## 4 Research Methodology
### Structure Equation Measurement  
In Structure Equation Measurement (SEM), the first measure is to find the association among independent and dependent variables by the model fit indices; eight fit indicators are used in this work, to test the proposed model and validate the proposed hypotheses, and the eight fit indicators are Chi-Square (\(\chi^{2}\)), Goodness-of-Fit Index (GFI), Adjusted goodness of fit index (ACFI), Root Mean Square Error of Approximation (RMSEA), Root Mean Square Residual (RMR), Comparative Fit Index (CFI), Normed Fit Index (NFI), and Incremental Fit Index (IFI). The performance of eight indicators, and each performance measure appropriate in the research, is mentioned below in Table 7.  
The model was confirmed by different criteria of model fit indicators, such as the \(\chi^{2}\) value, i.e., 1.745, which was between 1 to 2, indicating that the model was fit to the data. The goodness of fit index value was 0.877, which is acceptable as per threshold limit 0.9 [58], indicating that the model was appropriate for the data. Next, the adjusted goodness of fit index value was found to be 0.885, which is higher than threshold limit, i.e., 0.80. Hence, it is considered a well-fitting model. Now, the Root Mean Square Error of Approximation parameters is within the range of 0.05 to 0.10, an indicator of good fit, and value beyond 0.10 suggested poor fit was considered [59]. Hence, the result found, i.e., 0.059, is as per criteria, which is acceptable in study. The outcome of the root mean square residual is 0.043. Therefore, value as high as 0.08 are considered to be appropriate [60]. Next, the NFI is a cumulative measurement of goodness for a mathematical model that is not influenced by the numbers of specifications/constructs in the study. Goodness of fit is tested by a correlation between the study model and the model of completely uncorrelated constructs [61]; the value received in study is 0.895. Hence, the result is favorable, as \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Constructs** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** \\ \hline PE1 & 0.297 & 0.343 & 0.37 & **0.584** & 0.109 & 0.218 & 0.8 & \(-\)0.006 \\ PE2 & 0.265 & 0.176 & 0.238 & **0.718** & 0.8 & 0.192 & 0.024 & 0.18 \\ PE3 & 0.245 & 0.164 & 0.228 & **0.589** & 0.086 & 0.079 & 0.184 & 0.265 \\ ATT1 & 0.15 & **0.616** & 0.131 & 0.381 & 0.157 & 0.188 & 0.475 & 0.152 \\ ATT2 & 0.210 & **0.658** & 0.217 & 0.277 & 0.019 & 0.248 & 0.097 & 0.065 \\ ATT3 & 0.179 & **0.567** & 0.159 & 0.341 & 0.225 & 0.009 & 0.164 & 0.146 \\ FL1 & 0.175 & 0.127 & 0.145 & 0.117 & 0.029 & **0.852** & 0.56 & 0.026 \\ FL2 & 0.235 & 0.291 & 0.183 & 0.156 & 0.231 & **0.78** & \(-\)0.134 & 0.176 \\ FL3 & 0.374 & 0.273 & 0.182 & \(-\)0.087 & 0.133 & **0.670** & 0.039 & 0.126 \\ BI1 & 0.234 & 0.365 & 0.227 & 0.318 & 0.342 & 0.62 & **0.512** & 0.110 \\ BI2 & 0.112 & 0.065 & 0.157 & 0.076 & 0.475 & 0.067 & **0.645** & 0.184 \\ BI3 & 0.359 & 0.072 & 0.243 & 0.303 & 0.224 & 0.19 & **0.547** & 0.085 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: \(Cont.\)  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Hypothesis** & **Construct** & **Estimating** & **Decision** \\ \hline H1 & SI \(\longrightarrow\) UC & 0.356 **per threshold limit, and the results of NFI were evaluated with the help of the formula mentioned below. \[NFI=\Delta_{1}=1-\frac{\hat{C}}{\hat{C}_{b}}=1-\frac{\hat{F}}{\hat{F}_{b}}. \tag{1}\]  
Now, the value of Incremental Fit Index was measured to know the degree of freedom of the model. The alternative name of IFI is comparative [62] and relative fit indices [63]. Hence, the value is measured is 0.955, which indicates that the results are supportive as per the static criteria.