## 4 Results
### Second Phase of the Study
#### 4.2.3 Results
Therefore, the null hypothesis of [PERSON]'s Test of Sphericity was rejected, indicating that the scale is appropriate for factor analysis and thus has a sound validity structure. The results in Table 7 show that the total variance explained by the nine factors is 74.842%, which is greater than the 60% threshold, allowing for the classification of the 27 questionnaire items into nine distinct factors. Each item loaded significantly onto one factor, with all loadings higher than 0.5, and no item exhibited high cross-loadings on multiple factors. Additionally, the items under each dimension aggregated according to theoretical expectations, indicating that the questionnaire possesses strong construct validity. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Kaiser–Meyer–Olkin** & **0.909** \\ \hline \multirow{4}{*}{Bartlett’s Test of Sphericity} & Approx. Chi-Square & 5218.33 \\  & df & 351 \\  & Sig. & 0.000 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: KMO and Bartlett’s 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}{*}{Cross-Disciplinary Collaboration} & Q15 & 3.350 & 1.184 & \(-\)0.104 & \(-\)1.132 & 0.691 & 0.809 & 0.847 \\  & Q16 & 3.330 & 1.181 & \(-\)0.088 & \(-\)1.142 & 0.733 & 0.768 & \\  & Q17 & 3.350 & 1.197 & \(-\)0.195 & \(-\)1.043 & 0.719 & 0.782 & \\ \hline \multirow{3}{*}{Ethics and Privacy} & Q18 & 3.320 & 1.191 & 0.015 & \(-\)1.245 & 0.670 & 0.767 & 0.824 \\  & Q19 & 3.330 & 1.161 & \(-\)0.054 & \(-\)1.139 & 0.679 & 0.759 & \\  & Q20 & 3.330 & 1.184 & 0.040 & \(-\)1.290 & 0.691 & 0.747 & \\ \hline \multirow{3}{*}{Public Acceptance} & Q21 & 3.340 & 1.212 & \(-\)0.177 & \(-\)1.087 & 0.682 & 0.763 & 0.827 \\  & Q22 & 3.370 & 1.153 & \(-\)0.057 & \(-\)1.206 & 0.709 & 0.738 & \\  & Q23 & 3.340 & 1.216 & \(-\)0.139 & \(-\)1.110 & 0.662 & 0.783 & \\ \hline \multirow{3}{*}{Data Security and Copyright} & Q24 & 3.330 & 1.201 & \(-\)0.098 & \(-\)1.154 & 0.668 & 0.750 & 0.817 \\  & Q25 & 3.370 & 1.166 & \(-\)0.192 & \(-\)1.006 & 0.659 & 0.758 & \\  & Q26 & 3.380 & 1.145 & \(-\)0.107 & \(-\)1.051 & 0.680 & 0.737 & \\ \hline \multirow{3}{*}{Global Perspective and Localization} & Q27 & 3.330 & 1.143 & \(-\)0.069 & \(-\)1.052 & 0.664 & 0.730 & 0.808 \\  & Q28 & 3.300 & 1.144 & \(-\)0.036 & \(-\)0.972 & 0.661 & 0.733 & \\ \cline{1-1}  & Q29 & 3.300 & 1.127 & 0.029 & \(-\)1.075 & 0.646 & 0.749 & \\ \hline \multirow{3}{*}{Willingness to Adopt} & Q30 & 3.100 & 1.253 & 0.000 & \(-\)1.103 & 0.723 & 0.738 & 0.833 \\ \cline{1-1}  & Q31 & 3.120 & 1.220 & \(-\)0.010 & \(-\)0.985 & 0.678 & 0.783 & \\ \cline{1-1}  & Q32 & 3.190 & 1.210 & \(-\)0.036 & \(-\)1.061 & 0.678 & 0.783 & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._Correlation analysis is a method used to analyze the strength of the relationship between variables, the results we arrived at are shown in Table 8. Generally, if the correlation coefficient equals \(1\) or \(-1\), it is considered that the two variables are perfectly correlated. If the correlation coefficient falls within (\(-1\), \(-0.8\)] or [\(0.8\), \(1\)), it indicates an extremely strong correlation between the variables. If the correlation coefficient is within (\(-0.8\), \(-0.6\)] or [\(0.6\), \(0.8\)], it signifies a strong correlation. A correlation coefficient within (\(-0.6\), \(-0.4\)] or [\(0.4\), \(0.6\)] suggests a moderate correlation between the variables. If the coefficient is within (\(-0.4\), \(-0.2\)] or [\(0.2\), \(0.4\)], it indicates a weak correlation. Lastly, a correlation coefficient within (\(-0.2\), \(0.2\)) implies no correlation or a very weak correlation between the variables [60]. In data analysis methods, regression analysis is an extensively used statistical technique. It is employed to analyze the dependency relationships between variables, particularly focusing on the patterns of mutual variation.