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Influential processes for the acceptance of protectors toward emergency care for patient based on an elaboration likelihood model
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 Title & Authors
Influential processes for the acceptance of protectors toward emergency care for patient based on an elaboration likelihood model
Hwang, Ji-Young; Kim, Yun-Kwon; Kim, Ki-Young;
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 Abstract
Purpose: This study validated the influential relations between the effects of emergency care quality, credibility of 119 emergency medical technicians (119 EMTs), and perceived usefulness and attitude of emergency care, focusing on the moderating effect of protectors` characteristics (education, experience, age, and recognition of patient severity). Methods: This study was based on elaboration likelihood and technology acceptance models. In total, 172 protectors with experience in utilizing prehospital service were surveyed from April 1 to July 31, 2011. Results: The results showed that the emergency care quality and the credibility of 119 EMTs were the main determinants of the perceived usefulness and attitude of emergency care, irrespective of the protector`s characteristics (p <.001). In addition, the findings showed that the protector`s intention of emergency care had a moderating role. The impact of the quality of emergency care on its perceived usefulness was greater for high-level protectors (p <.001). By contrast, the impact of the credibility of 119 EMTs on the perceived usefulness of emergency care was greater for low-level protectors (p <.001). Conclusion: The protectors` characteristics have different influences on the relations between the effects of emergency care quality, the 119 EMT credibility, and the perceived usefulness and attitude of emergency care.
 Keywords
Credibility;Emergency medical technician;Elaboration likelihood model;Protectors;Quality;
 Language
Korean
 Cited by
 References
1.
Sin HS. The evolution of health and utilization inequalities over time. Health and Welfare Policy Forum 2009;149:26-35.

2.
Choi GS, Kim YK. Analysis of prehospital care report for improving emergency service at prehospital phase. Korean J Emerg Med Ser 2007;11(3):163-74.

3.
Park SS, Park JS. A study on the use realities and satisfaction with transport services in 119 emergency medical service system and private transport agent in some areas. Korean J Emerg Med Ser 2008;12(1):5-15.

4.
Kang KH. Predictors of emergency medical transports use based on 2009 Korea health panel. J Korean Inst Fire Sci Eng 2014; 28(3):80-6.

5.
Baek HS. Determinants of the demand for public ambulance calls in a metropolitan area. Korean J Emerg Med Ser 2008;12(3): 129-35.

6.
Davis FD, Bagozzi RP, Warshaw PR. User use intention of computer technology: A comparison of two theoretical models. Manag Sci 1989;35:982-1003. crossref(new window)

7.
Venkatesh V, Davis FD. A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science 2000;46(2):186-205. http://dx.doi.org/10.1287/mnsc.46.2.186.11926 crossref(new window)

8.
Karsh B, Holden RJ. The technology use intention model: Its past and its future in health care. J Biomed Inform 2000;43(1): 159-72.

9.
Hu PJH, Chau PTK, Sheng ORL. Adoption of telemedicine technology by health care organizations: An exploratory study. Journal of Organizational Computing and Electronic Commerce 2002;12(3):197-22. http://dx.doi.org/10.1207/S15327744JOCE1203_01 crossref(new window)

10.
Taylor S, Todd PA. Understanding information technology usage: A test of competing models. Inf Syst Res 1995;6(2):144-76. http://dx.doi.org/10.1287/isre.6.2.144 crossref(new window)

11.
Wood W. Attitude change: Persuasion and social influence. Annu Rev Psychol 2000;51: 539-70. http://dx.doi.org/10.1146/annurev.psych.51.1.539 crossref(new window)

12.
Lee WK, An Longitudinal analysis of changing beliefs on the use in IT educatee by elaboration likelihood model. Asia Pacific Journal of Information Systems 2008;18(3):147-65.

13.
Wu JH, Wang SC, Lin LM. Mobile computing acceptance factors in the healthcare industry: A structural equation model. Int J Med Inform 2007;76(1):66-77. http://dx.doi.org/10.1016/j.ijmedinf.2006.06.006 crossref(new window)

14.
Bhattacherjee A, Sanford C. Influence processes for information technology use intention: An elaboration likelihood model. Manag Inform Syst Q 2006;30:805-25. http://dx.doi.org/10.1016/j.ijmedinf.2006.06.006 crossref(new window)

15.
Sussman SW, Siegal WS. Informational in fluence in organizations: An integrated approach to knowledge adoption. Inf Syst Res 2003;14(1):47-65. http://dx.doi.org/10.1287/isre.14.1.47.14767 crossref(new window)

16.
Kim KY, Kim YK, Lee KH, Yong SJ. Factors affecting the use of a realtime telemetry system in emergency medical services. J Telemed Telecare 2011;17(8):444-5. http://dx.doi.org/0.1258/jtt.2011.110305

17.
Hwang JY, Kim KY, Lee KH. Factors that influence the acceptance of telemetry by emergency medical technicians in ambulances: An application of the extended technology acceptance model. Telemed J E Health 2014; 20(12):1127-34. http://dx.doi.org/10.1089/tmj.2013.0345 crossref(new window)

18.
Kim EH. Continuance intention of power- twitter from elaboration likelihood model perspectives. Unpublished master's thesis, Kyunghee University 2012, Seoul, Korea.

19.
Lee WK, An Longitudinal Analysis of changing beliefs on the use in IT educatee by elaboration likelihood model. Asia Pacific Journal of Information Systems 2008;18(3):147-65.

20.
Petty RE. Haughtvedt CP, Smith SM. Elaboration as a determinant of attitude strength: Creating attitudes that are persistent, resistant, and predictive of behavior, in attitude strength: Antecedents and consequences. Lawrence Erlbaum Associates, 1995. 93-130.

21.
Taylor S, Todd PA. Understanding information technology usage: A test of competing models. Inf Syst Res 1995;6(2): 144-76. crossref(new window)

22.
Bagozzi RP, Phillips LW. Representing and testing organizational theories: A holistic construal. Administrative Science Quarterly 1982;27(3):459-89. http://dx.doi.org/10.2307/2392322 crossref(new window)

23.
Fornell C, Larcker DF. Evaluating structural equation models with unobservable variables and measurement error. J Mark Res 1981; 18:39-50. http://dx.doi.org/10.2307/3151312 crossref(new window)

24.
Petty RE, Wegener DT. The elaboration likelihood model: Current status and controversies, in dual-process theories in social psychology. Guilford Press, 1999. 112-25.

25.
Chin WW, Todd PA. On the use, usefulness, and ease of use of structural equation modeling in MIS research: A note of caution. Manag Inform Syst Q 1995;19:237-46. http://dx.doi.org/10.2307/249690 crossref(new window)

26.
Gagnon MP, Orruno E, Asua J, Abdeljelil AB, Emparanza J. Using a modified technology acceptance model to evaluate healthcare professionals' adoption of a new telemonitoring system. Telemed J E Health 2012;18(1):54-9. http://dx.doi.org/10.1089/tmj.2011.0066 crossref(new window)

27.
Dunnebeil 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. http://dx.doi.org/10.1016/j.ijmed inf.2012.02.002 crossref(new window)

28.
Orruno E, Gagnon MP, Asua J, Ben AA. Evaluation of teledermatology adoption by health-care professionals using a modified Technology Acceptance Model. J Telemed Telecare 2011;17(6):303-7. http://dx.doi.org/10.1258/jtt.2011.101101 crossref(new window)

29.
Schaper L, Pervan G. ICT and OTs: A model of information and communication technology acceptance and utilisation by occupational therapists. Int J Med Inform 2007; 76S(1):S212-21. http://dx.doi.org/10.1016/j.ijmedinf.2006.05.028 crossref(new window)