## 4 Results
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Item** & \begin{tabular}{l} **Standardized** \\ **Factor Loadings** \\ \end{tabular} & \begin{tabular}{l} **Composite** \\ **Reliability** \\ \end{tabular} & \begin{tabular}{l} **Average Variance** \\ **Extracted (AVE)** \\ \end{tabular} & \
\begin{tabular}{l} **Cronbach Alpha** \\ **Cronbach Alpha** \\ \end{tabular} &
\begin{tabular}{l} **KMO and** \\ **Bartlett’s Test** \\ \end{tabular} \\ \hline EG1 & 0.720 & & & & & & \\ EG2 & 0.759 & & & & & \\ EG3 & 0.805 & & & & & \\ EG4 & 0.743 & & & & & \\ EG5 & 0.650 & 0.917 & 0.553 & 0.897 & 0.906 \\ EG6 & 0.732 & & & & & \\ EG7 & 0.784 & & & & & \\ EG8 & 0.735 & & & & & \\ EG9 & 0.753 & & & & & \\ \hline ESD1 & 0.735 & & & & & \\ ESD2 & 0.761 & & & & & \\ ESD4 & 0.813 & & & & & \\ ESD4 & 0.686 & & & & & \\ ESD5 & 0.672 & & 0.903 & 0.540 & 0.840 & 0.798 \\ ESD6 & 0.665 & & & & & \\ ESD7 & 0.776 & & & & & \\ ESD8 & 0.757 & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._ \begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline  & **N** & **Mean** & **Std. D** & **Edu** & **Gen** & **Exp** & **KM** & **Inn** & **EGov** & **ESD** \\ \hline Edu & 569 & 2.293 & 1.477 & 1 & & & & \\ Gen & 569 & 1.453 & 0.498 & 0.073 & 1 & & & \\ Exp & 569 & 2.489 & 0.695 & 0.037 & 0.157 ** & 1 & & \\ KM & 569 & 3.522 & 0.819 & 0.109 ** & 0.049 & 0.305 ** & 1 & \\ Inn & 569 & 3.592 & 0.683 & 0.108 ** & 0.095 * & 0.313 ** & 0.929 ** & 1 & \\ EGov & 569 & 3.851 & 0.602 & 0.102 * & 0.081 & 0.210 ** & 0.715 ** & 0.841 ** & 1 \\ ESD & 569 & 3.797 & 0.625 & 0.095 * & 0.034 & 0.318 ** & 0.668 ** & 0.771 ** & 0.614 ** & 1 \\ \hline \hline \end{tabular}  
* Correlation is significant at the 0.01 level (2-tailed).* Correlation is significant at the 0.05 level (2-tailed).  
\end{table} 
Table 3: Descriptive statistics, mean, standard deviation, and correlations between variables. We investigated the interaction effect of innovation on technology-oriented KM and e-service delivery and discovered a significant interactive effect (\(\beta=12.114\), \(p<0.01\)). We further examined the interaction impact of e-governance between innovation and e-service delivery and found a strong significant moderating effect (\(\beta=0.447\), \(p<0.01\)). This result corroborates our hypothesis that complementarity is essential in the suggested framework. Although the complementarity of internal and external dynamics may expedite synergic innovation, few investigations have been made to test this correlation. Therefore, we designed to simulate both the interactive and main impacts of innovation. When these interaction terms were included, the R\({}^{2}\) for innovation increased to 0.653. Model 5 was explained by applying multiple-regression modeling to explore the mediating role of innovation when knowledge management was a predictor variable and e-service delivery was considered an observed variable. The results in Table 5 revealed (\(\beta=12.114\), \(p<0.01\)) a significant positive and indirect relationship between knowledge management and e-service delivery, hence, H4 is strongly supported. The results of the moderating analysis are shown in Model 4 of Table 4. The findings demonstrate a direct positive relationship between e-governance and e-service delivery (\(\beta=0.447\), \(p<0.01\)), strongly supporting our proposed H5. It indicated a significant and positive direct relationship between e-governance and e-service delivery. Moreover, we hypothesized that e-governance would play a moderating role in the relationship between innovation and e-service delivery. The findings (\(\beta=0.079\), \(p<0.01\)) provided strong support for our hypothesis H6 as an indirect moderating relationship between innovation and e-service delivery. The outcomes showed a substantial and progressive direct and indirect relationship between e-governance and e-service delivery; e-governance plays a critical positive and significant moderating role between innovation and e-service delivery.