Title:	Telemedicine acceptance during the Covid-19 pandemic : an empirical example of robust consistent partial least squares path modeling
Authors:	Ramírez Correa, Patricio Ramírez Rivas, Catalina Alfaro Pérez, Jorge Mariano, Ari Melo
metadata.dc.identifier.orcid:	https://orcid.org/0000-0001-7089-1505 https://orcid.org/0000-0002-2843-7816 https://orcid.org/0000-0002-7987-5015
Assunto::	Telemedicina Aceitação de tecnologia Mínimos quadrados Covid-19
Issue Date:	2020
Publisher:	MDPI
Citation:	RAMÍREZ-CORREA, Patricio et al. Telemedicine acceptance during the Covid-19 pandemic: an empirical example of robust consistent partial least squares path modeling. Symmetry, v. 12, n. 10, 1593, 2020. DOI: https://doi.org/10.3390/sym12101593. Disponível em: https://www.mdpi.com/2073-8994/12/10/1593. Acesso em: 01 dez. 2020.
Abstract:	The explanation of behaviors concerning telemedicine acceptance is an evolving area of study. This topic is currently more critical than ever, given that the COVID-19 pandemic is making resources scarcer within the health industry. The objective of this study is to determine which model, the Theory of Planned Behavior or the Technology Acceptance Model, provides greater explanatory power for the adoption of telemedicine addressing outlier-associated bias. We carried out an online survey of patients. The data obtained through the survey were analyzed using both consistent partial least squares path modeling (PLSc) and robust PLSc. The latter used a robust estimator designed for elliptically symmetric unimodal distribution. Both estimation techniques led to similar results, without inconsistencies in interpretation. In short, the results indicate that the Theory of Planned Behavior Model provides a significant explanatory power. Furthermore, the findings show that attitude has the most substantial direct effect on behavioral intention to use telemedicine systems.
Licença::	© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
DOI:	https://doi.org/10.3390/sym12101593
Appears in Collections:	Artigos publicados em periódicos e afins UnB - Covid-19

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