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.
Highlights
Partial least squares path modeling (PLS) has been widely used to analyze data associated with complex phenomena [1]
3, We the results of will this reduce data analysis; in Section 4, Using thepresent telemedicine service the Perceived usefulness we offer a discussion of these results; and in the last section, we provide a summary of the psychological burden of people
Our results show that the Theory of Planned Behavior (TPB) model has significant explanatory power, while the Technology Acceptance Model (TAM) model does not
Summary
Partial least squares path modeling (PLS) has been widely used to analyze data associated with complex phenomena [1]. Many enhancements have been incorporated into PLS throughout the years. Among them, it is worth mentioning the following, multigroup analysis [3], identifying and treating unobserved heterogeneity [4], measures of model fit [5], predictive power assessment [6], and consistent PLS (PLSc) [7]. Despite the several enrichments of PLS [8], handling outliers in the context of PLS has been broadly ignored [9]. Johnson and Wichern [10] referred to an outlier as an observation in a dataset that appeared to be inconsistent with the rest of that dataset
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