# Evaluation of AI-Assisted Telemedicine Service Using a Mobile Pet Application
## 3 Methods
### Operational Definition and Pre-Processing  
We performed pre-processed and determined basic demographic information through the survey data and performed the hypothesis establishment and verification sequentially. We conducted a quantitative test using SAS University edition (100 SAS Campus Drive Cary, NC 27513-2414, USA), a free-source statistical package with Oracle Virtual-Box, a virtual machine (500 Oracle Parkway Redwood Shores, CA, 94065, USA), specifically used for testing the basic hypotheses. We also used partial least squares (PLS), one of the distribution-based techniques, because we sought to explain the relationship between each construct. PLS is relatively independent of the assumptions about the normal distribution of variables and residuals and mainly uses a method of minimizing measurement errors to increase the accuracy of path coefficients. This process is an attempt to reproduce the \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Category** & **Frequency** & **\%** \\ \hline \multirow{3}{*}{Gender} & Female & 1643 & 57.5 \\ \cline{2-4}  & Male & 1213 & 42.5 \\ \hline \multirow{4}{*}{Age} & Under 30\({}^{\prime}\)s & 1098 & 38.4 \\ \cline{2-4}  & 30–40\({}^{\prime}\)s & 786 & 27.5 \\ \cline{2-4}  & 40–50\({}^{\prime}\)s & 563 & 19.7 \\ \cline{2-4}  & 50–60\({}^{\prime}\)s & 282 & 9.9 \\ \cline{2-4}  & Over 60\({}^{\prime}\)s & 127 & 4.4 \\ \hline \multirow{4}{*}{Adoption Period} & Under 1 year & 439 & 15.4 \\ \cline{2-4}  & 1–3 years & 665 & 23.3 \\ \cline{2-4}  & 3–5 years & 618 & 21.6 \\ \cline{2-4}  & 5–7 years & 653 & 22.9 \\ \cline{2-4}  & Over 7 years & 481 & 16.8 \\ \hline \multirow{2}{*}{Health Condition} & Good or no problem & 1782 & 62.4 \\ \cline{2-4}  & Need medical Treatment & 1074 & 37.6 \\ \hline \multirow{2}{*}{Total} & Total & 2856 & 100.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Descriptive data summary. observed covariance matrix using the maximum likelihood function [55]. We estimated the model using Smart-PLS 3.0 for the robustness analysis, an additional hypotheses test. The moderation effect was measured with the Z-score obtained through [PERSON]'s equation and the Sobel test [56]. In order to analyze the reliability, convergent validity, and discriminant validity of measurement items (constructs), we analyzed all loading values related to the variables of the measurement model; items that did not satisfy 5% of the significance criteria were excluded from the analysis targets [57]. First, as shown in Table 5, we applied a factor loading criterion of 0.6 or higher for each questionnaire to secure the reliability of the measurement items [58]. It can be argued that the reliability of individual items was thus guaranteed because all items met the criteria. Second, we measured the composite reliability (CR) and Cronbach's alpha values of each construct. As a result, all critical values were above 0.7 and the average variance extracted (AVE) was also above 0.5; thus, the validity of internal consistency was secured. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  
Third, the criterion for accepting discriminant validity is that the square root of a latent variable should generally be greater than the correlation with other latent variables ([PERSON] and [PERSON], 2018). As shown in Table 6, the square root of AVE, which is the diagonal value of each recursive variable located on the diagonal, was higher than the correlation value of the same row or column; thus, the discrimination validity was secured. On the other hand, the correlation between all variables was less than 0.8; therefore, there was seemingly no problem of multicollinearity between variables ([PERSON] and [PERSON], 2018). Moreover, with regard to the multicollinearity, the value of the variance inflation factor (VIF) was found to be less than 1.3, which can be interpreted as having no problem of multicollinearity between the latent variables.