Journal of Information Systems Engineering and Management

Predicting Telemedicine Adoption: An Empirical Study on the Moderating Effect of Plasticity in Brazilian Patients
Catalina Ramírez-Rivas 1, Jorge Alfaro-Pérez 1, Patricio Ramírez-Correa 1 * , Ari Mariano-Melo 2
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1 Universidad Católica del Norte, Coquimbo, CHILE
2 Universidade de Brasília, Brasília, BRAZIL
* Corresponding Author
Research Article

Journal of Information Systems Engineering and Management, 2021 - Volume 6 Issue 1, Article No: em0135
https://doi.org/10.29333/jisem/9618

Published Online: 20 Jan 2021

Views: 1735 | Downloads: 1070

How to cite this article
APA 6th edition
In-text citation: (Ramírez-Rivas et al., 2021)
Reference: Ramírez-Rivas, C., Alfaro-Pérez, J., Ramírez-Correa, P., & Mariano-Melo, A. (2021). Predicting Telemedicine Adoption: An Empirical Study on the Moderating Effect of Plasticity in Brazilian Patients. Journal of Information Systems Engineering and Management, 6(1), em0135. https://doi.org/10.29333/jisem/9618
Vancouver
In-text citation: (1), (2), (3), etc.
Reference: Ramírez-Rivas C, Alfaro-Pérez J, Ramírez-Correa P, Mariano-Melo A. Predicting Telemedicine Adoption: An Empirical Study on the Moderating Effect of Plasticity in Brazilian Patients. J INFORM SYSTEMS ENG. 2021;6(1):em0135. https://doi.org/10.29333/jisem/9618
AMA 10th edition
In-text citation: (1), (2), (3), etc.
Reference: Ramírez-Rivas C, Alfaro-Pérez J, Ramírez-Correa P, Mariano-Melo A. Predicting Telemedicine Adoption: An Empirical Study on the Moderating Effect of Plasticity in Brazilian Patients. J INFORM SYSTEMS ENG. 2021;6(1), em0135. https://doi.org/10.29333/jisem/9618
Chicago
In-text citation: (Ramírez-Rivas et al., 2021)
Reference: Ramírez-Rivas, Catalina, Jorge Alfaro-Pérez, Patricio Ramírez-Correa, and Ari Mariano-Melo. "Predicting Telemedicine Adoption: An Empirical Study on the Moderating Effect of Plasticity in Brazilian Patients". Journal of Information Systems Engineering and Management 2021 6 no. 1 (2021): em0135. https://doi.org/10.29333/jisem/9618
Harvard
In-text citation: (Ramírez-Rivas et al., 2021)
Reference: Ramírez-Rivas, C., Alfaro-Pérez, J., Ramírez-Correa, P., and Mariano-Melo, A. (2021). Predicting Telemedicine Adoption: An Empirical Study on the Moderating Effect of Plasticity in Brazilian Patients. Journal of Information Systems Engineering and Management, 6(1), em0135. https://doi.org/10.29333/jisem/9618
MLA
In-text citation: (Ramírez-Rivas et al., 2021)
Reference: Ramírez-Rivas, Catalina et al. "Predicting Telemedicine Adoption: An Empirical Study on the Moderating Effect of Plasticity in Brazilian Patients". Journal of Information Systems Engineering and Management, vol. 6, no. 1, 2021, em0135. https://doi.org/10.29333/jisem/9618
ABSTRACT
Predicting behaviors around telemedicine acceptance in developing countries is an important developing area of study. This importance has been enhanced since the emergence of the COVID-19 pandemic, especially in Latin America. In this context, this study aims to explore the effect of plasticity on telemedicine acceptance in Brazil. This paper focuses on Brazilian patients and their acceptance of telemedicine through the Theory of Planned Behavior and the concept of Plasticity as a superordinate factor of the patients’ personality traits. An online survey of Brazilian patients was carried out, and structural model modeling was then utilized. Results indicate that the proposed model explains 62.1% of the behavioral intention of the use of telemedicine by the patients. Furthermore, the findings suggest that attitude has the most substantial direct effect on behavioral intention, with plasticity playing a considerable role in the strength of the impact. Government strategies for the spread of telemedicine in Latin America could consider these results for their design.
KEYWORDS
REFERENCES
  • Adams, J. G. and Walls, R. M. (2020). Supporting the Health Care Workforce during the COVID-19 Global Epidemic. JAMA, 323(15), 1439-1440. https://doi.org/10.1001/jama.2020.3972
  • Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211. https://doi.org/10.1016/0749-5978(91)90020-T
  • CFM. (2019) Telemedicina: CFM regulamenta atendimentos online no Brasil. Available at: http://www.portal.cfm.org.br/index.php?option=com_content&view=article&id=28061
  • Deyoung, C. G., Peterson, J. B. and Higgins, D. M. (2002). Higher-order factors of the Big Five predict conformity: Are there neuroses of health? Personality and Individual Differences, 33(4), 533-552. https://doi.org/10.1016/S0191-8869(01)00171-4
  • Dick, S., O’Connor, Y., Thompson, M. J., O’Donoghue, J., et al. (2020). Considerations for Improved Mobile Health Evaluation: Retrospective Qualitative Investigation. JMIR mHealth and uHealth, 8(1), e12424. https://doi.org/10.2196/12424
  • Fishbein, M. and Ajzen, I. (1975). Belief, attitude, intention and behaviour: An introduction to theory and research. Reading, MA: Addison-Wesley.
  • Gosling, S. D., Rentfrow, P. J. and Swann, W. B. (2003). A very brief measure of the Big-Five personality domains. Journal of Research in Personality, 37(6), 504-528. https://doi.org/10.1016/S0092-6566(03)00046-1
  • Grandón, E. E. and Ramírez-Correa, P. (2018). Managers/owners’ innovativeness and electronic commerce acceptance in chilean smes: A multi-group analysis based on a structural equation model. Journal of Theoretical and Applied Electronic Commerce Research, 13(3), 1-16. https://doi.org/10.4067/S071818762018000300102
  • Harst, L., Lantzsch, H. and Scheibe, M. (2019). Theories predicting end-user acceptance of telemedicine use: Systematic review. Journal of Medical Internet Research, 21(5), e13117. https://doi.org/10.2196/13117
  • Henseler, J., Ringle, C. M. and Sarstedt, M. (2016). Testing measurement invariance of composites using partial least squares. International Marketing Review, 33(3), 405-431. https://doi.org/10.1108/IMR-09-2014-0304
  • Hollander, J. E. and Carr, B. G. (2020). Virtually Perfect? Telemedicine for Covid-19. New England Journal of Medicine, 382(18), 1679-1681. https://doi.org/10.1056/NEJMp2003539
  • Huygens, M. W., Vermeulen, J., Friele, R. D., van Schayck, O. C. P., et al. (2015). Internet Services for Communicating with the General Practice: Barely Noticed and Used by Patients. Interactive Journal of Medical Research, 4(4), e21. https://doi.org/10.2196/ijmr.4245
  • Ipsos Global. (2018). Global Views on Healthcare Summary of Findings Personal Health Perceptions.
  • Jen, W.-Y. and Hung, M.-C. (2010). An Empirical Study of Adopting Mobile Healthcare Service: The Family’s Perspective on the Healthcare Needs of Their Elderly Members. Telemedicine and e-Health, 16(1), 41-48. https://doi.org/10.1089/tmj.2009.0093
  • Lin, S. P. and Yang, H. Y. (2009). Exploring key factors in the choice of e-health using an asthma care mobile service model. Telemedicine and e-Health, 15(9), 884-890. https://doi.org/10.1089/tmj.2009.0047
  • Meesala, A. and Paul, J. (2018). Service quality, consumer satisfaction and loyalty in hospitals: Thinking for the future. Journal of Retailing and Consumer Services, 40, 261-269. https://doi.org/https://doi.org/10.1016/j.jretconser.2016.10.011
  • Ohannessian, R., Duong, T. A. and Odone, A. (2020). Global Telemedicine Implementation and Integration Within Health Systems to Fight the COVID-19 Pandemic: A Call to Action. JMIR Public Health and Surveillance, 6(2), e18810. https://doi.org/10.2196/18810
  • Oliveira, W. K. de, Duarte, E., França, G. V. A. de, Garcia, L. P. (2020). How Brazil can hold back COVID-19. Epidemiologia e servicos de saude: revista do Sistema Unico de Saude do Brasil, 29(2), e2020044. https://doi.org/10.5123/s1679-49742020000200023
  • Painén-Aravena, G., Alfaro-Pérez, J., Ramírez-Correa, P., Grandón, E. E. and Araya-Guzmán, S. (2019). Investigating the effect of learning styles on the acceptance of e-books among university students. In Iberian Conference on Information Systems and Technologies, CISTI. IEEE Computer Society. https://doi.org/10.23919/CISTI.2019.8760702
  • Portnoy, J., Waller, M. and Elliott, T. (2020). Telemedicine in the Era of COVID-19. Journal of Allergy and Clinical Immunology: In Practice, 8(5), 1489-1491. https://doi.org/10.1016/j.jaip.2020.03.008
  • Ramírez-Correa, P. and Ramírez-Santana, M. (2018). Predicting Condom Use among Undergraduate Students Based on the Theory of Planned Behaviour, Coquimbo, Chile, 2016. International Journal of Environmental Research and Public Health, 15(8), 1689. https://doi.org/10.3390/ijerph15081689
  • Ramírez-Rivas, C., Alfaro-Pérez, J., Ramírez-Correa, P. and Mariano-Melo, A. (2020). Telemedicine Acceptance in Brazil: Explaining behavioral intention to move towards internet-based medical consultations. In 2020 15th Iberian Conference on Information Systems and Technologies (CISTI). IEEE, pp. 1-4. https://doi.org/10.23919/CISTI49556.2020.9140996
  • Sarstedt, M., Henseler, J. and Ringle, C. M. (2011). Multigroup analysis in partial least squares (PLS) path modeling: Alternative methods and empirical results. Advances in International Marketing, 22, 195-218. https://doi.org/10.1108/S1474-7979(2011)0000022012
  • Zhang, X., Han, X., Dang, Y., Meng, F., Guo, X. and Lin, J. (2017). User acceptance of mobile health services from users’ perspectives: The role of self-efficacy and response-efficacy in technology acceptance. Informatics for Health and Social Care, 42(2), 194-206. https://doi.org/10.1080/17538157.2016.1200053
  • Zhao, Y., Ni, Q. and Zhou, R. (2018). What factors influence the mobile health service adoption? A meta-analysis and the moderating role of age. International Journal of Information Management, 342-350. https://doi.org/10.1016/j.ijinfomgt.2017.08.006
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