## 5 Results
### Evaluating the Structural Model and Hypotheses  
The main metrics used to evaluate the structural model are collinearity (VIF), coefficient of determination (R\({}^{2}\) value), and predictive relevance (Q\({}^{2}\) value). The problem of collinearity may exist when the VIF of each indicator is greater than 5. In this study, the VIF of all indicators is less than 5 (Table 7), so there is no collinearity among constructs [48]. To illustrate the explanatory power of latent variables, the R\({}^{2}\) values (0.75, 0.50, and 0.25 are considered substantial, moderate, and weak, respectively) are usually assessed [47]. The R\({}^{2}\) values of the three latent variables in this study are all greater than 0.75 and another is close to 0.5, indicating good explanatory power (Table 8). The Q\({}^{2}\) value is a set of indicators to assess the predictive relevance of a model; a higher Q\({}^{2}\) value (usually suggested to be greater than 0) indicates a higher predictive relevance of the model [49]. The Q\({}^{2}\) value of each latent variable in this study was greater than 0 (Table 8), which can indicate good predictive power. \begin{table}
\begin{tabular}{c c c c c c c} \hline  & **INT** & **PE** & **PLT** & **PU** & **Vak-LS** & **TTT** \\ \hline INT & & & & & & \\ PE & 0.851 & & & & & \\ PLT & 0.865 & 0.855 & & & & \\ PU & 0.894 & 0.847 & 0.872 & & & \\ Vak-LS & 0.696 & 0.691 & 0.848 & 0.757 & & \\ TTT & 0.870 & 0.866 & 0.837 & 0.884 & 0.701 & \\ \hline \end{tabular}
\end{table} 
Table 6: HTMT. \begin{table}
\begin{tabular}{c c c c c c} \hline  & **INT** & **PE** & **PLT** & **PU** & **Vak-LS** & **TTT** \\ \hline INT & & & & & \\ PE & 0.851 & & & & \\ PLT & 0.865 & 0.855 & & & & \\ PU & 0.894 & 0.847 & 0.872 & & & \\ Vak-LS & 0.696 & 0.691 & 0.848 & 0.757 & & \\ TTT & 0.870 & 0.866 & 0.837 & 0.884 & 0.701 & \\ \hline \end{tabular}
\end{table} 
Table 7: VIF value. \begin{table}
\begin{tabular}{c c c c c c} \hline  & **INT** & **PE** & **PLT** & **PU** & **Vak-LS** & **TTT** \\ \hline PU3 & 0.799 & 0.791 & 0.812 & **0.940** & 0.627 & 0.759 \\ \hline PU4 & 0.779 & 0.783 & 0.774 & **0.881** & 0.595 & 0.725 \\ \hline Vak-LS1 & 0.533 & 0.531 & 0.638 & 0.571 & **0.882** & 0.540 \\ \hline Vak-LS2 & 0.606 & 0.589 & 0.720 & 0.651 & **0.920** & 0.586 \\ \hline Vak-LS3 & 0.544 & 0.544 & 0.672 & 0.603 & **0.870** & 0.550 \\ \hline TTT1 & 0.818 & 0.783 & 0.729 & 0.786 & 0.571 & **0.934** \\ \hline TTT2 & 0.806 & 0.812 & 0.715 & 0.770 & 0.579 & **0.935** \\ \hline TTT3 & 0.794 & 0.767 & 0.712 & 0.730 & 0.599 & **0.917** \\ \hline \end{tabular}
\end{table} 
Table 5: _Cont._  
Table 9 presents the path coefficients, significance, and confidence intervals for the hypotheses proposed in this study. The results indicate that perceived learning task, perceived usefulness, and perceived enjoyment have a significant positive relationship with behavioral intention to participate in an online gamified classroom (H1, H2, and H4 supported), and that VAK learning style has a significant positive effect on behavioral intention to participate in the online gamified classroom (H5a supported). Since both the indirect and direct effects between perceived usefulness and behavioral intention to participate were significant, the perceived learning task was a partial mediator between perceived usefulness and behavioral intention to participate (H3 Supported). The direct and all indirect effects between VAK learning style and behavioral intention to participate were significant, indicating a partial mediation between the two variables. The Variance Accounted For (VAF) value is usually calculated to determine the strength of mediation present, with full mediation when VAF \(>\) 80%, partial mediation when VAF \(<\) 80%, and no mediation present when VAF \(<\) 20% [50; 51].