# Dynamic BIM Adoption Impact on Contract Cost Variance Factors Using PLS-SEM Techniques
## 3 Methodology
### Develop the Causal Model Using the PLS-SEM Model (Component 1)
#### 3.1.1 Procedure of PLS-SEM Assessment
This assessment can be performed by examining the impact of changing the indicator's value to their latent group. This impact can be measured using two validity approaches: construct and reliability validity and discriminant validity. The construct and reliability validity approach used three measurement coefficients: Cronbach's alpha, composite reliability, and average variance extracted (AVE). The three coefficients are based on the outer loading of the indicator (factors) and should be more than the threshold value (more than 0.7 for Cronbach's alpha and composite reliability; more than 0.5 for AVE). The outer loading of the indicator (\(l\)) shall be more than or equal to 0.7. On the other hand, there are two cases when \(l\) is smaller than 0.7 for assessment _construct and reliability_. Case 1 represents the indicator's outer loading value smaller than 0.4. Therefore, the indicator should be eliminated from the model. Case 2 represents the indicator with an outer loading of more than 0.4 and less than 0.7. Thus, in such a case, the elimination of the indicator has no effect on increasing any of the three coefficients (\(\alpha\), \(CR\), and \(AVE\)) of its construct (group). Therefore, the indicator should remain in the model. Moreover, suppose the omitted indicator leads to an increase in any of the three coefficients of its construct. In that case, the indicator will be deleted from the model (the indicator is nonsignificant) [23]. As a result, PLS-SEM additionally uses composite reliability to assess the constructs' internal consistency dependability [42]. The discriminant validity approach examines if a latent group is unique from other latent groups. The overall goal of this validity approach is to remove the insignificant indicators. Two critical metrics can measure the uniqueness among latent groups: the Cornell-Larcker criterion and cross-loadings. The Cornell-Larcker criterion ensures whether a latent group shares more variance than another latent group variable. Accordingly, each latent group's square root of its \(AVE\) should be higher than its highest correlation with other latent groups [23]. Regarding the cross-loadings, an indicator's loading with the related latent group should be more significant than its loadings with all the other latent groups [23]. Structure Model Assessment  
The structural assessment model aims to test whether the hypothesis relationships are accepted or rejected. This assessment model can be achieved by examining the relation between two latent groups by determining the b coefficient or \(p\)-value. These coefficients and values indicate whether the relation exists or not. Refer to the reference for more details on calculating b and p. [42]. If p is less than 10%, then the alternative hypothesis (b \(\
eq\) 0, there was a relationship between the two latent groups) is accepted. Otherwise, the null hypothesis (b = 0, no relationship between the two latent groups) is accepted. Figure 3: Evaluation PLS-SEM model.