## 4 Results and Discussion
### Conceptual Framework and Validation  
The conceptual framework was constructed to depict the critical hindrances to BIM implementation and the hypothetical influence paths among the hindrances groupings (Figure 2). Three hypotheses were involved in the framework:  
**Hypothesis 1**. : _Lack of executive vision and training positively contributes to lack of continuous involvement and capabilities._  
**Hypothesis 2**. : _Lack of continuous involvement and capabilities positively contributes to lack of collaboration and model integration; and_  
**Hypothesis 3**. : _Lack of executive vision and training positively contributes to lack of collaboration and model integration._  
Structural equation modeling (SEM) has been recognized as a good technique in terms of relationship analysis among variables [42; 58]. A SEM model includes structural and measurement  
Figure 2: Conceptual framework to reduce critical hindrances for BIM implementation. models. The measurement model presents the relationships between measured variables (the critical hindrances) and latent variables or constructs (the hindrance groupings), and the structural one specifies the relationships among the three hindrance groupings. Two types of SEM are commonly-used: covariance-based SEM (CB-SEM) and partial least-squares structural equation modeling (PLS-SEM). Compared with CB-SEM, PLS-SEM: (1) ensures a good statistical power when the sample size is small [59]; (2) can deal with non-normal data sets [60; 61]; and (3) can identify key driving constructs [62]. In this study, PLS-SEM was used as the sample size (87) tended to be inadequate for CB-SEM, and this study primarily aimed to investigate the intergroup relationships and driving hindrance groupings. In addition, this approach was widely used in project management research conducted both in Singapore [42; 58] and globally [63; 64]. SEM can efficiently fit the data and the SEM model. This was because confirmatory factor analysis (CFA) and path modeling analysis were simultaneously conducted. CFA was performed to test whether the 21 critical hindrances could represent well the three hindrance groupings. Furthermore, to check whether the path coefficients (related to hypotheses) were statistically significant, the bootstrapping technique [65] was applied in this study. [PERSON] et al. [62] suggested that 5000 bootstrap subsamples should be used, which were randomly drawn from the 87 data sets. The PLS-SEM analysis results required the following interpretations: (1) reliability and validity testing; and (2) relationships assessment according to the path coefficients obtained from the analysis. The reliability and validity of the measurement model should be assessed using the indicators and their thresholds: (1) 0.70 for the Cronbach's alpha coefficients [47]; (2) 0.55 for the factor loadings of the measured variables [66]; (3) 0.50 for the average variances extracted (AVEs) of the latent variables [67]; and (4) 0.70 for composite reliability (CR) scores [62]. In addition, discriminant validity should be assessed to test a construct's distinction from others. Two traditional methods were widely used in previous studies: (1) the Fornell-Larcker criterion that the square root of the AVE of each construct should exceed the correlation between this construct and any other constructs [67]; and (2) the cross-loading assessment that a measured variable's factor loading on its respective construct should be greater than its cross-loadings [68]. However, [PERSON] et al. [69] found that the two traditional assessment methods are not reliable in terms of detecting the lack of discriminant validity in PLS-SEM. This is because such methods would result in an unacceptably low sensitivity, especially in the situation that the AVE is low. Instead, this said study proposed that heterotrait-monotrait ratio of correlations (HTMT) should be a new approach. The HTMT is the average of the heterotrait-heteromethod correlations (the correlations of the measured variables across the constructs that measure different phenomena).