## 4 Data Analysis and Findings
### Discriminant Validity  
Cross Loading and Fornell-Larker Criterion  
Discriminant Validity testing ensures that a variable is unique from other variables in the proposed model. It is defined as the degree to which a variable is distinct from other  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Indictors** & **Questionnaire Statements** & **(\(\lambda\geq\) 0.7)** \\ \hline PT2 & If I was given BIM project delivery guidance and training, I would find BIM easy to implement. & 0.831 \\ \hline PT3 & If I was given BIM project delivery guidance and training, further BIM-related learning becomes easier for me. & 0.896 \\ \hline PU1 & Using BIM software and guidelines in my job will increase my productivity. & 0.947 \\ \hline PU2 & Using BIM software and guidelines in my job will improve my performance. & 0.894 \\ \hline PU3 & Using BIM software and guidelines in my job will enhance my effectiveness. & 0.962 \\ \hline PU4 & Using BIM software and guidelines in my job is more useful than using CAD. & 0.825 \\ \hline SF1 & I prefer BIM because of the proportion of colleagues who use it. & **0.593** \\ \hline SF2 & The senior management in this organisation is helpful when implementing BIM. & 0.744 \\ \hline SF3 & My Line Manager advocates BIM implementation. & **0.647** \\ \hline SF4 & In general, the organisation has Adopted BIM. & **0.67** \\ \hline TT1 & If I was given technical training, it would be easy for me to become skilful using BIM software. & 0.928 \\ \hline TT2 & If I was given technical training, I would find BIM software easy to use. & 0.865 \\ \hline TT3 & If I was given technical training, further learning becomes easier for me. & 0.874 \\ \hline UC1 & I could complete a job or task using BIM software with minimal support from others. & 0.892 \\ \hline UC2 & I could complete a job or task using BIM software if I could call someone for instant help if I got stuck. & 0.868 \\ \hline UC3 & My perception of BIM for project delivery is clear and understandable. & **0.616** \\ \hline \hline \end{tabular}
\end{table}
Table 2: \(Cont.\)  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variables** & **Code** & \(\rho\)c \(\geq\) 0.6 & \(\Sigma\)\(\lambda^{2}\) \textgreater{} 0.5 \\ \hline
**Attitude** & AT & 0.904 & 0.76 \\ \hline
**BIM Uptake** & BU & 0.911 & 0.774 \\ \hline
**Behavioural Intention to Use** & BI & 0.969 & 0.913 \\ \hline
**CI/Kaizen** & CI & 0.961 & 0.891 \\ \hline
**Knowledge Acquisition** & KA & 0.902 & 0.754 \\ \hline
**Lean** & LN & 0.901 & 0.647 \\ \hline
**Organisational Learning** & OL & 0.917 & 0.735 \\ \hline
**Organisational Support** & OS & 0.901 & 0.646 \\ \hline
**Perceived Ease of Use** & PE & 0.929 & 0.813 \\ \hline
**Perceived Usefulness** & PU & 0.95 & 0.826 \\ \hline
**Process Training** & PT & 0.906 & 0.762 \\ \hline
**Social Factors** & SF & 0.794 & 0.562 \\ \hline
**Technical Training** & TT & 0.919 & 0.791 \\ \hline
**User Competency** & UC & 0.904 & 0.825 \\ \hline \hline \end{tabular}
\end{table}
Table 3: AVE and composite reliability results.  
variables by measuring the correlations between overlapping indicators using two testing methods: Cross Loadings and the Fornell-Larcker criterion [47]. In the first method, an indicator's outer loading on the associated variable should be greater than any of the other cross-loadings on the remaining variables. Cross Loadings are best reported in a table where a successful indicator receives the highest loading in the same row. Alternatively, for the second method, the Fornell-Larcker criterion requires the square root of each variable's AVE to be greater than the correlation value of any other variable. Accordingly, no conflicts were reported from either the Cross Loadings or Fornell-Larker assessments (Tables 4 and 5).