## 4 Results and Discussion
### Measurement Model Analysis and Validation
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Variable** & **Item** & **Loading** & \
\begin{tabular}{c} **United Kingdom** \\ **CR** \\ \end{tabular} & **AVE** & **Loading** &
\begin{tabular}{c} **Turkey** \\ **Cronbach’s** \\ \end{tabular} & **AVE** \\ \hline Performance & PE1 & 0.732 & 0.799 & 0.919 & 0.554 & 0.793 & 0.799 & 0.882 & 0.554 \\ (PE) & PE2 & 0.8 & & & & 0.817 & & \\  & PE3 & 0.79 & & & & 0.763 & & \\  & PE4 & 0.646 & & & & 0.472 & & \\ Effort & & & & & & & \\ Expectancy (EE) & EE1 & 0.598 & 0.796 & 0.884 & 0.667 & 0.598 & 0.796 & 0.897 & 0.667 \\  & EE2 & 0.877 & & & & 0.877 & & \\  & EE3 & 0.859 & & & & 0.859 & & \\  & EE4 & 0.896 & & & & 0.896 & & \\ Social Influence (SI) & SI1 & 0.704 & 0.799 & 0.903 & 0.566 & 0.704 & 0.799 & 0.827 & 0.566 \\  & SI2 & 0.689 & & & & 0.689 & & \\  & SI3 & 0.817 & & & & 0.817 & & \\  & SI4 & 0.793 & & & & 0.793 & & \\ Perceived & & & & & & & \\ Sociability (PS) & PS1 & 0.893 & 0.797 & 0.884 & 0.62 & 0.893 & 0.797 & 0.885 & 0.620 \\ (PS) & PS2 & 0.825 & & & & 0.825 & & \\  & PS3 & 0.805 & & & & 0.805 & & \\  & PS4 & 0.594 & & & & 0.594 & & \\ Behavioral & & BI1 & 0.838 & 0.75 & 0.962 & 0.707 & 0.838 & 0.75 & 0.946 & 0.707 \\  & BI2 & 0.853 & & & & 0.853 & & \\  & BI3 & 0.831 & & & & 0.831 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: United Kingdom and Turkey—validity analysis. * The goodness-of-fit (GFI) index is greater than 0.80 [67] or 0.90 [65], which is a measure used to evaluate how well the observed data match the hypothesized model. It is computed as follows: \[\text{GFI}=1-\frac{\text{F}_{\text{model}}}{\text{F}_{0}}\] (4) where Fmodel: The minimum fit function after a SEM model has been estimated using degrees of freedom for the hypothesized model; F0: The fit function that would result if all parameters were zero. * The root mean square error approximation (RMSEA) is less than 0.08 [65]. The root mean square error approximation (RMSEA) is a widely used index to assess the fit of a structural equation model. It evaluates how well the model approximates the data, considering the model's complexity. Lower values indicate a better fit, with thresholds typically interpreted as follows: \[\text{RMSEA}=\sqrt{\frac{(\text{X}^{2}-\text{df}_{\text{model}})}{\text{N}-1}}\] (5) where X2: Chi-square statistic for the hypothesized model; dfmodel: Degrees of freedom for the hypothesized model; N: Sample size. The RMSEA penalizes models with unnecessary complexity by incorporating degrees of freedom in its calculation, making it a robust indicator of parsimony. It is particularly valuable for comparing models with different levels of complexity. A confidence interval is often reported alongside the RMSEA to indicate its precision. * The category of parsimonious fit
* The \(\text{CMIN}/\text{df}\) value is less than 3 [55]. This GOF measure is a simple ratio of \(\chi 2\) to the degrees of freedom for a model. * The category of incremental fit
* The comparative fit index (CFI) is indicative of a good fit if its value is greater than 0.95 [68]. The comparative fit index (CFI) is a widely used measure to evaluate the goodness-of-fit of a structural equation model. It compares the fit of the hypothesized model to that of an independent (null) model, where no relationships among variables are assumed. Higher values indicate a better fit, with the following thresholds commonly applied. CFI is computed as follows: \[\text{CFI}=1-\frac{\left(\text{x}_{\text{model}}^{2}-\text{df}_{\text{model}} \right)}{\left(\text{x}_{\text{null}}^{2}-\text{df}_{\text{null}}\right)}\] (6) where xmodel: Chi-square statistic for the hypothesized model; dfmodel: Degrees of freedom for the hypothesized model; xnull: Chi-square statistic for the null model; dffull: Degrees of freedom for the null model. CFI adjusts for sample size and is less sensitive to sample size variations than some other fit indexes, making it a robust and reliable measure. Comparing the hypothesized model to a baseline model provides a normalized index of improvement in fit.