## 4 Results
### Testing Normality, Multicollinearity, and Coefficient of Determination  
Table 6 highlighted data normality, Multicollinearity, and coefficient of determination of constructs. The applied tools, mean, standard deviation, skewness kurtosis, and VIF, were extracted using the descriptive statistics option of SPSS 25. The R square values were obtained from the AMOS 21 output at the time of structural modeling assessment. In terms of normality, the results were good, as no issues were recorded from the variance obtained from the normality assessment. The skewness and kurtosis values were less than \(\pm\)3 and \(\pm\)10 [104], respectively (Table 6). The effective technique involving the assessment of the variance inflation factor (VIF) was utilized as recommended by [PERSON] et al. [105] to determine the presence of multicollinearity amongst the independent variables. The results revealed that the VIF range varies from 1.00 to 2.582, which is substantially below 10. Therefore, this suggests that multicollinearity is not a concern in this investigation.  
The R square analyzes the explanatory capacities of models by identifying the endogenous variables highlighted as determining coefficients. According to [PERSON] [106], the R\({}^{2}\) value of the endogenous variable is substantial when the value is above 0.26 and up to 0.13 is regarded as moderate. However, a value below 0.13 is considered weak. The R\({}^{2}\) estimates of each endogenous value reported in the research in Table 6 are based on the conditions specified by [PERSON] and [PERSON] [107], which demonstrate that the proposed model falls within a high explanatory power range with an exception of the PBC, which has low predicting power.  
\begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline  & **Mean** & **Std. Deviation** & **Skewness** & **Kurtosis** & \multicolumn{3}{c}{**VIF**} & \multicolumn{3}{c}{**R\({}^{2}\)**} \\ \cline{4-10}  & & & & **AT** & **BI** & **PBC** & **SN** & **Values** & **Strength** \\ \hline LB & 3.305 & 0.7534 & \(-\)0.259 & \(-\)0.076 & 2.087 & & & & \\ \hline EK & 3.080 & 0.752 & \(-\)0.329 & \(-\)0.438 & 1.905 & 1.967 & & & \\ \hline EC & 3.280 & 0.904 & \(-\)0.243 & \(-\)0.064 & 2.391 & & 1.00 & 1.00 & & \\ \hline SN & 3.426 & 0.779 & \(-\)0.456 & 0.323 & 2.320 & 2.303 & & & & \\ \hline PBC & 2.587 & 1.089 & 0.620 & \(-\)0.652 & & 1.060 & & 0.09 & Low \\ \hline AT & 3.342 & 0.812 & \(-\)0.485 & 0.309 & & 2.582 & & 0.70 & High \\ \hline GSI & 3.460 & 0.744 & \(-\)0.494 & 0.781 & & & 0.61 & High \\ \hline BI & 3.298 & 0.828 & \(-\)0.300 & \(-\)0.375 & & & 0.77 & High \\ \hline \hline \end{tabular} Source: Selected output of SPSS 25.0 and AMOS 21.0.  
\end{table}
Table 6: Variance Inflation Factor (VIF) & R\({}^{2}\) Value.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **LB** & **EK** & **EC** & **SN** & **PBC** & **AT** & **GSI** & **BI** \\ \hline LB & & & & & & & \\ \hline EK & 0.703 & & & & & & & \\ \hline EC & 0.749 & 0.728 & & & & & & \\ \hline SN & 0.736 & 0.749 & 0.786 & & & & & \\ \hline PBC & 0.152 & 0.226 & 0.300 & 0.258 & & & \\ \hline AT & 0.762 & 0.794 & 0.834 & 0.824 & 0.230 & & \\ \hline GSI & 0.720 & 0.782 & 0.823 & 0.777 & 0.196 & 0.817 & \\ \hline BI & 0.768 & 0.830 & 0.828 & 0.775 & 0.472 & 0.831 & 0.799 \\ \hline \hline \end{tabular} Source: Selected output of SPSS 25.0 and MS-Excel.  
\end{table}
Table 5: Discriminant Validity using Heterotrait-Monotrait Ratio (HTMT).