## 8 Results
### Structural Model Testing  
Structural model testing started with examining a set of predictor constructs for the variance inflation factor (VIF). All values were below the cut-off value of 5, which means no collinearity issues were found [51]. Next, we calculated the coefficient of determination for endogenous variables (R\({}^{2}\)) and their predictive relevance (Q\({}^{2}\)). Results are shown in Table 4.  
The evaluation of goodness-of-fit indices for the structural model was performed in R since SmartPLS software provides less detailed data. All constructs showed satisfactory fit since all indices were in the desired range (see Table 5) [52]. These results suggested that this research model can confirm and explain the teachers' perception of the CRISS platform. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **R\({}^{2}\)** & **Results** & **Q\({}^{2}\)** & **Results** \\ \hline Net Impacts & 0.82 & Substantial & 0.42 & Path model’s predictive \\ System Use & 0.55 & Moderate & 0.26 & relevance for \\ User Satisfaction & 0.86 & Substantial & 0.50 & particular constructs \\ \hline \hline \end{tabular}
\end{table} 
Table 4: R\({}^{2}\) and Q\({}^{2}\) of the endogenous latent variables. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Latent Variable**} & \multirow{2}{*}{**Indicators**} & \multirow{2}{*}{**Item**} & \multirow{2}{*}{**Standard**} & \multicolumn{2}{c}{**Convergent**} & \multicolumn{2}{c}{**Internal Consistency**} \\ \cline{4-9}  & & & & **Coadings** & **AVE** & **CR** & **CA** \\ \hline \multirow{4}{*}{User Satisfaction} & US1 & 2.88 & 1.12 & 0.89 & & & \\  & US2 & 3.30 & 1.13 & 0.88 & & & \\  & US3 & 3.25 & 1.13 & 0.88 & 0.72 & 0.93 & 0.90 \\  & US4 & 3.24 & 1.05 & 0.72 & & & \\  & US5 & 3.19 & 1.06 & 0.85 & & & \\ \hline \multirow{4}{*}{Net Impacts} & NI3 & 3.38 & 1.06 & 0.84 & & & \\  & NI4 & 3.34 & 1.04 & 0.79 & & & \\  & NI5 & 3.28 & 1.01 & 0.84 & & & \\  & NI6 & 3.50 & 0.97 & 0.74 & & & \\  & NI7 & 3.44 & 1.02 & 0.82 & & & \\  & NI8 & 2.78 & 1.15 & 0.82 & 0.63 & 0.95 & 0.94 \\  & NI9 & 3.12 & 1.01 & 0.83 & & & \\  & NI10 & 2.90 & 1.04 & 0.77 & & & \\  & NI11 & 3.11 & 0.98 & 0.85 & & & \\  & NI12 & 3.57 & 0.96 & 0.74 & & & \\  & NI13 & 3.08 & 0.99 & 0.70 & & & \\ \hline \hline \end{tabular} Note. Loadings \(>\) 0.60; AVE \(>\) 0.50; CR \(>\) 0.70; CA \(>\) 0.70.  
\end{table} 
Table 2: _Cont._ \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Information** & **Net** & **Service** & **System** & **System** & **User** \\  & **Quality** & **Impacts** & **Quality** & **Quality** & **Use** & **Satisfaction** \\ \hline Information Quality & & & & & & \\ Net Impacts & 0.83 & & & & & \\ Service Quality & 0.53 & 0.53 & & & & \\ System Quality & 0.89 & 0.78 & 0.51 & & & \\ System Use & 0.71 & 0.74 & 0.31 & 0.71 & & \\ User Satisfaction & 0.89 & 0.90 & 0.53 & 0.87 & 0.77 & \\ \hline \hline \end{tabular} Note. HTMT \(<\) 0.90.  
\end{table} 
Table 3: Heterotrait-monotrait (HTMT). As shown in Table 6, the path coefficient estimates for the hypothesized relationships ranged from 0.10 to 0.70. They were all significant at a 1% significance level except in two cases [51]. The path between Service Quality and User Satisfaction was significant at a 5% significance level. The path between Service Quality and System Use was nonsignificant; therefore, this hypothesis was rejected. The measured \(\bar{f}^{2}\) values of the relationships between the constructs ranged from 0.03 to 0.10 or from 0.15 to 0.21 or were equal to 0.90, indicating low, medium, and large effect sizes, respectively [53]. The relationship of Service Quality to System Use was not supported in line with H5. Figure 3 shows the revised research model, i.e., relationships between the constructs based on supported hypotheses.