## 4 Results and Discussion
### Factor Analysis  
Two statistical tests were carried out on data before conducting factor analysis to indicate the suitability of the data for structure detection. The first test is Kaiser-Meyer-Olkin (KMO) that measures sampling accuracy to indicate the proportion of variance of the variables that might cause by the  
Figure 3: Average rating for the likelihood of risk factors having an impact on value creation. underlying factors. High values close to 1.0 in the KMO test indicate that a factor analysis is useful for the data while the values less than 0.50 indicate that the results of the factor analysis are not useful. Also, KMO values between 0.5 and 0.7 are good, between 0.7 and 0.8 are very good, and above 0.8 are excellent. The Bartlett Test of Sphericity tests the hypothesis that the correlation matrix is an identity matrix, i.e., a small significance level of less than 0.05 indicates the need for factor analysis [55; 56; 57]. [PERSON] [55] mentions that a value close to 1 indicates that the patterns of correlations are relatively compact and the factor analysis will provide distinct and reliable factors. [PERSON] [60] recommended accepting values greater than 0.5 as acceptable. In the present research, the corresponding value for KMO was 0.702 and Bartlett test found significance value of '0'. As the KMO value was found close to 1, the factor analysis is likely to be appropriate and acceptable. [PERSON]'s test measures the null hypothesis (H\({}_{0}\): \(p>0.05\)) and shows that the original correlation matrix is an identity matrix. Therefore, factor analysis needs some relationships between variables and the significance value to be (\(p<0.05\)). By considering the significance level of 0.05, [PERSON]'s test showed the \(p\)-values for the likelihood of risk impact were highly significant. This test shows that the correlation matrix is not an identity matrix, so there are some relationships between the variables. Both KMO and Bartlett tests demonstrated that factor analysis is appropriate for these data. Table 3 presents the components extracted by the principal component analysis (PCA). The likelihood of risk factors impacting on the value created by green building design shows that just 16 components carry an eigenvalue of more than 1 and account for nearly 79.939% of the whole variance. Consequently, the 16 components can be considered as representative of the 66 factors included in this study. In the subsequent phase of factor analysis, a rotated component matrix was extracted to find out the risk factors with the highest level of influence on project value creation. Table 4 presents the summary of factor analysis results. The matrix loading scores in Table 3 extracted the most effective factors of each component. The factors with the highest scores and correlation values were chosen for each component. For example, the risk factor (R1; 0.808) has greater influence on component 13 compared to other components, whereas the risk factor R46 (0.843) has more influence on component 4 in relation to other components, and R57 (0.762) has more influence on component 9 in relation to other components. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Component**} & \multicolumn{4}{c}{**Initial Eigenvalues**} & \multicolumn{2}{c}{**Extraction Sums of Squared Loadings**} & \multicolumn{2}{c}{**Rotation Sums of Squared Loadings**} \\ \cline{2-10}  & \multirow{2}{*}{**Total**} & **\% of** & **Cumulative** & **\% of** & **Cumulative** & **\%** & **Total** & **\% of** & **Cumulative** \\  & & **Variance** & **\%** & **Total** & **Variance** & **\%** & **\%** & **Variance** & **\%** \\ \hline
1 & 22.380 & 33.909 & 33.909 & 22.380 & 33.909 & 33.909 & 6.027 & 9.132 & 9.132 \\
2 & 4.154 & 6.294 & 40.203