How habit affects continuous use: evidence from Jordan’s national health information system

References

• Painter MW, Jha AK, Health Information Technology in the United States. Transition to a Post-HITECH World (Executive Summary). Mathematica Policy Research. New Jersey, USA: Robert Wood Johnson Foundation. 2015.
   Google Scholar
• Sayyah Gilani M, Iranmanesh M, Nikbin D, Zailani S. EMR continuance usage intention of healthcare professionals. Inform Health Soc Care. 2017;42(2):153–65. doi:10.3109/17538157.2016.1160245.
   PubMed Web of Science ®Google Scholar
• Bleich HL, Slack WV. Reflections on electronic medical records: when doctors will use them and when they will not. Int J Med Inform. 2010;79(1):1–4. doi:10.1016/j.ijmedinf.2009.10.002.
   PubMed Web of Science ®Google Scholar
• Jenkin MA. Clinical informatics: a strategy for the use of information in the clinical setting. Med Inform. 1984;9(3–4):225–32. doi:10.3109/14639238409015196.
   PubMed Web of Science ®Google Scholar
• Protti D, Johansen I, Perez-Torres F. Comparing the application of health information technology in primary care in Denmark and Andalucía, Spain. Int J Med Inform. 2009;78(4):270–83. doi:10.1016/j.ijmedinf.2008.08.002.
   PubMed Web of Science ®Google Scholar
• Hoque MR, Bao Y, Sorwar G. Investigating factors influencing the adoption of e-Health in developing countries: a patient’s perspective. Inform Health Soc Care. 2017;42(1):1–17. doi:10.3109/17538157.2015.1075541.
   PubMed Web of Science ®Google Scholar
• Fraser H, Biondich P, Moodley D, Choi S, Mamlin B, Szolovits P. Implementing electronic medical record systems in developing countries. J Innovation Health Inform. 2005;13(2):83–95. doi:10.14236/jhi.v13i2.585.
   Google Scholar
• Sood SP, Nwabueze SN, Mbarika VW, Prakash N, Chatterjee S, Ray P, et al., editors. Electronic medical records: a review comparing the challenges in developed and developing countries. Hawaii International Conference on System Sciences, Proceedings of the 41st Annual, Hawaii, USA; 2008: IEEE.
   Google Scholar
• Ahlan AR, Ahmad B. User acceptance of health information technology (HIT) in developing countries: a conceptual model. Procedia Technol. 2014;16:1287–96. doi:10.1016/j.protcy.2014.10.145.
   Google Scholar
• Blaya JA, Fraser HS, Holt B. E-health technologies show promise in developing countries. Health Aff. 2010;29(2):244–51. doi:10.1377/hlthaff.2009.0894.
   Web of Science ®Google Scholar
• Garman AN, Tran L. Knowledge of the healthcare environment. J Healthc Manag. 2006;51(3):152. doi:10.1097/00115514-200605000-00004.
   PubMed Web of Science ®Google Scholar
• Williams F, Boren S. The role of the electronic medical record (EMR) in care delivery development in developing countries: a systematic review. J Innovation Health Inform. 2008;16(2):139–45. doi:10.14236/jhi.v16i2.685.
   Google Scholar
• Tubaishat A. Perceived usefulness and perceived ease of use of electronic health records among nurses: application of technology acceptance model. Inform Health Soc Care. 2017;1–11. doi:10.1080/17538157.2017.1398753.
   PubMed Web of Science ®Google Scholar
• Wears RL, Berg MJJ. Computer technology and clinical work: still waiting for Godot. The Journal of The American Medical Association. 2005;293(10):1261–63.
   Google Scholar
• Beynon-Davies P, Lloyd-Williams M. When health information systems fail. Topics in health information management. 1999;20(1):66–79.
   Google Scholar
• Heeks R. Health information systems. Fail Success Improvisate. 2006;75:125–37.
   Google Scholar
• Abouzahra M, editor Causes of failure in healthcare IT projects. 3rd International Conference on Advanced Management Science, Kuala Lumpur, Malaysia; 2011: IACSIT Press Singapore.
   Google Scholar
• Heeks R, Mundy D, Salazar A. Why Health CareInformation Systems Succeed or Fail. Information Systems forPublic Sector Management Working Paper Series. Manchester: Institute for Development Policy and Management, Universityof Manchester, UK. 1999. Available at http://unpan1.un.org/intradoc/groups/public/documents/NISPAcee/UNPAN015482.pdf Heeks R, Mundy D, Salazar AJISfPSM. Why health care information systems succeed or fail. 1999.
   Google Scholar
• Liberati EG, Ruggiero F, Galuppo L, Gorli M, González-Lorenzo M, Maraldi M. What hinders the uptake of computerized decision support systems in hospitals? A qualitative study and framework for implementation. Implementation Sci. 2017;12(1):113. doi:10.1186/s13012-017-0644-2.
   PubMedGoogle Scholar
• Kijsanayotin B, Pannarunothai S, Speedie SM. Factors influencing health information technology adoption in Thailand’s community health centers: applying the UTAUT model. Int J Med Inform. 2009;78(6):404–16. doi:10.1016/j.ijmedinf.2008.12.005.
   PubMed Web of Science ®Google Scholar
• Lin C, Lin I-C RJ. Barriers to physicians’ adoption of healthcare information technology: an empirical study on multiple hospitals. J Med Syst. 2012;36(3):1965–77. doi:10.1007/s10916-011-9656-7.
   PubMed Web of Science ®Google Scholar
• Spetz J, Gates M, Jones CB. Internationally educated nurses in the United States: their origins and roles. Nurs Outlook. 2014;62(1):8–15. doi:10.1016/j.outlook.2013.05.001.
   PubMed Web of Science ®Google Scholar
• Scholl J, Syed-Abdul S, Ahmed LA. A case study of an EMR system at a large hospital in India: challenges and strategies for successful adoption. J Biomed Inform. 2011;44(6):958–67. doi:10.1016/j.jbi.2011.07.008.
   PubMed Web of Science ®Google Scholar
• Dünnebeil S, Sunyaev A, Blohm I, Leimeister JM, Krcmar H. Determinants of physicians’ technology acceptance for e-health in ambulatory care. Int J Med Inform. 2012;81(11):746–60. doi:10.1016/j.ijmedinf.2012.02.002.
   PubMed Web of Science ®Google Scholar
• Yarbrough AK, Smith TB. Technology acceptance among physicians: a new take on TAM. Med Care Res Rev. 2007;64(6):650–72. doi:10.1177/1077558707305942.
   PubMed Web of Science ®Google Scholar
• Chiasson MW, Davidson E. Pushing the contextual envelope: developing and diffusing IS theory for health information systems research. Inf Organ. 2004;14(3):155–88. doi:10.1016/j.infoandorg.2004.02.001.
   Google Scholar
• Bakker AR, Leguit FA. Evolution of an integrated HIS in the Netherlands. Int J Med Inform. 1999;54(3):209–24. doi:10.1016/S1386-5056(99)00008-8.
   PubMed Web of Science ®Google Scholar
• Davis FD, Bagozzi RP, Warshaw PR. User acceptance of computer technology: a comparison of two theoretical models. Manage Sci. 1989;35(8):982–1003. doi:10.1287/mnsc.35.8.982.
   Web of Science ®Google Scholar
• Egea JMO, González MVR. Explaining physicians’ acceptance of EHCR systems: an extension of TAM with trust and risk factors. Comput Human Behav. 2011;27(1):319–32. doi:10.1016/j.chb.2010.08.010.
   Web of Science ®Google Scholar
• Schnall R, Bakken S. Testing the technology acceptance model: HIV case managers’ intention to use a continuity of care record with context-specific links. Inform Health Soc Care. 2011;36(3):161–72. doi:10.3109/17538157.2011.584998.
   PubMed Web of Science ®Google Scholar
• Zhang X, Han X, Dang Y, Meng F, Guo X, Lin J. User acceptance of mobile health services from users’ perspectives: the role of self-efficacy and response-efficacy in technology acceptance. Inform Health Soc Care. 2017;42(2):194–206. doi:10.1080/17538157.2016.1200053.
   PubMed Web of Science ®Google Scholar
• Palm J-M, Colombet I, Sicotte C, Degoulet P, editors. Determinants of user satisfaction with a clinical information system. AMIA Annual Symposium Proceedings, Washington, DC, USA; 2006: American Medical Informatics Association.
   Google Scholar
• Chau PY, Hu PJ-H. Investigating healthcare professionals’ decisions to accept telemedicine technology: an empirical test of competing theories. Inf management. 2002;39(4):297–311. doi:10.1016/S0378-7206(01)00098-2.
   Web of Science ®Google Scholar
• Van der Heijden H. User acceptance of hedonic information systems. MIS Quarterly. 2004;695–704. doi:10.2307/25148660.
   Web of Science ®Google Scholar
• Koufaris M. Applying the technology acceptance model and flow theory to online consumer behavior. Inform system res. 2002;13(2):205–23. doi:10.1287/isre.13.2.205.83.
   Web of Science ®Google Scholar
• Gefen D. TAM or just plain habit: a look at experienced online shoppers. Journal Organizational End User Computing (JOEUC). 2003;15(3):1–13. doi:10.4018/JOEUC.
   Google Scholar
• Gefen D, Straub DW. Gender differences in the perception and use of e-mail: an extension to the technology acceptance model. MIS Quarterly. 1997;389–400. doi:10.2307/249720.
   Web of Science ®Google Scholar
• Venkatesh V, Morris MG, Davis GB, Davis FD. User acceptance of information technology: toward a unified view. MIS Quarterly. 2003;425–78. doi:10.2307/30036540.
   Web of Science ®Google Scholar
• Sezgin E, Yıldırım SÖ. A literature review on attitudes of health professionals towards health information systems: from e-Health to m-Health. Procedia Technol. 2014;16:1317–26. doi:10.1016/j.protcy.2014.10.148.
   Google Scholar
• Holden RJ, Karsh B-T. The technology acceptance model: its past and its future in health care. J Biomed Inform. 2010;43(1):159–72. doi:10.1016/j.jbi.2009.07.002.
   PubMed Web of Science ®Google Scholar
• Handayani PW, Hidayanto AN, Budi I. User acceptance factors of hospital information systems and related technologies: systematic review. Inform Health Soc Care. 2017;43(4):1–26. DOI:10.1080/17538157.2017.1353999
   Web of Science ®Google Scholar
• Schnall R, Higgins T, Brown W, Carballo-Dieguez A, Bakken S. Trust, perceived risk, perceived ease of use and perceived usefulness as factors related to mHealth technology use. Stud Health Technol Inform. 2015;216:467.
   PubMedGoogle Scholar
• Bhattacherjee A. Understanding information systems continuance: an expectation-confirmation model. MIS Quarterly. 2001;351–70. doi:10.2307/3250921.
   Web of Science ®Google Scholar
• Barnes SJ. Understanding use continuance in virtual worlds: empirical test of a research model. Inf Manag. 2011;48(8):313–19. doi:10.1016/j.im.2011.08.004.
   Web of Science ®Google Scholar
• Gebauer L, Söllner M, Leimeister JM. Towardsunderstanding the formation of continuous it use. 34th InternationalConference on Information Systems; 2013; Milan, Italy.
   Google Scholar
• Karahanna E, Straub DW, Chervany NL. Information technology adoption across time: a cross-sectional comparison of pre-adoption and post-adoption beliefs. MIS Quarterly. 1999;183–213. doi:10.2307/249751.
   Web of Science ®Google Scholar
• Kim SS, Malhotra NK. A longitudinal model of continued IS use: an integrative view of four mechanisms underlying postadoption phenomena. Manage Sci. 2005;51(5):741–55. doi:10.1287/mnsc.1040.0326.
   Web of Science ®Google Scholar
• Davis FD, Venkatesh V. Toward preprototype user acceptance testing of new information systems: implications for software project management. IEEE Trans Eng Management. 2004;51(1):31–46. doi:10.1109/TEM.2003.822468.
   Web of Science ®Google Scholar
• Venkatesh V, Morris MG, Ackerman PL. A longitudinal field investigation of gender differences in individual technology adoption decision-making processes. Organ Behav Hum Decis Process. 2000;83(1):33–60. doi:10.1006/obhd.2000.2896.
   PubMed Web of Science ®Google Scholar
• Handayani P, Hidayanto A, Pinem A, Sandhyaduhita P, Budi I. Hospital information system user acceptance factors: user group perspectives. Inform Health Soc Care. 2018;43(1):84–107. doi:10.1080/17538157.2016.1269109.
   PubMed Web of Science ®Google Scholar
• 2018 Available from: https://ehs.com.jo.
   Google Scholar
• Hersh W, Margolis A, Quirós F, Otero PJHA. Building a health informatics workforce in developing countries. Health affairs 2010;29(2):274–77. DOI:10.1377/hlthaff.2009.0883.
   Google Scholar
• Khalifa M. Technical and HumanChallenges of Implementing Hospital Information Systemsin Saudi Arabia. Journal of Health Informatics in DevelopingCountries (JHIDC). 2014;8(1): 12-25. Available from: www.jhidc.org/index.php/jhidc/article/view/111
   Google Scholar
• Adjorlolo S, Ellingsen G. Readinessassessment for implementation of electronic patient record inGhana: a case of University of Ghana Hospital. Journal of HealthInformatics in Developing Countries (JHIDC) (JHIDC).2013;7(2):128–140. Available from: http://www.jhidc.org/index.php/jhidc/article/view/104
   Google Scholar
• Were MC, Emenyonu N, Achieng M, Shen C, Ssali J, Masaba JP. Evaluating a Scalable Model for Implementing Electronic Health Records in Resource-Limited Settings 2010; 17(3): 237–44.
   Google Scholar
• Shu T, Liu H, Goss FR, Yang W, Zhou L, Bates DW. EHR Adoption across China’s Tertiary Hospitals: a Cross-Sectional Observational Study. International journal of medical informatics 2014; 83(2): 113–21.
   Google Scholar
• Davis FD. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly. 1989;319–40. doi:10.2307/249008.
   Web of Science ®Google Scholar
• Ajzen I, Fishbein M. Understanding attitudes and predicting social behaviour. 1980. NJ, USA: Prentice-Hall.
   Google Scholar
• Limayem M, Hirt SG, Cheung CM. How habit limits the predictive power of intention: the case of information systems continuance. MIS Quarterly. 2007;705–37. doi:10.2307/25148817.
   Web of Science ®Google Scholar
• Gebauer J. User requirements of mobile technology: a summary of research results. Inf Knowledge Syst Manag. 2008;7:101–19.
   Google Scholar
• Varma S, Marler JH. The dual nature of prior computer experience: more is not necessarily better for technology acceptance. Comput Human Behav. 2013;29(4):1475–82. doi:10.1016/j.chb.2013.01.029.
   Web of Science ®Google Scholar
• Limayem M, Cheung CM. Predicting the continued use of Internet-based learning technologies: the role of habit. Behav Inf Technol. 2011;30(1):91–99. doi:10.1080/0144929X.2010.490956.
   Web of Science ®Google Scholar
• Shiau W-L, Luo MM. Continuance intention of blog users: the impact of perceived enjoyment, habit, user involvement and blogging time. Behav Inf Technol. 2013;32(6):570–83. doi:10.1080/0144929X.2012.671851.
   Web of Science ®Google Scholar
• Strudwick G, Hardiker NR Understanding the use of standardized nursing terminology and classification systems in published research: a case study using the International classification for nursing practice®. International journal of medical informatics. 2016;94:215–21. doi: 10.1016/j.ijmedinf.2016.06.012
   Google Scholar
• Shaikh AA, Karjaluoto H. Making the most of information technology & systems usage: a literature review, framework and future research agenda. Comput Human Behav. 2015;49:541–66. doi:10.1016/j.chb.2015.03.059.
   Web of Science ®Google Scholar
• Giannakos MN, Chorianopoulos K, Giotopoulos K, Vlamos P. Using Facebook out of habit. Behav Inf Technol. 2013;32(6):594–602. doi:10.1080/0144929X.2012.659218.
   Web of Science ®Google Scholar
• Pahnila S, Warsta J. Online shopping viewed from a habit and value perspective. Behav Inf Technol. 2010;29(6):621–32. doi:10.1080/0144929X.2010.501115.
   Web of Science ®Google Scholar
• Wu M-C, Kuo F-Y. An empirical investigation of habitual usage and past usage on technology acceptance evaluations and continuance intention. ACM Sigmis Database. 2008;39(4):48–73. doi:10.1145/1453794.
   Google Scholar
• Limayenm M, Hirt SG, Cheung CM. Habit in the context of IS continuance: theory extension and scale development. ECIS 2003 Proceedings. 2003;11:90.
   Google Scholar
• Polites GL, Karahanna E. The embeddedness of information systems habits in organizational and individual level routines: development and disruption. Mis Quarterly. 2013;37:1. doi:10.25300/MISQ/2013/37.1.10.
   Web of Science ®Google Scholar
• Ouellette JA, Wood W. Habit and intention in everyday life: the multiple processes by which past behavior predicts future behavior. Psychol Bull. 1998;124(1):54. doi:10.1037/0033-2909.124.1.54.
   Web of Science ®Google Scholar
• Verplanken B, Aarts H, Van Knippenberg A. Habit, information acquisition, and the process of making travel mode choices. Eur J Soc Psychol. 1997;27(5):539–60. doi:10.1002/(ISSN)1099-0992.
   Web of Science ®Google Scholar
• Polites GL, editor Counterintentional habit as an inhibitor of technology acceptance. Proceedings of the 2005 southern association for information systems conference, Savannah, Georgia; 2005.
   Google Scholar
• De Guinea AO, Markus ML. Why break the habit of a lifetime? Rethinking the roles of intention, habit, and emotion in continuing information technology use. Mis Quarterly 2009: 433–44. doi: 10.2307/20650303.
   Web of Science ®Google Scholar
• Bargh JA, Ferguson MJ. Beyond behaviorism: on the automaticity of higher mental processes. Psychol Bull. 2000;126(6):925. doi:10.1037/0033-2909.126.6.925.
   PubMed Web of Science ®Google Scholar
• Verplanken B, Aarts H. Habit, attitude, and planned behaviour: is habit an empty construct or an interesting case of goal-directed automaticity? Eur Rev Soc Psychol. 1999;10(1):101–34. doi:10.1080/14792779943000035.
   Google Scholar
• Polites GL. The duality of habit in information technology acceptance. University of Georgia; 2009.
   Google Scholar
• Gewald H, Gewald C. Physician’s use of mandatory information systems: an exploratory research in German Hospitals. In Proceedings of The 50th Hawaii InternationalConference on System Sciences. 2017.
   Google Scholar
• Shekhter M, Potapova AY. The problem of habit formation: theoretical and applied aspects. Soviet J Psychol. 1991;12(3):3-16.
   Google Scholar
• van Bemmel JH, editor Criteria for the acceptance of decision-support systems by clinicians; lessons from ECG interpretation system. Proceedings of the Artificial Intelligence in Medicine Conference AIME, Munich, Germany; 1993. DOI:10.3233/978-1-60750-854-0-7
   Google Scholar
• Aggelidis VP, Chatzoglou PD. Using a modified technology acceptance model in hospitals. Int J Med Inform. 2009;78(2):115–26. doi:10.1016/j.ijmedinf.2008.06.006.
   PubMed Web of Science ®Google Scholar
• Melas CD, Zampetakis LA, Dimopoulou A, Moustakis V. Modeling the acceptance of clinical information systems among hospital medical staff: an extended TAM model. J Biomed Inform. 2011;44(4):553–64. doi:10.1016/j.jbi.2011.01.009.
   PubMed Web of Science ®Google Scholar
• Hair JF, Anderson RE, Tatham RL, Black W, (Ed). Multivariate Data Analysis. Upper Saddle River (NJ): Prentice Hall; 1998.
   Google Scholar
• Fornell C, Larcker DF. Evaluating structural equation models with unobservable variables and measurement error. J Mark Res. 1981;39–50. doi:10.2307/3151312.
   Web of Science ®Google Scholar
• Chin WW. The partial least squares approach to structural equation modeling. Modern Methods Business Research. 1998;295:295–336.
   Google Scholar
• Tabachnick BG, Fidell LS, Osterlind SJ. Using multivariate statistics. Boston, USA: Allyn and Bacon. 2001.
   Google Scholar
• Davcik S. The use and misuse of structural equation modeling in management research: a review and critique. J Adv Manag Res. 2014;11(1):47–81. doi:10.1108/JAMR-07-2013-0043.
   Google Scholar
• Hair JF, Ringle CM, Pls-Sem: SM. Indeed a silver bullet. J Marketing Theory Pract. 2011;19(2):139–52. doi:10.2753/MTP1069-6679190202.
   Google Scholar
• Qureshi I, Compeau D. Assessing between-group differences in information systems research: a comparison of covariance-and component-based SEM. Mis Quarterly. 2009;197–214. doi:10.2307/20650285.
   Web of Science ®Google Scholar
• Hair J, Anderson R, Tatham R, Black WJNJ. Multivariate data analysis, 6th ed. prentice hall, New Jersey, USA; 2006.
   Google Scholar
• Ajzen I. The theory of planned behavior prganizational behavior and human decision processes. Taylor, S and Todd, PA (1995) understanding information technology usage: a test of competing models. Inf Syst Res. 1991 June;6(2):144–76.
   Google Scholar
• de Veer AJ, Francke AL. Attitudes of nursing staff towards electronic patient records: a questionnaire survey. Int J Nurs Stud. 2010;47(7):846–54. doi:10.1016/j.ijnurstu.2009.11.016.
   PubMed Web of Science ®Google Scholar