Volume 20, Issue 1 (January - February 2021)
Payesh 2021, 20(1): 31-47 |
Back to browse issues page
1- Faculty of Economic & Administrative Sciences (FEAS) Ferdowsi University of Mashhad (FUM), Iran
2- Warsaw University, Warsaw, Poland
2- Warsaw University, Warsaw, Poland
Abstract: (3366 Views)
Objective (s): In a threatening situation such as Covid-19 Pandemic, E-health is more effective in providing public health, including prevention, monitoring, diagnosis, prioritization, treatment and follow-up patients. Regardless of E-health potential benefits, implementation and adaptation barriers is expected. In this regarding, it is essential to study the factors influencing EHCR adoption. Therefore, this study aimed to investigate the factors affecting the adoption of E-Health.
Methods: In this research, the mixt method approach and exploratory design - typology creation model have been used. After conducting semi-structured interviews and a focus discussion group among physicians, specialists, health experts and CEOs, a research model was developed and tested one a sample of 417 physicians in an online survey. Structural Equation Modeling (SEM) has also been used to analyze the data and test the research hypotheses.
Results: Trust and confidentiality, waiting time, authority, health provider-patient relationship are influencing factors that affect adoption of E-health factor. Five other factors were also found that were similar to the UTAUT2 model: performance expectancy, effort expectancy or ease of use, facilities, price value, habit. All hypotheses were significant because the absolute value of the significant number obtained from the t statistic in all hypotheses was higher than 1.96, with a 0.84 effect rate.
Conclusion: The findings from this study help to understand the factors influencing behavioral tendency in using E-Health. Theoretical findings, development, and validation in this dissertation provide a framework that includes the factors influencing the adoption of health technology, theoretical foundations for designing and selecting health technology in future health care before they enter the market, or solving the problems of their acceptance implementation.
Methods: In this research, the mixt method approach and exploratory design - typology creation model have been used. After conducting semi-structured interviews and a focus discussion group among physicians, specialists, health experts and CEOs, a research model was developed and tested one a sample of 417 physicians in an online survey. Structural Equation Modeling (SEM) has also been used to analyze the data and test the research hypotheses.
Results: Trust and confidentiality, waiting time, authority, health provider-patient relationship are influencing factors that affect adoption of E-health factor. Five other factors were also found that were similar to the UTAUT2 model: performance expectancy, effort expectancy or ease of use, facilities, price value, habit. All hypotheses were significant because the absolute value of the significant number obtained from the t statistic in all hypotheses was higher than 1.96, with a 0.84 effect rate.
Conclusion: The findings from this study help to understand the factors influencing behavioral tendency in using E-Health. Theoretical findings, development, and validation in this dissertation provide a framework that includes the factors influencing the adoption of health technology, theoretical foundations for designing and selecting health technology in future health care before they enter the market, or solving the problems of their acceptance implementation.
type of study: Descriptive |
Subject:
Helath Services Management
Received: 2020/12/14 | Accepted: 2021/02/13 | ePublished ahead of print: 2021/02/23 | Published: 2021/03/1
Received: 2020/12/14 | Accepted: 2021/02/13 | ePublished ahead of print: 2021/02/23 | Published: 2021/03/1
References
1. Akematsu Y, Tsuji M. An empirical approach to estimating the effect of e-health on medical expenditure. Journal of Telemedicine and Tele care 2010; 16:169-71 [DOI:10.1258/jtt.2010.004001]
2. Wickramasinghe NS, Fadlalla AM, Geisler E, Schaffer JL. A framework for assessing e-health preparedness. International Journal of Electronic Healthcare 2005 1; 1:316-34 [DOI:10.1504/IJEH.2005.006478]
3. Maheu M, Whitten P, Allen A. E-Health, Telehealth, and Telemedicine: a guide to startup and success. 1st Edition, John Wiley & Sons: Uk, 2002 [DOI:10.1111/j.1945-1474.2002.tb00449.x]
4. Tan Y. Feeling Blue? Go Online: An Empirical Study of Social Support Among Patients. Information Systems Research 2014; 25: 690-709 [DOI:10.1287/isre.2014.0538]
5. Hannan TJ, Celia C. Are doctors the structural weakness in the health building? Internal Medicine Journal 2013; 43:1155-64 [DOI:10.1111/imj.12270]
6. Anderson JG, Balas EA. Computerization of primary care in the United States. International Journal of Healthcare Information Systems and Informatics (IJHISI) 2006 1; 1:1-23 [DOI:10.4018/jhisi.2006070101]
7. Terry AL, Thorpe CF, Giles G, Brown JB, Harris SB, Reid GJ, Thind A, Stewart M. Implementing electronic health records: Key factors in primary care. Canadian Family Physician 2008 1; 54:730-6
8. Wager KA, Lee FW, Glaser JP. Health care information systems: a practical approach for health care management. 1st Edition, John Wiley & Sons: UK, 2017
9. Backer TE. Reviewing the behavioral science knowledge base on technology transfer. 1st Edition, United States Government Printing: USA, 1995 [DOI:10.1037/e495742006-001]
10. Kim HW, Kankanhalli A. Investigating user resistance to information systems implementation: A status quo bias perspective. MIS quarterly 2009 1:567-82 [DOI:10.2307/20650309]
11. Poon EG, Blumenthal D, Jaggi T, Honour MM, Bates DW, Kaushal R. Overcoming barriers to adopting and implementing computerized physician order entry systems in US hospitals. Health Affairs 2004; 23:184-90 [DOI:10.1377/hlthaff.23.4.184]
12. Nair SV. Benefits and security of electronic health record (EHR) use by pediatric staff: a technology acceptance model (TAM)-based quantitative study [Thesis]. USA: Capella University; 2011
13. Wilkins MA. Factors influencing acceptance of electronic health records in hospitals. Online Research Journal Perspective in Health Information Management 2009; 6: 1
14. Alanazy S. Factors associated with implementation of electronic health records in Saudi Arabias [Theseis]. HKU: The University of Hong Kong 2006.
15. Morton ME. Use and acceptance of an electronic health record: factors affecting physician attitudes [Thesis]. USA: Drexel University 2008
16. Venkatesh V, Thong JY, Xu X. Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS quarterly 2012; 1:157-78 [DOI:10.2307/41410412]
17. Venkatesh V, Morris MG, Davis GB, Davis FD. User acceptance of information technology: Toward a unified view. MIS quarterly 2003; 1:425-78 [DOI:10.2307/30036540]
18. Herrero Á, San Martín H. Explaining the adoption of social networks sites for sharing user-generated content: A revision of the UTAUT2. Computers in Human Behavior 2017; 71:209-17 [DOI:10.1016/j.chb.2017.02.007]
19. Slade EL, Williams MD, Dwivedi Y. An extension of the UTAUT 2 in a healthcare context. In UK AIS 2013; 19: 55
20. Hsu CL, Lin JC. An empirical examination of consumer adoption of Internet of Things services: Network externalities and concern for information privacy perspectives. Computers in Human Behavior 2016; 62:516-27 [DOI:10.1016/j.chb.2016.04.023]
21. Kim S, Kim S. User preference for an IoT healthcare application for lifestyle disease management. Telecommunications Policy 2018; 42:304-14 [DOI:10.1016/j.telpol.2017.03.006]
22. Götz O, Liehr-Gobbers K, Krafft M. Evaluation of structural equation models using the partial least squares (PLS) approach. In Handbook of partial least squares. 1st Edition, Springer: Berlin, Heidelberg, 2010 [DOI:10.1007/978-3-540-32827-8_30]
23. Mohsenin S, Esfidani MR. Structural Equation Modeling with the partial least squares (PLS) approach using the software Smart PLS. 1st Edition, Institute Ketone Mehraban Publication: Tehran, 2015 [InPersian]
24. Fornell C, Larcker DF. Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research 1981; 18:39-50 [DOI:10.1177/002224378101800104]
25. Ghazi Tabatabaee M. Lisrel methods, and describes the structure and logic underlying the analysis methods, Covariance structure models or LISREL in social scince. Journal of Litrature Faculty of Tabriz University 1995:2:3 [InPersian]
26. Werts CE, Linn RL, Jöreskog KG. Intraclass reliability estimates: Testing structural assumptions. Educational and Psychological Measurement 1974; 34:25-33 [DOI:10.1177/001316447403400104]
27. Vinzi VE, Trinchera L, Amato S. PLS path modeling: from foundations to recent developments and open issues for model assessment and improvement. 1st Edition, In Hand book of partial least squares: Springer: Berlin, Heidelberg, 2010 [DOI:10.1007/978-3-540-32827-8_3]
28. Fornell C, Larcker DF. Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research 1981; 18:39-50 [DOI:10.1177/002224378101800104]
29. Magner N, Welker RB, Campbell TL. Testing a model of cognitive budgetary participation processes in a latent variable structural equations' framework. Accounting and Business Research 1996; 27:41-50 [DOI:10.1080/00014788.1996.9729530]
30. Chin, Wynne W. "Commentary: Issues and Opinion on Structural Equation Modeling 1998: 7-8
31. Henseler J, Ringle CM, Sinkovics RR. The use of partial least squares path modeling in international marketing. In New challenges to international marketing 2009 Mar 6. Emerald Group Publishing Limited. [DOI:10.1108/S1474-7979(2009)0000020014]
32. M. Tenenhaus, S. Amato, V. Esposito Vinzi, A global goodness-of-fit index for PLS structural equation modelling, in: Proceedings of the XLII SIS Scientific Meeting 2004; 14: 739-742
33. Ringle CM. Segmentation for path models and unobserved heterogeneity: The finite mixture partial least squares approach. University of Hamburg research paper on marketing and retailing 2006 Nov. Available at SSRN: https://ssrn.com/abstract=1586309or http://dx.doi.org/10.2139/ssrn.1586309. [DOI:10.2139/ssrn.1586309]
34. Suriya Begum, M. Computing, Comparison of various techniques in IoT for health care system. International Journal of Computer Science and Mobile Computing 2016; 5:59-66
35. V.J.I.D. Bhatiasevi, An extended UTAUT model to explain the adoption of mobile banking. Information Development 2016; 322: 799-814 [DOI:10.1177/0266666915570764]
36. Suriya Begum V. Comparison of various techniques in IOT for healthcare system. International Journal of Computer Science and Mobile Computing. 2016; 5: 59-66
37. Alpay LL, Henkemans OB, Otten W, Rovekamp TAJM, Dumay ACM. E-health Applications and Services for Patient Empowerment: Directions for Best Practices in The Netherlands. Telemedicine and e-Health 2010; 16:787-91 [DOI:10.1089/tmj.2009.0156]
38. Arsand E, Demiris G. User-centered methods for designing patient-centric self-help tools. Informatics for Health & Social Care 2008; 33:158-169 [DOI:10.1080/17538150802457562]
39. Keselman A, Logan R, Smith CA, Leroy G, Zeng-Treitler Q. Developing Informatics Tools and Strategies for Consumer-centered Health Communication. Journal of the American Medical Informatics Association 2008; 15:473-483 [DOI:10.1197/jamia.M2744]
40. Bhatiasevi V. An extended UTAUT model to explain the adoption of mobile banking. Information Development 2016; 32:799-814 [DOI:10.1177/0266666915570764]
41. Wang X. Using attitude functions, self-efficacy, and norms to predict attitudes and intentions to use mobile devices to access social media during sporting event attendance. Mobile Media & Communication 2015; 3:75-90 [DOI:10.1177/2050157914548932]
42. Sun Y, Liu L, Peng X, Dong Y, Barnes SJ. Understanding Chinese users' continuance intention toward online social networks: an integrative theoretical model. Electronic Markets 2014; 24:57-66 [DOI:10.1007/s12525-013-0131-9]
43. Park J, Yang S, Lehto X. Adoption of mobile technologies for Chinese consumers. Journal of Electronic Commerce Research 2007; 8:3
44. Wills MJ, El-Gayar OF, Bennett D. Examining healthcare professionals' acceptance of electronic medical records using UTAUT. Issues in Information Systems 2008; 9:396-401
45. Moores TT. Towards an integrated model of IT acceptance in healthcare. Decision Support Systems 2012; 53:507-16 [DOI:10.1016/j.dss.2012.04.014]
46. Wang X, White L, Chen X, Gao Y, Li H, Luo Y. An empirical study of wearable technology acceptance in healthcare. Industrial Management & Data Systems 2015; 9: 1704-1723 [DOI:10.1108/IMDS-03-2015-0087]
47. Kalankesh L, Weatherall J, Ba-Dhfari T, Buchan IE, Brass A. Taming EHR data: using semantic similarity to reduce dimensionality. Medical Information for Patients 2013; 1:52-56
48. Aarts H, Verplanken B, Van Knippenberg A. Predicting behavior from actions in the past: Repeated decision making or a matter of habit? Journal of Applied Social Psychology 1998; 28:1355-74 [DOI:10.1111/j.1559-1816.1998.tb01681.x]
49. Webb TL, Sheeran P, Luszczynska A. Planning to break unwanted habits: Habit strength moderates implementation intention effects on behavior change. British Journal of Social Psychology 2009; 48:507-23 [DOI:10.1348/014466608X370591]
50. Wang HY, Wang SH. Predicting mobile hotel reservation adoption: Insight from a perceived value standpoint. International Journal of Hospitality Management 2010; 29:598-608 [DOI:10.1016/j.ijhm.2009.11.001]
51. Chang EC, Tseng YF. Research notes: E-store image, perceived value and perceived risk. Journal of Business Research 2013; 66:864-70 [DOI:10.1016/j.jbusres.2011.06.012]
52. Soltani I, Gharbi JE. Determinants and consequences of the website perceived value. The Journal of Internet Banking and Commerce 1970; 13:1-3
53. Zhao L, Lu Y, Zhang L, Chau PY. Assessing the effects of service quality and justice on customer satisfaction and the continuance intention of mobile value-added services: An empirical test of a multidimensional model. Decision Support Systems 2012; 52:645-56 [DOI:10.1016/j.dss.2011.10.022]
54. Kuo YF, Wu CM, Deng WJ. The relationships among service quality, perceived value, customer satisfaction, and post-purchase intention in mobile value-added services. Computers in Human Behavior 2009; 25:887-96 [DOI:10.1016/j.chb.2009.03.003]
55. Venkatesh V, Bala H. Technology acceptance model 3 and a research agenda on interventions. Decision Sciences 2008; 39:273-315 [DOI:10.1111/j.1540-5915.2008.00192.x]
56. 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]
57. Kim SS, Malhotra NK. A longitudinal model of continued IS use: An integrative view of four mechanisms underlying postadoption phenomena. Management Science 2005; 51:741-55 [DOI:10.1287/mnsc.1040.0326]
58. Ratchford BT, Talukdar D, Lee MS. A model of consumer choice of the Internet as an information source. International Journal of Electronic Commerce 2001; 5:7-21 [DOI:10.1080/10864415.2001.11044217]
59. Kim HW, Chan HC, Gupta S. Value-based adoption of mobile internet: an empirical investigation. Decision Support Systems 2007; 43:111-26 [DOI:10.1016/j.dss.2005.05.009]
60. Mallat N. Exploring consumer adoption of mobile payments - A qualitative study. The Journal of Strategic Information Systems 2007; 16:413-32 [DOI:10.1016/j.jsis.2007.08.001]
61. Dwivedi YK, Shareef MA, Simintiras AC, Lal B, Weerakkody V. A behavioral adoption model for services: A cross-country comparison of mobile health (m-health). Government Information Quarterly 2016; 33:174-87 [DOI:10.1016/j.giq.2015.06.003]
62. El-Wajeeh M, Galal-Edeen GH, Mokhtar H. Cloud computing for mobile health: Opportunities and Challenges 2016;3: 09-14
63. Kossman SP, Scheidenhelm SL. Nurses' perceptions of the impact of electronic health records on work and patient outcomes. CIN: Computers, Informatics, Nursing 2008 1;26:69-77 [DOI:10.1097/01.NCN.0000304775.40531.67]
64. Detmer WM, Friedman CP. Academic physicians' assessment of the effects of computers on health care. Annual Symposium on Computer Application in Medical Care 1994; 1: 558-62
65. Walter Z, Lopez MS. Physician acceptance of information technologies: Role of perceived threat to professional autonomy. Decision Support Systems 2008; 46:206-15 [DOI:10.1016/j.dss.2008.06.004]
66. Sarbaz M. Health information security. Fourth regional electronic health conference. Eastern Mediterranean 2004; 71:7-9
67. Hajavi A, Sarbaz M, Moradi N. Medical Records 3&4. 1st Edition, Jahanrayane: Tehran, 2003
68. Hillestad R, Bigelow J, Bower A, Girosi F, Meili R, Scoville R, Taylor R. Can electronic medical record systems transform health care? Potential health benefits, savings, and costs. Health affairs 2005; 24:1103-17 [DOI:10.1377/hlthaff.24.5.1103]
69. Piry Z. Determinants of the acceptance and development of HER implementation. Fourth reginal electronic health conference. Eastern Mediterranean 2004; 15: 7-9
70. Safdari R, Rorabi M. Electronic Health Records. 1st Edition, Behineh: Teharan, 2005
71. Neumann M, Edelhäuser F, Tauschel D, Fischer MR, Wirtz M, Woopen C, Haramati A, Scheffer C. Empathy decline and its reasons: a systematic review of studies with medical students and residents. Academic Medicine 2011; 86:996-1009 [DOI:10.1097/ACM.0b013e318221e615]
72. Lynch DJ, McGrady AV, Nagel RW, Wahl EF. The patient-physician relationship and medical utilization. Primary care companion to the Journal of Clinical Psychiatry 2007; 9:266 [DOI:10.4088/PCC.v09n0403]
73. Hsu J, Huang J, Fung V, Robertson N, Jimison H, Frankel R. Health information technology and physician-patient interactions: impact of computers on communication during outpatient primary care visits. Journal of the American Medical Informatics Association 2005; 12:474-80 [DOI:10.1197/jamia.M1741]
74. Abdekhoda M, Ahmadi M, Gohari M, Noruzi A. The effects of organizational contextual factors on physicians' attitude toward adoption of Electronic Medical Records. Journal of Biomedical Informatics 2015; 53:174-9 [DOI:10.1016/j.jbi.2014.10.008]
| Rights and Permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |