# Underlying Factors and Strategies for Organizational BIM Capabilities: The Case of Iran
## 4 Analysis and Results  
### Exploratory Factor Analysis (EFA)  
There exist two forms of factor analysis: EFA and confirmatory factor analysis. EFA seeks to regroup and reduce many interrelated variables into a group of constructs [95]. Contradictory, confirmatory factor analysis verifies if a certain group of constructs is influencing variables in a predicted way. This study used the EFA to discover the multiple dimensions of capability factors affecting OBIMC and strategies for improving OBIMC.  
Regarding the sample size for EFA, the ratio of a sample size to the number of variables method was used. As recommended by [96], the minimum ratio value should be 5.00. In this study, the ratio value is \(126/14=9.00\) for the strategies data and \(126/19=6.63\) for the capability factors data. Therefore, using the mentioned method, the sample size for this study is considered adequate for conducting EFA. Then, to ensure that the data are suitable for EFA, the Kaiser-Meyer-Olkin (KMO) test and [PERSON]'s test of sphericity were conducted. Accordingly, the strategy data have a KMO value of 0.899, which was greater than the threshold value of 0.80 [97]. For capability factors, the KMO value was 0.927. Based on KMO values for both data, the data were considered suitable for EFA. Furthermore, the result of the [PERSON]'s test of sphericity revealed a significance value of less than 0.001, indicating that the correlation matrix is significant at \(p<0.05\) and, thus, is not an identity matrix. Therefore, the data are suitable for the EFA method.  
The factor extraction method used was Principal Axis Factoring (PAF) because it yields more stable loadings than other extraction methods [98]. PAF has been used in other construction management works [99; 100]. The factor loading threshold value for identifying a construct is 0.50 [101]. After conducting EFA on strategies data, ten strategies were finally included in the analysis. From ten strategies, two constructs are extracted. The two constructs demonstrate approximately 64.329% of the total variance, which is greater than 60%, indicating adequate construct validity [102]. In addition, fourteen capability factors were finally considered in the analysis, from which two constructs were extracted. The two constructs explain approximately 66.248% of the total variance. Finally, [PERSON]'s alpha reliability test was conducted to ensure that strategies and capability factors were appropriately grouped. Accordingly, all constructs have [PERSON]'s alpha value greater than the required minimum of 0.70 [103]. Tables 4 and 5 illustrate the EFA results and [PERSON]'s alpha values.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Constructs**} & \multirow{2}{*}{**Code**} & \multirow{2}{*}{**Factor Loadings**} & **Variance** & **Cronbach’s** \\  & & & **Explained (\%)** & **Alpha** \\ \hline \multirow{6}{*}{BIM Capability Requirement (BIMCR)} & SBIM5 & 0.637 & & \\ \cline{2-5}  & SBIM6 & 0.590 & & \\ \cline{2-5}  & SBIM7 & 0.648 & & \\ \cline{2-5}  & SBIM8 & 0.663 & & \\ \cline{2-5}  & SBIM9 & 0.765 & & \\ \cline{2-5}  & SBIM10 & 0.716 & & \\ \cline{2-5}  & SBIM14 & 0.560 & & \\ \hline \multirow{2}{*}{CCU} & SBIM2 & 0.721 & & \\ \cline{2-5}  & SBIM1 & 0.650 & 11.848 & 0.791 \\ \cline{2-5}  & SBIM3 & 0.742 & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of FA on strategies for improving OBIMC.