## 5 Analysis of the Data and Results
### Structural Model (Path Analysis)  
Linear regression statistical analysis is done using paths. In social science and management, path analysis is the most used analytical method. Corresponding to this, path analysis is a commonly used technique for simultaneously evaluating all complex relationships [70]. Structural equation modelling is used in the first stage of the SEM study. Analyzing the interrelationships between the research constructs can be done using the architectural framework. Once the model has been fitted, the structural equation model is the next significant stage in SEM analysis. It is possible to use the structural model by identifying relationships between the variables. The links between the variables are elucidated. The data illustrates the connection between endogenous or dependent factors  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Construct** & **Poor Governance** & **Safety Consciousness** & \
\begin{tabular}{c} **Poor Working** \\ **Conditions** \\ \end{tabular} &
\begin{tabular}{c} **A Lack of Industry** \\ **Standards** \\ \end{tabular} \\ \hline Poor governance & 0.86 & & & \\ Safety consciousness & 0.773 & 0.838 & & \\ poor working conditions & 0.742 & 0.781 & 0.765 & \\ Unsupportive industry norms & 0.718 & 0.721 & 0.711 & 0.857 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Correlation of latent variables and discriminant validity (Fornell-Larcker).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Barriers** & \begin{tabular}{c} **Unconducive Work** \\ **Climate** \\ \end{tabular} & \
\begin{tabular}{c} **Poor** \\ **Goverance** \\ \end{tabular} &
\begin{tabular}{c} **Safety Awareness** \\ **Industry Norms** \\ \end{tabular} \\ \hline B2 & 0.845 & 0.697 & 0.721 & 0.633 \\ B4 & 0.663 & 0.402 & 0.436 & 0.389 \\ B8 & 0.777 & 0.562 & 0.597 & 0.577 \\ B11 & 0.612 & 0.876 & 0.706 & 0.617 \\ B7 & 0.685 & 0.897 & 0.677 & 0.681 \\ B1 & 0.615 & 0.804 & 0.61 & 0.549 \\ B10 & 0.636 & 0.692 & 0.833 & 0.568 \\ B6 & 0.668 & 0.596 & 0.856 & 0.661 \\ B9 & 0.659 & 0.656 & 0.826 & 0.584 \\ B12 & 0.59 & 0.632 & 0.656 & 0.863 \\ B5 & 0.629 & 0.598 & 0.578 & 0.851 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Cross loading analysis.  
and independent and exogenous variables [71; 72]. The total model fit is considered in the assessment of the structural design after the magnitude, direction, and applicability of the assumed parameters [71]. Based on the research theories shown in Figure 1, the final step was verifying the suggested study relationship. SEM was employed for the research hypothesis. In Figure 4, the corresponding research hypothesis model is displayed. The importance of the hypothesis for the model was assessed as part of the bootstrapping approach. The bootstrapping procedure is used in conjunction with the random resampling of the original dataset to create fresh samples that are the same size as the entire data. The accuracy of the generated path coefficients is therefore verified using this procedure, which additionally examines the data set's validity and statistical significance [73]. The pathway importance, with \(p\)-values for each path, is depicted in Figure 4 together with the standardised path coefficients (\(\beta\)) and p-values. Four hypotheses had a statistically significant impact on the findings.