Evaluation of the Users’ Continuous Intention to Use PACS Based on the Expectation Confirmation Model in Teaching Hospitals of Shiraz University of Medical Sciences

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Abstract

Introduction: Users’ behavioral intention to use the Picture Archiving and Communication System (PACS) is important in the systems’ success and is an indicator of the users’ satisfaction with commitment and dependence on information systems. The present study aimed to evaluate the users’ continuous intention to use PACS based on the expectation confirmation model in educational hospitals of Shiraz University of Medical Sciences.Method: This cross-sectional study was conducted in Nemazee and Shahid Faghihi hospitals, Shiraz, Iran in 2014. The subjects were50 general practitioners, residents and specialists selected through stratified random sampling. The study data were collected using a researcher-made questionnaire. The content validity of the questionnaire items was confirmed by five experts in health information management. To evaluate the accuracy of relationships among the measurement models, reliability criteria, including Cronbach’s alpha and composite reliability, convergent and divergent validity were used which showed acceptable reliability and validity. The data were entered into Smart PLS software, version 3.1.9 and analyzed through Structural Equation Modeling (SEM) by using Partial Least Squares (PLS) approach.Results: The results showed appropriate fitness of reliability indices (Cronbach’s alpha >0.7, composite reliability >0.7, loading >0.7), validity indices (AVE >0.5), structural  model (redundancy  =0.395, Q2CI=0.364, f2H5=0.524, R2CI=0.687), and the total model (GoF=0.518). Moreover, all the research hypotheses, except H1 (the relationship between expectation confirmation and perceived usefulness) with T-value of <1.96, showed a significant relationship (T-value >1.96).Conclusion: Expectation confirmation, perceived usefulness, and satisfaction were effective in continuous intention to use PACS. Thus, these factors should be considered by designers, developers, and managers while designing and implementing information systems to guarantee their success and improve the quality of health services.Keywords: Information Systems, Expectation confirmation model, PACS, Satisfactio

References

  1. Van de Velde R, Degoulet P. Clinical Information Systems: A
  2. Component-Based Approach. 2003.
  3. Huang H. PACS and Imaging Informatics: Basic Principles and
  4. Applications,2nd Edition 2010.
  5. Hurlen P, Borthne A, Dahl FA, Ostbye T, Gulbrandsen P.
  6. Does PACS improve diagnostic accuracy in chest radiograph interpretations in clinical practice? European journal of radiology. 2012 Jan;81(1):173-7.
  7. De Backer AI, Mortele KJ, De Keulenaer BL. Picture archiving and communication system--Part one: Filmless radiology and distance radiology. JBR-BTR. 2004 Sep-Oct;87(5):234-41.
  8. Mansoori B, Erhard KK, Sunshine JL. Picture Archiving and
  9. Communication System (PACS) implementation, integration
  10. & benefits in an integrated health system. Acad Radiol. 2012
  11. Feb;19(2):229-35.
  12. Pare G, Trudel MC. Knowledge barriers to PACS adoption and implementation in hospitals. Int J Med Inform. 2007
  13. Jan;76(1):22-33.
  14. Bhattacherjee A. Understanding information systems continuance: an expectation-confirmation model. MIS Q.
  15. ;25(3):351-70.
  16. Thong JYL, Hong S-J, Tam KY. The effects of post-adoption beliefs on the expectation-confirmation model for information technology continuance. International Journal of Human- Computer Studies. 2006;64(9):799-810.
  17. Santos J, Boote J. A theoretical exploration and model of consumer expectations, post-purchase affective states and affective behaviour. Journal of Consumer Behaviour.
  18. ;3(2):142-56.
  19. Brown SA, Venkatesh,Viswanath, Kuruzovich,Jason, Massey,Anne P. Expectation confirmation: An examination of three competing models. Organizational Behavior and Human Decision Processes. 2005;105:52-66.
  20. Mou J, Cohen J. A Longitudinal Study of Trust and Perceived Usefulness in Consumer Acceptance of an eService: The Case of Online Health Services. Pacific Asia Conference on Information Systems2014.
  21. Cheng Y. What Drives Nurses’ Blended e-Learning Continuance
  22. Intention? . Educational Technology & Society. 2014;17(4):203-
  23. Koo C, Wati Y, Park K, Lim MK. Website quality, expectation, confirmation, and end user satisfaction: the knowledge-intensive website of the Korean National Cancer Information Center. Journal of medical Internet research. 2011;13(4):e81.
  24. Lee M-C. Explaining and predicting users’ continuance intention toward e-learning: An extension of the expectation– confirmation model. Computers & Education. 2010;54(2):506-
  25. Stone RW, Baker-Eveleth L. Students’ expectation, confirmation, and continuance intention to use electronic textbooks. Computers in Human Behavior. 2013;29(3):984-90.
  26. Palm J-M, Dart T, Dupuis I, Leneveut L, Degoulet P. Clinical Information System Post-Adoption Evaluation at the Georges Pompidou University Hospital. AMIA Annual Symposium Proceedings. 2010 11/13;2010:582-6.
  27. Tabibi J, Farhangi A, Nasiripour A, Baradaran Kazemzadeh R, Ebrahimi P. The Effect of Supervisors and Work Group on Hospital Information System Acceptance Model. Journal of Health Administration. 2013;15(50):52-64.
  28. Hadji B, Dupuis I, Leneveut L, Heudes D, Wagner JF, Degoulet P. Determinants of continuance intention in a post-adoption satisfaction evaluation of a clinical information system. Stud Health Technol Inform. 2014;205:990-4.
  29. Lippert SK. Investigating Postadoption Utilization: An Examination Into the Role of Interorganizational and Technology Trust. IEEE Transactions on Engineering Management.
  30. ;54(3):468-83.
  31. Wang Y-S. Assessing e-commerce systems success: a respecification and validation of the DeLone and McLean model of IS success. Information Systems Journal. 2008;18(5).
  32. Davari A, Rezazadeh A. equation modeling with PLS. Tehran: Jahad University2013.
  33. Chin W. Issues and opinion on structural equation modeling.
  34. MIS quarterly. 1998;22(1):7-16.
  35. Cohen J. Statistical Power Analysis for the Behavioral Sciences
  36. Hillsdale, New Jersey: Lawrence Erlbaum Associates; 1988.
  37. Henseler J, Ringle CM, Sinkovics RR. The use of partial least squares path modeling in international marketing. Advances in international marketing. 2009;20(1):277-319.
  38. Wetzels M, Odekerken-Schröder G, Van Oppen C. Using PLS path modeling for assessing hierarchical construct models: Guidelines and empirical illustration. MIS Quarterly. 2009:177-
  39. Po-An Hsieh JJ, Wang W. Explaining employees’ Extended Use of complex information systems. European Journal of Information Systems. 2007;16(3):216-27.
Health Management & Information Science
Volume 4, Issue 1 - Serial Number 1
21
January 2017
Pages 12-16

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