## 4 Results
### Second Phase of the Study
#### 4.2.3 Results  
Current methods for testing the common method bias include [PERSON]'s single-factor test, a partial correlation, marker variable techniques, unique variances correlational models, and direct product models. To ensure the accuracy of statistical analysis results, this study employed the most commonly used [PERSON]'s single-factor method to test for common method bias [59]. The results indicate that this scale has passed the common method bias test, as shown in Table 4.  
As shown in Table 5, the relationship model proposed in this study consists of 9 variables and 27 items. All scales used Likert five-point scales, with a maximum value of 5 and a minimum value of 1. Higher scores on all scales indicate higher levels of evaluation. The absolute skewness values of all measurement items are less than 2, and the absolute kurtosis values are less than 7, indicating that the survey data from this study generally meet the requirements of the critical values and are suitable for further analysis. Moreover, the [PERSON]'s Alpha coefficients for all variables meet the basic standard of being greater than 0.7, with most exceeding 0.8. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Component**} & \multicolumn{3}{c}{**Initial Eigenvalues**} & \multicolumn{3}{c}{**Extraction Sums of Squared Loadings**} \\ \cline{2-7}  & **Total** & **\% of Variance** & **Cumulative \%** & **Total** & **\% of Variance** & **Cumulative \%** \\ \hline
1 & 9.316 & 34.504 & 34.504 & 9.316 & 34.504 & 34.504 \\
2 & 1.634 & 6.052 & 40.556 & 1.634 & 6.052 & 40.556 \\
3 & 1.499 & 5.552 & 46.108 & 1.499 & 5.552 & 46.108 \\
4 & 1.470 & 5.446 & 51.554 & 1.470 & 5.446 & 51.554 \\
5 & 1.435 & 5.313 & 56.867 & 1.435 & 5.313 & 56.867 \\
6 & 1.361 & 5.042 & 61.910 & 1.361 & 5.042 & 61.910 \\
7 & 1.219 & 4.515 & 66.424 & 1.219 & 4.515 & 66.424 \\
8 & 1.170 & 4.334 & 70.758 & 1.170 & 4.334 & 70.758 \\
9 & 1.103 & 4.084 & 74.842 & 1.103 & 4.084 & 74.842 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Common method deviation test. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Variables** & **Mean** & **Std.** & **Skewness** & **Kurtosis** & **CITC** & **CA** \\ \hline \multirow{3}{*}{Innovation} & Q6 & 3.410 & 1.173 & \(-\)0.142 & \(-\)1.201 & 0.659 & 0.724 & 0.804 \\  & Q7 & 3.360 & 1.147 & \(-\)0.010 & \(-\)1.177 & 0.653 & 0.731 & \\  & Q8 & 3.370 & 1.165 & \(-\)0.089 & \(-\)1.164 & 0.642 & 0.742 & \\ \hline \multirow{3}{*}{Market Demand} & Q9 & 3.340 & 1.211 & \(-\)0.119 & \(-\)1.128 & 0.691 & 0.757 & 0.828 \\  & Q10 & 3.390 & 1.168 & \(-\)0.082 & \(-\)1.177 & 0.698 & 0.750 & \\  & Q11 & 3.360 & 1.152 & \(-\)0.143 & \(-\)1.078 & 0.668 & 0.779 & \\ \hline \multirow{3}{*}{Technological Maturity} & Q12 & 3.290 & 1.151 & \(-\)0.034 & \(-\)1.083 & 0.678 & 0.797 & 0.838 \\  & Q13 & 3.320 & 1.170 & \(-\)0.117 & \(-\)1.090 & 0.704 & 0.772 & \\ \cline{1-1}  & Q14 & 3.270 & 1.230 & \(-\)0.068 & \(-\)1.162 & 0.721 & 0.755 & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of descriptive statistics and reliability analyses. When conducting a validity analysis using factor analysis, it is imperative first to determine whether the conditions for the factor analysis are satisfied. Generally, two conditions need to be met: firstly, the KMO measure must be greater than 0.7; secondly, [PERSON]'s Test of Sphericity should be significant, with a \(p\)-value less than 0.05. If these two conditions are fulfilled, it indicates strong correlations among the observed variables, making them suitable for factor analysis; Table 6 shows these data. The test results indicated that the KMO measure for the survey data was 0.909, which is greater than the requisite 0.7, suggesting that the questionnaire is suitable for factor analysis. The result of [PERSON]'s Test of Sphericity showed an approximate chi-square value of 5218.330, which is considerably large, with a significance probability of 0.000 (\(p<0.01\)).