# 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  
Generally, PLS-SEM is a variance-based structural equation modeling technique that creates linear combinations of the indicators (CCV factors) and subsequently estimates the model parameters using the ordinary least square algorithm [41]. PLS-SEM components are indicators (CCV factors) and latent groups (CCV-groups). The PLS-SEM model consists of the measurement and structure models, as shown in Figure 2.  
The measurement model displays the relationship between the latent group and its indicators (CCV factors). Two indicators are used in PLS-SEM: reflective indicators (arrows from the latent group to its indicators) and formative indicators (arrows from indicators to their latent group). Reflective indicators are assumed to be caused by the latent group construct they measure. Formative indicators are believed to cause the latent construct they are measuring. Reflective indicators are typically used for constructs that consider latent variables that cause the observed indicators. On the other hand, formative indicators are used when the indicators are seen as distinct factors that define the latent construct. [PERSON] et al. [24] argued that the mode of the measurement model, 75.89% of these models, employed only reflectively measured constructs, whereas 11.35% employed both reflective and formative measures. Only a few reviewed PLS-SEM applications included latent group variables with only formative measurement models (9.22%); they also stated that most construction studies utilized reflective indicators. Hence, the use of type indicators in this paper was reflective. The second model (structure model) represents the relation diagram among the latent group and examines the hypothesis relationships. There are two types of latent groups in PLS-SEM: exogenous and endogenous latent group. An exogenous latent group is often considered the independent group in the model and is used to explain the variance in endogenous variables. On the other hand, the endogenous group is specified to have one or more causal paths leading to them from other groups within the model. The endogenous group is often considered the dependent variable in the model, influenced by the exogenous groups and other endogenous groups. The latent group (EP, IQ, CP, PC, or EF) can be classified as an exogenous and endogenous latent group. The exogenous latent group is an independent latent group that changes other latent groups' values within the causal model. For example, CP and EF are exogenous latent groups, which cause PC value change as the  
Figure 1: Methodology components. arrow directed from CP to PC in Figure 2. Also, EF causes a change to PC, as indicated in Figure 2, through the arrow emitted from EF to PC. On the other hand, the endogenous is a latent group directly or indirectly influenced by the exogenous latent group. For instance, the PC is an endogenous latent group influenced by EF and CP, as shown by two arrows received in PC from EF and CP in Figure 2.  
After defining all the latent groups and their indicators of the causal model, all relationships among the latent groups need to be evaluated. The primary purpose of the PLS-SEM is to examine the existence of hypothesis relationships among the latent groups. Therefore, the hypothesis relationships between all latent groups were first created. Following a particular methodology, including expert logic, has eliminated these hypotheses' logic. At this point, the assessment of measurement and structure model are followed to test these hypothesis relationships (accept or reject the relationship). The following explanation of these two assessment models is shown in Figure 3. It is noted that the measurement assessment model was first performed, followed by the structure model assessment. Figure 2: Illustrative example of the PLS-SEM model. The measurement model assessments aim to identify the necessary indicators and remove the insignificant ones.