## 4 Results  
Table 2 displays the item measures' standardized loading outcomes and other benchmarks, as well as the reliability and validity indicators. All of the components in the reliability analysis had factor loadings varying from 0.637 to 0.895, suggesting they were suitable for the rest of the assessment. The composite reliability indicators of all of the first-order components range from 0.903 to 0.953, which is greater than the recommended threshold of 0.70 [82]. Furthermore, the average variance that was extracted was greater than the 0.50 threshold that was suggested by [82]. The descriptive and discriminant validity of the measurements is shown in Table 3. For better discriminant validity, the square root of a construct's average variance must be greater than the square root of the construct's comparisons with the other components [83]. The findings also suggested that our components met this threshold, proving discriminant validity. An investigation of cross-loadings revealed appropriate discriminant validity as well. \begin{table}
\begin{tabular}{l c c c c c} \hline
**Item** & \begin{tabular}{c} **Standardized** \\ **Factor Loadings** \\ \end{tabular} & \begin{tabular}{c} **Composite** \\ **Reliability** \\ \end{tabular} & \
\begin{tabular}{c} **Average Variance** \\ **Extracted (AVE)** \\ \end{tabular} &
\begin{tabular}{c} **Cronbach Alpha** \\ **Bartlett’s Test** \\ \end{tabular} \\ \hline \multicolumn{5}{c}{Cronbach Alpha = 0.971} & KMO \& Bartlett’s Test = 0.815 \\ \hline KM1 & 0.742 & & & & \\ KM2 & 0.807 & & & & \\ KM3 & 0.895 & & & & \\ KM4 & 0.870 & & & & \\ KM5 & 0.852 & 0.953 & 0.717 & 0.943 & 0.934 \\ KM6 & 0.872 & & & & \\ KM7 & 0.875 & & & & \\ KM8 & 0.850 & & & & \\ \hline INN1 & 0.717 & & & & \\ INN2 & 0.678 & & & & \\ INN3 & 0.819 & & & & \\ INN4 & 0.823 & & & & \\ INN5 & 0.817 & & & & \\ INN6 & 0.656 & 0.929 & 0.569 & 0.910 & 0.928 \\ INN7 & 0.792 & & & & \\ INN8 & 0.637 & & & & \\ INN9 & 0.809 & & & & \\ INN10 & 0.764 & & & & \\ \hline \end{tabular}
\end{table} 
Table 2: Construct reliability and validity Using CR, AVE, Cronbach’s’ Alpha, and KMO Test. Figure 1: Research framework of AI-based KM on innovation and e-service delivery. We employed IBM SPSS Statistics 23 with the bootstrap technique to examine the proposed model. An evaluation of the conceptual framework, which included the coefficients of the correlation between the constructs, substantiated the hypothesized impacts and the R-square values, which suggest the proportion of the variation in the dependent constructs is expressed by their forebears. The control constructs (Model 1) were joined into the analysis model first, preceded by the main variables (Model 2), two-way interaction effect (Model 3), and moderating effects (Model 4), as suggested by [84]. Consequently, we simulated both the interactive (Models 3 and 4) and main effects on innovation (Model 2). The findings of the structural equation model analysis are demonstrated below. We concentrated on Model 3 and Model 4 because the speculated complex interactions are statistically significant. Figure 2 illustrates the Model 4 and Model 5 paths and their significance. Technology-oriented knowledge management had a significant impact on innovation (\(\beta\) = 0.766, \(p\) < 0.01) and e-service delivery (\(\beta\) = 0.370, \(p\) < 0.01). This factor accounted for 64.9% of the variation in innovation and 65.3% of the variation in e-service delivery. Consequently, H1 and H2 are supported. H3 was supported by the fact that innovation had a significant impact on e-service delivery (\(\beta\) = 0.935, \(p\) < 0.01). The outcomes for the three control variables in the study exhibit that the respondents' gender, education, age, and experience have no impact on innovation, e-governance, or e-service delivery.