M.: My Health Record: Medics speak up, Government News, 15 August 2016. Accessed 15 Apr 2017.
  45. 45.
    Schumacker, R.E., Lomax, R.G.: A Beginner’s Guide to Structural Equation Modeling. Routledge, New York (2010)zbMATHGoogle Scholar
  46. 46.
    Smith, C., Ellis, I., Jaffray, L.: Nursing competencies needed for electronic advance care planning in community. GSTF Int. J. Nurs. Health Care 1(1), 160–164 (2013)Google Scholar
  47. 47.
    Steininger, K., Stiglbauer, B.: EHR acceptance among Austrian resident doctors. Health Policy Technol. 2015(4), 121–130 (2015)CrossRefGoogle Scholar
  48. 48.
    Swinn, M., Weber, M.: My Health Record: The resuscitation of e-health, or a data placebo?, Image & Data Manager, 13 April 2017. Accessed 10 May 2017.
  49. 49.
    Tabachnick, B.G., Fidell, L.S.: Using multivariate statistics. Allyn and Bacon, Boston (2001)Google Scholar
  50. 50.
    Tanaka, J.S.: How big is big enough: sample size and goodness of fit in structural equation models with latent variables. Child Dev. 58, 134–146 (1987)CrossRefGoogle Scholar
  51. 51.
    Taylor, S., Todd, P.A.: Understanding information technology usage: a test of competing models. Inf. Syst. Res. 6(2), 145–177 (1995)CrossRefGoogle Scholar
  52. 52.
    Thompson, R.L., Higgins, C.A., Howell, J.M.: Personal computing: toward a conceptual model of utilization. MIS Q. 15(1), 125–143 (1981)CrossRefGoogle Scholar
  53. 53.
    Ward, M.J., Froehle, C.M., Hart, K.W., Collins, S.P., Lindsell, C.J.: Transient and sustained changes in operational performance, patient evaluation, and medication administration during electronic health record implementation in the emergency department. Ann. Emerg. Med. 63(3), 320–328 (2014)CrossRefGoogle Scholar
  54. 54.
    Xu, J., Quaddus, M.: Examining a model of knowledge management systems adoption and diffusion: a partial least square approach. J. Knowl.-Based Syst. 27, 18–28 (2012)CrossRefGoogle Scholar
  55. 55.
    Xu, J., Gao, X., Sorwar, G., Croll, P.: Current status, challenges, and outlook of E-Health record systems in Australia. In: Sun, F., Li, T., Li, H. (eds.) Knowledge Engineering and Management. AISC, vol. 214, pp. 683–692. Springer, Heidelberg (2014). Scholar
  56. 56.
    Xu, J., Gao, X.J., Sorwar, G., Croll, P.: Implementation of E-Health Record Systems in Australia. Int. Technol. Manag. Rev. 3(2), 92–104 (2013)CrossRefGoogle Scholar
  57. 57.
    Xu, J., Gao, X.J., Sorwar, G.: A research model of consumers’ adoption and continued use of the Personally Controlled Electronic Health Record (PCEHR) system. Int. Technol. Manag. Rev. 4(4), 187–200 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Jun Xu
    • 1
    Email author
  • Xiangzhu Gao
    • 1
  • Golam Sorwar
    • 1
  • Nicky Antonius
    • 1
  • John Hammond
    • 1
  1. 1.Southern Cross UniversityLismoreAustralia

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