## 9 The Model Testing  
The first step in model testing is to estimate the goodness-of-fit of the research model. The indices recommended for evaluating the overall model fitness are those suggested from previous studies [55, 57]: goodness-of-fit index (GFI), normed fit index (NFI), non-normed fit index (NNFI), comparative fit index (CFI), and the root-mean-square error of approximation (RMSEA). The chi-square test provides a statistical test for the null hypothesis that the model fits the data, but it is too sensitive to sample size differences, especially where the sample sizes exceed 200 respondents [54]. [PERSON] and [PERSON] [57] suggested a chi-square per degrees of freedom instead. All of the fit indexes indicate that the structural model has a good fit (Table 4). The second step in model estimation is to examine the path significance of each hypothesized association in the research model and variance explained (R\({}^{2}\)) by each path. Figure 1 presents the research model after validation along with the hypotheses. The standardized path coefficients, and explained variances of the structure model are shown. Non-significant estimates (\(p>0.05\)) are presented with dotted lines. \begin{table}
\begin{tabular}{|l|c|} \hline
**Fit index compared to the Recommended value** & **Research model** \\ \hline Chi-square/d.f. \(\leq\)3.0 & 2.04 \\ \hline \(\mathrm{GFI}\geq 0.80\) & 0.84 \\ \hline \(\mathrm{AGFI}\geq 0.80\) & 0.81 \\ \hline \(\mathrm{NFI}\geq 0.90\) & 0.95 \\ \hline \(\mathrm{NNFI}\geq 0.90\) & 0.97 \\ \hline \(\mathrm{RMSEA}\leq 0.08\) & 0.07 \\ \hline \(\mathrm{CFI}\geq 0.90\) & 0.97 \\ \hline \end{tabular}
\end{table} 
Table 4: Analysis of the goodness-of-fit of the overall model. \begin{table}
\begin{tabular}{|l|c|c|c|c|c|c|c|c|c|} \hline  & \multicolumn{2}{c|}{**Trust in**} & & & & & & & & \\ \multicolumn{2}{|c|}{} & \multicolumn{2}{|c|}{**e-**} & & & & & & & & \\ \multicolumn{2}{|c|}{**Government**} & **Perceived** & **Subjective** & & & **Job** & **Perceived** & **Perceived** & **Computability** & **Output** & **Relative** \\ \multicolumn{2}{|c|}{} & **website** & **risk** & **norm** & **Image** & **relevance** & **case of use** & **usefulness** & **Compatibility** & **quality** & **advantage** \\ \hline Perceived risk & \(-0.48\) & & & & & & & & & \\ \hline Subjective norm & 0.22 & \(-0.06\) & & & & & & & & \\ \hline Image & 0.08 & \(-0.01\) & 0.40 & & & & & & & \\ \hline Job relevance & 0.28 & \(-0.27\) & 0.36 & 0  
From Figure 1 it is obvious that:  
* Hypothesis H1 is supported since the effect of Trust in e-Government websites to Perceived risk is significant, and equals -0.49. The negative value implies that risk is perceived to be low by users who trust e-Government websites. R\({}^{2}\) equals 0.24, thus Perceived risk is largely explained by trust. H2 is also supported, since perceived ease of use affects significantly and positively Perceived usefulness (\(\gamma\) = 0.49). Also, H3 is supported since Subjective norm has a positive direct effect on Image (\(\gamma\) = 0.4). * The effect of Image to Perceived usefulness is not statistically significant thus H4 is not supported. On the contrary, the effect of Job relevance on Perceived usefulness is significant (\(\gamma\) = 0.19). Further, Output quality has no significant direct effect on Perceived usefulness, so H6 is not supported. Subjective norm has no significant effect on Perceived usefulness (H7 not supported). In conclusion, Perceived usefulness is affected by Perceived ease of use, and job relevance and the effect of perceived ease of use is larger than that of Job relevance. Totally, 44% of Perceived usefulness is explained by these variables. * Perceived risk has no significant direct effect on Intention to use (H9 not supported). Trust in e-Government websites has no significant effect on Intention to use, thus neither H8 is supported. Also, Image has no significant direct effect on intention to use (H10 not supported). Finally, Perceived ease of use has no significant direct effect on Intention to use.