## 5 Results
### Evaluating the Reflective Measurement Model  
Assessments of reflective measurement models consist of three main steps: internal consistency reliability, convergent validity, and discriminant validity. First, according to [PERSON] et al. [45], the measurement model has good reliability when the factor loadings of each indicator are greater than 0.7, Cronbach's alpha is greater than 0.8, and composite reliability (CR) is greater than 0.7. The average variance extracted (AVE) is a criterion to test the convergent validity, and an AVE greater than 0.5 can indicate that the model has good convergent validity [46]. Discriminant validity refers to the absence of correlation between a construct and other constructs in the model. There are three means of assessing discriminant validity in PLS-SEM, namely, Cornell-Larcker criterion, Cross loadings, and the Heterotrait-monotrait ratio (HTMT). According to the Cornell-Larcker criterion, the AVE of each latent variable should be greater than the highest squared correlation of that latent variable with any other latent variable [46]. Cross loadings require that each metric has a higher loading on its assigned construct than any other, and that each construct has the highest loading on its metric to infer that the model's constructs are sufficiently different from each other [47]. HTMT is a criterion for discriminant validity which is specific to the PLS-SEM; a model is considered to have good discriminant validity when the HTMT is less than 0.9. The factor loadings, Cronbach's alpha, CR, and AVE of the reflective measurement model constructed in this study are shown in Table 3, while the Cornell-Larcker criterion, Cross loadings, and HTMT are presented in Tables 4-6. According to Table 3, due to the factor loadings of each indicator being greater than 0.7, Cronbach's alpha being greater than 0.8, the CR value being greater than 0.7, and AVE being greater than 0.5, this reflective measurement model has good internal consistency, reliability, and convergent validity. Table 4 presents the Cornell-Larcker criterion. Each value in the diagonal of the table is greater than the other values in the row and column they are in. Table 5 presents the cross-loadings of the indicators; each metric has a higher loading on its assigned construct than any other (i.e., the INT loadings are higher than the other constructs in the first column). Table 6 demonstrates that the HTMT values for all constructs were less than 0.9. As a result, the reflective measurement model has good discriminant validity. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Categories** & **Frequencies** & **Percentages (\%)** \\ \hline \multirow{8}{*}{Grade} & Freshman & 166 & 26.9 \\  & Sophomore & 119 & 19.3 \\  & Junior & 106 & 17.2 \\  & Senior & 55 & 8.9 \\  & Others & 170 & 27.6 \\  & Humanities and Social & 347 & 56.3 \\  & Science & 347 & 56.3 \\  & Language & 36 & 5.8 \\  & Arts & 23 & 3.7 \\  & Education & 30 & 4.9 \\  & Public Administration & 48 & 7.8 \\  & Law & 76 & 12.3 \\  & Medicine & 4 & 0.6 \\  & Science and Technology & 28 & 4.5 \\  & Others & 24 & 3.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._ \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **INT** & **PE** & **PLT** & **PU** & **Vak-LS** \\ \hline INT & 0.943 & & & & \\ PE & 0.871 & 0.91 & & & \\ PLT & 0.807 & 0.795 & 0.903 & & \\ PU & 0.848 & 0.846 & 0.878 & 0.916 & \\ Vak-LS & 0.631 & 0.623 & 0.761 & 0.684 & 0.891 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Fornell-Larcker criterion.