## 4 Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & **FIT** & **CUI** & **PHV** & **IMM** & **INT** & **PVV** & **Covnbach’s Alpha** & **rho\_a** & **CR** & **AVE** \\ \hline FIT & 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 & & & \\ 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 & & & \\ PHY1 & 0.721 & 0.664 & 0.900 & 0.707 & 0.590 & 0.661 & 0.536 & & & \\ PHY2 & 0.621 & 0.584 & 0.909 & 0.607 & 0.589 & 0.704 & 0.409 & & & \\ PHY3 & 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 \hline \end{tabular}
\end{table} 
Table 2: _Cont._ \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **FIT** & **CUI** & **PHV** & **IMM** & **INT** & **PUV** & **SV** \\ \hline FIT & 0.839 & & & & & & \\ CUI & 0.756 & 0.815 & & & & & \\ PHY & 0.718 & 0.685 & 0.884 & & & & \\ IMM & 0.708 & 0.774 & 0.747 & 0.806 & & & \\ INT & 0.637 & 0.475 & 0.638 & 0.497 & 0.801 & & \\ PUV & 0.750 & 0.719 & 0.757 & 0.679 & 0.692 & 0.785 & \\ SV & 0.586 & 0.730 & 0.541 & 0.766 & 0.332 & 0.495 & 0.833 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Cornell-Larker standard study for investigating discriminant validity. SV has the most considerable direct influence on CUI (0.320), followed by FIT (0.256), PUV (0.247), and IMM (0.180). FIT has the most considerable total (direct and indirect) influence on CUI (0.643), followed by INT (0.565), IMM (0.458), SV (0.320), PHV (0.297), and PUV (0.247). IMM has the most considerable influence on SV (0.766), followed by FIT (0.469), INT (0.414), and PHV (0.378). FIT has the most considerable influence on IMM (0.612), followed by INT (0.540) and PHV (0.493). INT has the most considerable influence on PUV (0.698), followed by FIT (0.514), PHV (0.353), and IMM (0.135). INT has a positive influence on FIT (0.637). INT has the most considerable influence on PHV (0.638), followed by FIT (0.525) (see Table 5). With 5000 iterations, we performed in SmartPLS a bootstrapping technique to explore the R\({}^{2}\) statistical implication. All hypotheses are supported--the values in Table 6 are statistically significant. In Table 6, we present the path coefficients (all are greater than 0.100, which means that they are significant), t statistics (all values are greater than 1.96), \(p\) values (all values are less than 0.05), and the remarks on each hypothesis. In Table 7, we deliver the indirect effects of the factors. In Table 7, we present the path coefficients (all are greater than 0.100, which means that they are significant), t statistics (all values are greater than 1.96), and \(p\) values (all values are less than 0.05). These values from Table 7 show that the indirect effects are significant.