## 3 Research Methodology
### Analysis  
The survey data were analyzed employing IBM SPSS Statistics 23 and SmartPLS 3, a multi-regression modelling approach that has gained prominence due to its precision and effectiveness. The multi-regression technique includes a regression estimation procedure, depicting quantitative and qualitative latent constructs while enforcing fundamental criteria on scale items, sample size, and redistributive assumptions. We performed an analysis in stages: (1) we evaluated the measurement model by restricting our indicators to a sequence of confirmatory factor analysis (CFA); and (2) we developed a structural model to investigate our hypotheses. SPSS 23 [79] was used for statistical analysis to substantiate the indicators and investigate the hypotheses. To ensure that the answers were truly representative, the stimulatory effects of nonrespondent bias were mitigated by distinct participants to a sample of nonrespondents that were predicated on personal characteristics such as age, education, and experience. At the 5% level of significance, the chi-square test results found no significant difference between the three respondent groups for age (\(\chi^{2}\) = 70.323, \(p<0.01\)), education (\(\chi^{2}\) = 484.580, \(p<0.01\)), gender (\(\chi^{2}\) = 4.937,\(p<0.01\)), and experience (\(\chi^{2}\) = 423.907, \(p<0.01\)). Consequently, we asserted that this study was not concerned with nonresponse bias. Another potential source of concern is the presence of common technique bias. By separating predictors and criterion construct objects throughout a lengthy survey question and assuring survey confidentiality, we reduced typical technique bias. The Harman one-factor test was used to look for common approach bias [80]. An unrotated confirmatory factor analysis of all the elements that were employed in this study reveals five elements with eigenvectors that were greater than one, which explains about 73% of the variation. The first (largest) component accounted for 18% of the variance. As multiple factors were collected and no single criterion accounted for more than 52% of the variation, common technique bias was not identified as a significant concern. A convergent validity test was employed to create a measurement model of the entire self-rating scales using confirmatory factor analysis (CFA). After that, the modification index was used to select objects from the factors. The element with the highest modification index score was eliminated first, followed by the next component, until the intended goodness of fit was accomplished. Most goodness-of-fit predictors surpassed the defined cut-off criterion, but a few factor loadings were below the minimum standard of 0.5. Therefore, we excluded them from acquiring valid data for our model. The factor loadings of all factors of estimated parameters are validated to be higher than the critical value point of 0.5 [15]. We are now at the crucial stage of determining whether the conceptual framework we have defined is legitimate after it has been explained and delivered all the necessary reliability and validity tests. It was achieved by ascertaining the goodness-of-fit benchmark for the model fit. The potential to ascertain how well the model fits into the variation structure of the dataset is regarded as the goodness of fit. The CFA evaluation and research framework represent the data well based on quantitative assessment criteria. [PERSON]'s alpha coefficients were employed to evaluate the reliability of the metrics, and construct correlation was applied to estimate the sample's validity. The items for each variable were created using previous research. These indices have the potential to provide definitive evidence about construct reliability and validity above the threshold of 0.50. Figure 1 illustrates our research framework, in which technology-oriented KM is depicted as an independent variable, smart city performance dependent, and innovation mediating and e-governance as a moderating variable.