## 5 Results  
### Measurement Model  
The data analysis employed [PERSON] and [PERSON]'s [58] two-step approach, initially evaluating the convergent and discriminant validity of the measurement model. Subsequently, the research hypotheses and the structural model framework were tested. Given that data collection was performed via questionnaires, this study implemented [PERSON]'s single-factor test to assess the potential for common method bias. [PERSON]'s single-factor test involves analyzing all of the study's items that were subjected to principal component analysis or exploratory factor analysis [59]. The results showed that all extracted factors explained 76.351% of the total variance, representing a relatively strong explanatory power of the extracted factors in capturing a large portion of the variance in the responses. The first factor explained 30.597% of the first variance, indicating no common methodological bias. When evaluating the reliability and validity of test model data, [PERSON]'s alpha, Composite Reliability (CR), and Average Variance Extracted (AVE) can be used as statistical indicators [60]. Internal consistency is considered good when [PERSON]'s alpha is \(\geq\)0.7. The data in Table 3 show that all [PERSON]'s alpha values are well above this critical value, indicating that the items for each variable are reliable. Meanwhile, the CR value was also greater than 0.7, strengthening the reliability evaluation provided by [PERSON]'s alpha and confirming the variables' consistency and reliability. AVE measures the average proportion of variance in an observed variable that can be attributed to its underlying factor, thereby assessing the convergent validity of the construct. The AVE values for all variables in Table 3 meet or exceed the criterion of 0.5, and the high loading on the data results indicates that the terms strongly indicate validity. Moreover, Table 4 was subjected to a validated factor analysis (CFA) using maximum likelihood estimation to estimate the measured customer purchase intention characteristics. The results from the confirmatory factor analysis (CFA) indicated a good model fit: the chi-squared statistic was significant (\(\chi^{2}\) (329) = 534, \(p\) = 0.000), and the normed chi-squared \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Total Variance Explained**} \\ \hline  & \multicolumn{3}{c}{**Initial Eigenvalues**} & \multicolumn{3}{c}{**Extraction Sums of Squared Loadings**} \\  & & \% of** & & & \% of & \\ \hline
**Component** & **Total** & **Variance** & \
\begin{tabular}{c} **Cumulative** \\ **\%** \\ \end{tabular} & **Total** & **Variance** &
\begin{tabular}{c} **Cumulative** \\ **\%** \\ \end{tabular} \\ \hline
1 & 8.567 & 30.597 & 30.597 & 8.567 & 30.597 & 30.597 \\
2 & 2.900 & 40.953 & 40.953 & 2.900 & 10.356 & 40.953 \\
3 & 2.334 & 49.289 & 49.289 & 2.334 & 8.337 & 49.289 \\
4 & 2.114 & 56.839 & 56.839 & 2.114 & 7.549 & 56.839 \\
5 & 2.207 & 64.078 & 64.078 & 2.027 & 7.240 & 64.078 \\
6 & 1.813 & 70.555 & 70.555 & 1.813 & 6.477 & 70.555 \\
7 & 1.623 & 76.351 & 76.351 & 1.623 & 5.796 & 76.351 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Result of [PERSON]’s single-factor test. (\(\chi^{2}/\text{df}=1.524\)) was below the suggested threshold of 3.0 as recommended by [PERSON] and [PERSON] [61]. Additionally, all other fit indices were within the levels recommended for a good model fit [62], confirming that the proposed model adequately fits the observed data. Furthermore, in this study, we also tested discriminant validity using the Fornell-Larcker criterion method [63], the specific results of which are shown in Appendix A. Discriminant validity is a form of structural validity, which generally means that items that are not in the same factor will not be constituted in the same factor. The method of evaluating such validity is to conduct correlation analysis, calculate the square root of mean-variance extraction (AVE), and then combine these results for analytical expression.