EMPIRICAL STUDY ON USER ACCEPTANCE TESTING E-HEALTH SERVICES ACROSS DELHI - NCR

Authors

  • RAHUL SHARMA

Keywords:

e-Health, user acceptance testing, empirical study, technology acceptance model

Abstract

This study focused on the traits pertaining to user acceptance behavior among the patients towards the e-Health services provided by the public and private healthcare institutions across Delhi - NCR.The objective of the study was to analyze the impact of Perceived Usefulness, Output Quality, Results Demonstrability, Ease of Use, Subjective Norm, Attitude and Perceived Accessibility on Intention to Use, Credibility and Attitude. Compared to previous researches, the findings of this study exhibited that citizens’ intention to use e-Health is basically determined by their attitude toward utilizing e-health. It implies that the propensity to use the e-Health services would be higher among patrons who shall bear a more positive attitude towards e-Health services as compared to a person who has contrarian attitude towards e-Health services. The results of the research also affirmed that though a person might have positive outlook towards e-Health services, however the person shall refrain from patronizing the e-Health services if the e-Health services are not easily accessible to the prospect, thus access being one of the key dimensions in the dynamics. The findings of this research also revealed that the perceived accessibility, which was being used as metric for access for this work being connected to the accessibility to the internet for the citizens and the moderation tests unveiled that access being an issue among the elderly personnel along with those citizens who didn’t have privilege to internet access.

References

Ahadzadeh, A.S., Pahlevan, S.S., Ong, F.S., and Khong, K.W. (2015). Integrating health belief model and technology acceptance model: an investigation of health-related internet use. J. Med. Int. Res., 17(2):e45. doi: 10.2196/jmir.3564.

Alam, M.Z., Hu, W., and Barua, Z. (2018). Using the UTAUT Model to Determine Factors Affecting Acceptance and Use of Mobile Health (mHealth) Services in Bangladesh. J. Stud. Soc. Sci., 17(2):137-172.

Aldosari, B., Al-Mansour, S., Aldosari, H., and Alanazi, A. (2018). Assessment of factors influencing nurses acceptance of electronic medical record in a Saudi Arabia hospital. Inform. Med. Unlock., 10:82-88. doi: 10.1016/j.imu.2017.12.007.

Carlisle, K., Warren, R., Schuffham, P., and Cheffins, T. (2012). Randomised controlled trial of an in-home monitoring intervention to improve health outcomes of type 2 diabetes: study protocol. Stud. Health Technol. Inform., 182:43-51.

Cimperman, M., Makovec, B.M., and Trkman, P. (2016). Analyzing older users' home telehealth services acceptance behavior-applying an Extended UTAUT model. Int. J. Med. Inform., 90:22-31. doi: 10.1016/j.ijmedinf.2016.03.002.

Davis, F.D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly., 13(3):319-340.

Davis, F.D. and V. Venkatesh. (1996). A critical assessment of potential measurement biases in the technology acceptance model: Three experiments Internet. J. Human-Comput. Stud., 45:19-45.

Davis, F.D., Bagozzi, R.P., and Warshaw, P.R. (1989). User acceptance of computer technology: a comparison of two theoretical models. Manage. Sci., 35(8):982-1,003. doi: 10.1287/mnsc.35.8.982.

Deepthi, P., Kruse, C.S., Soma, M., Nemali, N.T., and Brooks, M. (2017). The effectiveness of telemedicine in the management of chronic heart disease - a systematic review. Journal of the Royal Society of Medicine., 8(3):1-7.

Ekman, B. (2018). Cost analysis of a digital health care model in sweden. Pharmac. Open., 2(3):347-354. doi: 10.1007/s41669-017-0059-7.

Fisher, S.I. and Howell, A.W. (2004). Beyond user acceptance: An examination of employee reactions to information technology systems. Human Res Manag., 43(2-3):243-258. doi: 10.1002/hrm.20018.

Handayani, P.W., Hidayanto, A.N., and Budi, I. (2017). User acceptance factors of hospital information systems and related technologies: Systematic review. Inform. Health Soc. Care., 22:1-26. doi: 10.1080/17538157.2017.1353999.

Holahan, P.J., Lesselroth, B.J., Adams, K., Wang, K., and Church, V. (2015). Beyond technology acceptance to effective technology use: a parsimonious and actionable model. J. Am. Med. Inform. Assoc., 22(3):718-29. doi: 10.1093/jamia/ocu043.

Holden, R.J. and Karsh, B. (2010). The technology acceptance model: its past and its future in health care. J. Biomed Inform., 43(1):159-72. doi: 10.1016/j.jbi.2009.07.002.

Jayaseelan, R., Koothoor, P., and Pichandy, C. (2020). Technology acceptance by medical doctors in India: an analysis with UTAUT model. Int. J. Sci. Technol. Res., 9(1):3,854-3,857.

Karkonasasi, K., Yu-N, C., and Mousavi, S.A. (2018). Intention to Use SMS Vaccination Reminder and Management System among Health Centers in Malaysia: The Mediating Effect of Attitude. Computers and Society. Available from: arxiv.org/abs/1806. Doi: https://doi.org/10.48550/arXiv.1806.10744.

Koivumäki, T., Pekkarinen, S., Lappi, M., Väisänen, J., Juntunen, J., Pikkarainen, M. (2017). Consumer adoption of future mydata-based preventive ehealth services: an acceptance model and survey study. J. Med. Int. Res., 22,19(12):429. doi: 10.2196/jmir.7821.

Krousel-Wood, M., McCoy, A.B., Ahia, C., Holt, E.W., Trapani, D.N., Luo, Q., Price-Haywood, E.G., Thomas, E.J., Sittig, D.F., and Milani, R.V. (2017). Implementing electronic health records (EHRs): health care provider perceptions before and after transition from a local basic EHR to a commercial comprehensive EHR. J. Am. Med. Inform. Assoc., 25(6):618–626. doi: 10.1093/jamia/ocx094.

Lee, C. and Coughlin, J.F. (2014). Perspective: older adults' adoption of technology: an integrated approach to identifying determinants and barriers. J. Prod. Innov. Manag., 32(5):747-759. https://doi.org/10.1111/jpim.12176.

Legris, P., Ingham, J., and Collerette, P. (2003). Why do people use information technology? A critical review of the technology acceptance model. Inform. Manage., 40(3):191-204. https://doi.org/10.1016/S0378-7206(01)00143-4

Lin, J.C.-C. and Lu, H.P. (2000). Towards an understanding of the behavioral intention to use a web site. Int. J. Inform. Manage., 20(3):197-208. DOI:10.1016/S0268-4012(00)00005-0.

Marangunic, N. and Granic, A. (2014). Technology acceptance model: a literature review from 1986 to 2013. Univ. Access. Inf. Soc., 14(1):81-95. https://doi.org/10.1007/s10209-014-0348-1.

Peek, S.T.M., Wouters, E.J.M., Van, H.J., Luijkx, K.G., Boeije, H.R., and Vrijhoef, H.J.M. (2014). Factors influencing acceptance of technology for aging in place: a systematic review. Int. J. Med. Inform., 83(4):235-248. doi: 10.1016/j.ijmedinf.2014.01.004.

Shadangi, P.Y. and Dash, M. (2019). A conceptual model for telemedicine adoption: an examination of technology acceptance model. Int. J. Recent Technol. Eng., 8(2):1,286.

Venkatesh, V., Morris, M.G., Davis, G.B., and Davis, F.D. (2003). User acceptance of information technology: toward a unified view. MIS Quart., 27(3):425-478. https://doi.org/10.2307/30036540.

Ward, R. (2013). The application of technology acceptance and diffusion of innovation models in healthcare informatics. Health Pol. Technol., 2(4):222-228. https://doi.org/10.1016/j.hlpt.2013.07.002.

Wass, S. (2017). The Importance of eHealth Innovations: Lessons About Patient Accessible Information [Ph.D. thesis]. Jonkoping University, Sweden, 68p. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-38045.

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Published

2023-01-03

How to Cite

SHARMA, R. (2023). EMPIRICAL STUDY ON USER ACCEPTANCE TESTING E-HEALTH SERVICES ACROSS DELHI - NCR. Suranaree Journal of Science and Technology, 29(6), 070061(1–7). retrieved from https://ph04.tci-thaijo.org/index.php/SUJST/article/view/48

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Research Article