## 8 Findings  
LISREL 8.8 was used to analyze the data. Model estimation was done using the maximum likelihood approach, with the item covariance matrix used as input.  
The measurement model was first examined for validating and refining the research constructs, followed by an analysis of the Structural Equation Model for testing the research hypotheses in the research model.  
Confirmatory Factor Analysis was used for model refinement. Testing the measurement model involves examining the convergent validity, discriminant validity, and internal consistency of the constructs. Reliability and convergent validity of the measurements are estimated by the item factor loadings, Composite Reliability, and Average Variance Extracted [54].  
Convergent validity refers to the extent to which the items under each construct are actually measuring the same construct. Two methods were applied to assess convergent validity. First, item reliability was examined for each item, which suggested that the factor loading of each item on its corresponding construct must be higher than 0.55 [41]. All items had a loading above the suggested threshold. Convergent validity was assessed by examining the average variance extracted (AVE) for each construct. The AVE for a construct reflects the ratio of the construct's variance to the total variances among the items of the construct. The average extracted variances, presented in Table 2, are all above the recommended 0.50 level [41, 55].  
Discriminant validity refers to the extent to which a given construct differs from other constructs. An Exploratory Factor Analysis (Principal Component Analysis with Varimax Rotation), demonstrated that all items loaded more heavily on their corresponding constructs rather than on other constructs. Further, the square roots of all AVEs were larger than correlations among constructs, thereby satisfying discriminant validity. Compared to [PERSON]'s alpha, which assumes equal weights of all the items of a construct and is influenced by the number of items, Composite Reliability relies on actual loadings to compute the factor scores and thus provides a better indicator for measuring internal consistency [41]. As shown in Table 2, Composite Reliability are above the threshold of 0.7. Overall, the measures in this study are reliable and valid.  
\begin{table}
\begin{tabular}{|l|c|c|} \hline  & **AVE** & **CR** \\ \hline Trust in e-Government website & 0.83 & 0.94 \\ \hline Perceived risk & 0.84 & 0.91 \\ \hline Subjective norm & 0.77 & 0.91 \\ \hline Image & 0.74 & 0.89 \\ \hline Job relevance & 0.65 & 0.79 \\ \hline Perceived ease of use & 0.64 & 0.87 \\ \hline Perceived usefulness & 0.75 & 0.94 \\ \hline Compatibility & 0.74 & 0.92 \\ \hline Output quality & 0.60 & 0.73 \\ \hline Relative advantage & 0.69 & 0.90 \\ \hline Intention to use & 0.76 & 0.93 \\ \hline \end{tabular}
\end{table}
Table 2: Composite Reliability (CR), and Average Variance Extracted (AVE).  
Table 3 shows that all the inter-construct correlations are below 0.9. Also, the estimated correlation between all construct pairs is below the suggested cutoff of 0.9, and this implies distinctness in construct content or discriminant validity [41, 56].