Factors affecting acceptance and adoption of mobile health application (MHA)
DOI:
https://doi.org/10.61841/037w9w79Keywords:
Mobile HealthApplication,, e-HealthcareAbstract
Health systems using modern Technologies i.e. Mobile Health Application (MHAs) have a deep impact on the standards of hospital services and reducing healthcare costs. The use of MHA in a society is directly proportional to the awareness and education of the society. The factors that affect MHA acceptance has been analyzed in this study usingUnified Theory of Acceptance and Use of Technology (UTAUT) framework[1]. The UTAUT model is a new tool for evaluating the integration/adoption of MHAs. We tend to formulate the propensity to using the MHA system and behavioral exercise of healthcare professionals using empirical studies and the use of the UTAUT2 model. Trust of data most important in health sector, themain aim of this research is to check and test the factors which effect the assimilation and acceptance of the MHA by the healthcare providers. The target area of this research is Jordanian hospitals using MHA. The data used in this research is gathered from healthcare professional working in hospitals of Jordan using MHA. The work presented in this research gives a clear view of which elements effect the acceptance and adoption of MHA[2][3].
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References
1. “Factors predicting oncology care providers ’ behavioral intention to adopt CLINICAL DECISION SUPPORT SYSTEMS by Andrew Wolfenden January 2012,” no. January, 2012.
2. Can Peker, “An Analysis of the Main Critical Factors that Affect the Acceptance of Technology in
Hospital management Systems,” no. September, 2010.
3. N. Theera-ampornpunt, “Thai Hospitals’ Adoption of Information Technology: A Theory Development and Nationwide Survey,” no. December, 2011.
4. N. Rahimi and A. Jetter, “Explaining Health Technology Adoption: Past, Present, Future,” Manag. Eng. Technol. (PICMET), 2015 Portl. Int. Conf. on. IEEE, pp. 2465–2495, 2015.
5. M. Williams, “Is UTAUT really used or just cited for the sake of it ? a systematic review of citations of UTAUT ’ s originating article,” 2011.
6. G.-H. Kim, S. Trimi, and J.-H. Chung, “Big-data applications in the government sector,” Commun. ACM, vol. 57, no. 3, pp. 78–85, 2014.
7. J. Li, A. Talaei-Khoei, H. Seale, P. Ray, and C. R. Macintyre, “Health Care Provider Adoption of eHealth: Systematic Literature Review.,” Interact. J. Med. Res., vol. 2, no. 1, p. e7, Jan. 2013.
8. M. Doheir, B. Hussin, A. Samad, H. Basari, and M. B. Alazzam, “Structural Design of Secure Transmission Module for Protecting Patient Data in Cloud-Based Healthcare Environment,” Middle-East
J. Sci. Res., vol. 23, no. 12, pp. 2961–2967, 2015.
9. Y. Mohammad Al-Sharo, G. Shakah, M. Sh Alkhaswneh, B. Zeyad Alju-Naeidi, and M. Bader Alazzam, “Classification of big data: machine learning problems and challenges in network intrusion prediction,” Int. J. Eng. Technol., vol. 7, no. 4, pp. 3865–3869, 2018.
10. S. T. M. Peek, E. J. M. Wouters, J. van Hoof, K. G. Luijkx, H. R. Boeije, and H. J. M. Vrijhoef, “Factors influencing acceptance of technology for aging in place: A systematic review,” Int. J. Med. Inform., vol. 83, no. 4, pp. 235–248, 2014.
11. M. Mital, P. Choudhary, V. Chang, A. Papa, and A. K. Pani, “Adoption of Internet of Things in India: A test of competing models using a structured equation modeling approach,” Technol. Forecast. Soc. Change, pp. 1–8, 2016.
12. I. Journal and S. S. Vol, “The Effect of Self-Efficacy in the Acceptance of Information Technology in the Public Sector Sami Mohamed Saleh Al-Haderi School of Business , Univeristy Utara Malaysia UUM
, sintok 06010 Kedah , Malaysia,” vol. 4, no. 9, pp. 188–198, 2013.
13. M. Neumeier, “Using Kotter ’ s Change Management Theory and Innovation Diffusion Theory In Implementing an Electronic Medical Record Implementing an EMR.”
14. M. L. Heath, “IT Cultural Conflict , Trust Belief in IT Artifacts , and User Involvement : Conceptual Study of Meaningful Use Resistance IT Cultural Conflict , Trust Belief in IT Artifacts , and User Involvement : Conceptual Study of Meaningful Use Resistance,” Mwais, 2015.
15. Huijuan Wu, “EXPLORING HEALTHCARE CONSUMER ACCEPTANCE OF PERSONAL HEALTH INFORMATION MANAGEMENT TECHNOLOGY THROUGH PERSONAL HEALTH RECORD SYSTEMS,” 2013.
16. G. Kenny and R. Connolly, “Citizens’ Health Information Privacy Concerns: A Multifaceted Approach,” no. MAY 2015, pp. 0–11, 2015.
17. T. U. Daim, N. Behkami, N. Basoglu, O. M. Kök, and L. Hogaboam, Healthcare Technology Innovation Adoption. 2016.
18. F. Baharom, O. T. Khorma, H. Mohd, and M. G. Bashayreh, “DEVELOPING AN EXTENDED TECHNOLOGY ACCEPTANCE MODEL : DOCTORS ’ ACCEPTANCE OF ELECTRONIC MEDICAL RECORDS IN JORDAN,” no. 106, pp. 8–9, 2011.
19. M.-P. Gagnon et al., “Electronic health record acceptance by physicians: testing an integrated theoretical model.,” J. Biomed. Inform., vol. 48, pp. 17–27, Apr. 2014.
20. C. a Harle, L. a Gruber, and M. a Dewar, “Factors in medical student beliefs about electronic health record use.,” Perspect. Health Inf. Manag., vol. 11, p. 1f, Jan. 2014.
21. malik bader Alazzam, “Critical Successful Factors Affecting Adoption of E- Health System in Developing Countries,” Test Eng. Manag., vol. 82, pp. 306–316, 2020.
22. A. Karahoca, D. Karahoca, and M. Aksöz, “Examining intention to adopt to internet of things in healthcare technology products,” Kybernetes, vol. 47, no. 4, pp. 742–770, 2018.
23. M. B. Alazzam, “Theories and factors applied in investigating the user acceptance towards personal health records : Review study Theories and factors applied in investigating the user acceptance towards personal health records : Review study,” Int. J. Healthc. Manag., vol. 0, no. 0, pp. 1–8, 2017.
24. A. Touray and A. Salminen, “The Impact of Moderating Factors on Behavioral Intention Towards Internet : a Transnational Perspective,” vol. 02, no. 06, pp. 1035–1041, 2013.
25. S. M.Alazzam, BASARI, “EHRs Acceptance in Jordan Hospitals By UTAUT2 Model: Preliminary Result,” J. Theor. Appl. Inf. Technol., vol. 3178, no. 3, pp. 473–482, 2015.
26. J. Tavares and T. Oliveira, “Electronic Health Record Portal Adoption by Health Care Consumers Proposal of a New Adoption Model,” pp. 387–393, 2013.
27. H. Li, J. Wu, Y. Gao, and Y. Shi, “Examining Individuals’ Adoption of Healthcare Wearable Devices: An Empirical Study from Privacy Calculus Perspective,” Int. J. Med. Inform., vol. 88, no. 555, pp. 8–17, 2016.
28. A. Alaiad and L. Zhou, “The determinants of home healthcare robots adoption: An empirical investigation,” Int. J. Med. Inform., vol. 83, no. 11, pp. 825–840, 2014.
29. M. K. Al-azzam, “MHealth for Decision Making Support : A Case Study of EHealth in the Public Sector,” no. January, 2019.
30. N. Aa, S. J. Econ, B. Manag, R. Razeghi, and A. A. Nasiripour, “An investigation of factors affecting Electronic Health Record ( EHR ) in Health Care Centers,” vol. 1, no. 1, pp. 19–24, 2014.
31. V. Venkatesh, “consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology,” vol. 36, no. 1, pp. 157–178, 2012.
32. M. Rasmi, M. B. Alazzam, M. K. Alsmadi, A. Ibrahim, R. A. Alkhasawneh, and S. Alsmadi, “Healthcare professionals ’ acceptance Electronic Health Records system : Critical literature review ( Jordan case study ) Healthcare professionals ’ acceptance Electronic Health Records system : Critical literature review ( Jordan case study ),” Int. J. Healthc. Manag., vol. 0, no. 0, pp. 1–13, 2018.
33. M. B. Alazzam, Y. M. Al-sharo, and M. K. Al-, “DEVELOPING ( UTAUT 2 ) MODEL OF ADOPTION MOBILE HEALTH APPLICATION IN JORDAN E- GOVERNMENT,” J. Theor. Appl. Inf. Technol.
30th, vol. 96, no. 12, 2018.
34. P.-J. Hsieh, “Physicians’ acceptance of electronic medical records exchange: An extension of the decomposed TPB model with institutional trust and perceived risk.,” Int. J. Med. Inform., vol. 84, no. 1,
pp. 1–14, Jan. 2015.
35. H. Alsaghier, M. Ford, A. Nguyen, and R. Hexel, “Conceptualising Citizen ’ s Trust in e-Government : Application of Q Methodology,” vol. 7, no. 4, pp. 295–310, 2009.
36. A. A. Alalwan, A. M. Baabdullah, N. P. Rana, K. Tamilmani, and Y. K. Dwivedi, “Examining adoption
of mobile internet in Saudi Arabia: Extending TAM with perceived enjoyment, innovativeness and trust,” Technol. Soc., vol. 55, pp. 100–110, 2018.
37. M. N. Herian, N. C. Shank, and T. L. Abdel-Monem, “Trust in government and support for governmental regulation: the case of electronic health records.,” Health Expect., vol. 17, no. 6, pp. 784–94, Dec. 2014.
38. T. Wang et al., “Trust and risk in e-government adoption,” Public Adm. Rev., vol. 17, pp. 153–158, 2005.
39. L. J. Wekre, L. Melby, and A. Grimsmo, “Early experiences with the multidose drug dispensing system-
-a matter of trust?,” Scand. J. Prim. Health Care, vol. 29, no. 1, pp. 45–50, Mar. 2011.
40. F. Yang and C. Hankin, “Secondary use of data in EHR systems,” pp. 1–40, 2012.
41. [ O. G. Ochieng and R. Hosoi, “Factors influencing diffusion of electronic medical records : a case study in three healthcare institutions in Japan,” vol. 34, no. 4, pp. 120–129, 2005.
42. M. B. Alazzam, A. Samad, H. Basari, and A. S. Sibghatullah, “Trust in stored data in EHRs acceptance of medical staff : using UTAUT2,” Int. J. Appl. Eng. Res., vol. 11, no. 4, pp. 2737–2748, 2016.
43. R. Ho, Handbook of univariate and multivariate data analysis and interpretation with SPSS. 2006.
44. Hair, Multivariate data analysis, vol. (sixth ed. 2006.
45. A. Maiorana et al., “Trust, confidentiality, and the acceptability of sharing HIV-related patient data: lessons learned from a mixed methods study about Health Information Exchanges.,” Implement. Sci., vol. 7, p. 34, Jan. 2012.
46. J. Mou and J. Cohen, “TRUST IN ELECTRONIC-SERVICE PROVIDERS : A META- ANALYSIS OF,” 2014.
47. K. Bhavya sri (2018) a brief overview of the very rare fibrodysplasia ossificans progressiva (stone man syndrome). Journal of Critical Reviews, 5 (1), 19-3. doi:10.22159/jcr.2018v5i1.22809
48. Conte, E. On some explanations and analysis of the basic foundations of quantum cognition: Comments on a paper by pothos, busemeyer and trueblood (2015) NeuroQuantology, 13 (3), pp. 371-383.
49. Georgiev, D. No-go theorem for stapp’s quantum zeno model of mind-brain interaction (2015) NeuroQuantology, 13 (2), pp. 179-189.
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