## 4 Results and Discussion
### Measurement Model Analysis and Validation  
The results of [PERSON]'s test of sphericity indicated that the correlation matrix was not random, and all data were well above the minimum standard for conducting factor analysis. The Kaiser-Meyer-Olkin (KMO) measure assesses the adequacy of the sample for factor analysis. It evaluates the proportion of variance among variables that might be common variance (i.e., shared across factors) versus individual variance. The KMO is calculated as follows:  
\[\text{KMO for multiple variables}=\frac{\sum_{i\
eq j}\sum r_{ij}^{2}}{\sum_{i \
eq j}\sum r_{ij}^{2}+\sum_{i\
eq j}\sum q_{ij}^{2}} \tag{3}\]  
where  
\(r_{ij}^{2}\): correlation coefficients. \(q_{ij}^{2}\): partial correlation coefficients. According to [64], the Keiser-Meyer-Olkin Measure of Sampling Adequacy (KMO) values \"in the 0.90 s, marvelous; in the 0.80 s, meritorious; in the 0.70 s, middling; in the 0.60 s, mediocre; in the 0.50 s, miserable; below 0.50, unacceptable\" (p. 35). Consequently, we concluded that the correlation matrix was suitable for factor analysis, as shown in Table 6.  
The quality of the constructs in this study is assessed based on the evaluation of the measurement model. The assessment of the quality criteria starts with the evaluation of the factor loadings, which is followed by establishing the construct reliability and construct validity. Composite reliability was in line with the 0.70 cut-offs in the literature [65], and [PERSON]'s alpha was greater than 0.80 [66], confirming the reliability of the questionnaire. The factor loadings and the average variance extracted (AVE) were used to analyze convergent validity. The variables indicated more than three items, which have more than \begin{table}
\begin{tabular}{c c c} \hline \hline
**Data** & **Barlett’s Test of Sphericity** & **KMO** \\ \hline Complete Data & \(\chi 2(171)=4181,p<0.001\) & 0.90 \\ The Unified Kingdom & \(\chi 2(171)=4790,p<0.001\) & 0.93 \\ Turkey & \(\chi 2(171)=4481,p<0.001\) & 0.90 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: KMO and Bartlett’s tests. 0.5 factor loadings [65]. The values of AVE obtained were greater than the 0.5 cut-offs, indicating convergent validity [66]. The results showed that the items from the constructs are distinct from each other, confirming the discriminant validity of the data (Tables 7 and 8). A model is considered as fitting well according to the goodness-of-fit (GOF) indices: \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Variable** & **Items** &
\begin{tabular}{c} **Factor** \\ **Loadings** \\ \end{tabular} & **CR** & **Cronbach’s** \(\alpha\) & **AVE** \\ \hline Performance Expectancy (PE) & PE1 & 0.756 & 0.823 & 0.903 & 0.540 \\  & PE2 & 0.804 & & & & \\  & PE3 & 0.769 & & & & \\  & PE4 & 0.594 & & & & \\ Effort Expectancy (EE) & EE1 & 0.596 & 0.875 & 0.890 & 0.640 \\  & EE2 & 0.845 & & & & \\  & EE3 & 0.861 & & & & \\  & EE4 & 0.866 & & & & \\ Social Influence (SI) & SI1 & 0.755 & 0.848 & 0.873 & 0.583 \\  & SI2 & 0.742 & & & & \\  & SI3 & 0.772 & & & & \\  & SI4 & 0.784 & & & & \\ Perceived Sociability (PS) & PS1 & 0.866 & 0.871 & 0.886 & 0.630 \\  & PS2 & 0.825 & & & & \\  & PS3 & 0.763 & & & & \\  & PS4 & 0.711 & & & & \\ Behavioral Intention (BI) & BI1 & 0.821 & 0.872 & 0.959 & 0.693 \\  & BI2 & 0.843 & & & & \\  & BI3 & 0.834 & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Complete data validity analysis.