## 4 Results  
In PLS-SEM, outer loadings refer to the relationships between items and their factor. Cross loadings refer to the relationships between observed items and factors other than the ones they measure. In Figure 2 and Table 2, we can see the values for outer loadings (INT4 is 0.685, all the other values are between 0.714 and 0.909--the items' reliability), and in Table 2 the values for cross loading. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline  & **FIT** & **CUI** & **PHV** & **IMM** & **INT** & **PUV** & **SV** & **Cronbach’s Alpha** & **rho\_a** & **CR** & **AVE** \\ \hline  & & & & & & & FIT & 0.894 & 0.894 & 0.922 & 0.703 \\ FIT1 & 0.874 & 0.637 & 0.605 & 0.642 & 0.515 & 0.555 & 0.518 & & & & \\ FIT2 & 0.806 & 0.645 & 0.650 & 0.569 & 0.634 & 0.674 & 0.395 & & & & \\ FIT3 & 0.824 & 0.589 & 0.576 & 0.602 & 0.514 & 0.643 & 0.506 & & & & \\ FIT4 & 0.862 & 0.681 & 0.541 & 0.629 & 0.501 & 0.600 & 0.562 & & & & \\ FIT5 & 0.824 & 0.612 & 0.632 & 0.524 & 0.497 & 0.667 & 0.483 & CUI & 0.916 & 0.917 & 0.933 & 0.665 \\ CUI1 & 0.679 & 0.857 & 0.617 & 0.658 & 0.459 & 0.657 & 0.622 & & & & \\ CUI2 & 0.572 & 0.832 & 0.541 & 0.635 & 0.294 & 0.549 & 0.534 & & & & \\ CUI3 & 0.621 & 0.813 & 0.492 & 0.652 & 0.318 & 0.523 & 0.548 & & & & \\ CUI4 & 0.632 & 0.846 & 0.566 & 0.614 & 0.375 & 0.546 & 0.666 & & & & \\ CUI5 & 0.647 & 0.747 & 0.594 & 0.559 & 0.469 & 0.638 & 0.543 & & & & \\ CUI6 & 0.577 & 0.809 & 0.515 & 0.668 & 0.351 & 0.557 & 0.640 & & & & \\ CUI7 & 0.577 & 0.799 & 0.574 & 0.627 & 0.432 & 0.625 & 0.603 & & & & \\ PHV1 & 0.721 & 0.664 & 0.900 & 0.707 & 0.590 & 0.661 & 0.536 & & & & \\ PHV2 & 0.621 & 0.584 & 0.909 & 0.607 & 0.589 & 0.704 & 0.409 & & & & \\ PHV3 & 0.555 & 0.564 & 0.842 & 0.666 & 0.510 & 0.645 & 0.487 & & & & \\ IMM1 & 0.524 & 0.570 & 0.655 & 0.849 & 0.416 & 0.539 & 0.622 & IMM & 0.890 & 0.892 & 0.917 & 0.650 \\ \hline \end{tabular}
\end{table} 
Table 2: Results of the model and cross loadings. Figure 2: PLS-SEM results. We examined the reliability with Cronbach's alpha and composite reliability, and all constructs showed high internal consistency with values between 0.844 and 0.933. The average variance extracted (AVE) was used to assess convergent validity, and all constructs exceeded the minimum threshold of 0.5, indicating good convergent validity (Table 2). We conducted a discriminant validity assessment by comparing the AVE values to the squared correlations (Table 3). In Table 4, the heterotrait-monotrait ratio (HTMT) matrix values are less than 0.850, confirming the discriminant validity [70]. Usual assessment measures include R\({}^{2}\), the determination coefficient; the variance inflation factor (VIF); the statistical significance and relevance of path coefficients; and the predictive relevance (Q\({}^{2}\)). The VIFs are between 1.522 and 4.808--obtained through multicollinearity examination. The R\({}^{2}\) value is 0.743 for the CUI; it is 0.702 for PUV; it is 0.619 for IMM; it is 0.587 for SV; it is 0.570 for PHV; and it is 0.406 for FIT (Figure 2). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **FIT** & **CUI** & **PHV** & **IMM** & **INT** & **PUV** & **SV** \\ \hline FIT & 0.834 & & & & & & & \\ PHY & 0.769 & 0.814 & & & & & & \\ IMM & 0.837 & 0.796 & 0.839 & & & & & \\ INT & 0.536 & 0.720 & 0.741 & 0.575 & & & & \\ PUV & 0.816 & 0.842 & 0.846 & 0.786 & 0.808 & & & \\ SV & 0.805 & 0.655 & 0.613 & 0.835 & 0.379 & 0.566 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Discriminant validity—HTMT matrix.