## 5 Analysis and Findings
### Exploratory Data Analysis  
In this section, we delve into the process of scrutinizing and interpreting the data set through exploratory techniques, aiming to extract insights and understand the dynamics between the studied elements. This segment outlines the methodologies utilized in examining the data collected from respondents in Saudi Arabia concerning the seven variables under investigation. Commencing with descriptive analytics, we assess the correlation coefficients to discern the nature and intensity of associations between paired variables, which can range from \(-1\) to \(1\). Concerning the nature of these connections, a positive coefficient signifies a direct relationship wherein both variables move conjointly. Conversely, a negative coefficient indicates an inverse relationship, with the variables moving in opposition. As for the intensity, a coefficient nearing the extremities of \(-1\) or \(1\) denotes a more potent link between the variables. By contrast, a coefficient proximate to zero suggests a lack of interdependence, meaning that changes in one variable do not systematically relate to shifts in the other. To quantify these correlations, we employ the Pearson Correlation Coefficient (PCC), which visually depicts the correlations among the studied factors that may affect the adoption of green logistics. The outcomes of this computation for all seven variables are represented in Figure 2.  
The matrix analysis provides insights into the relationships between factors influencing the adoption of green logistics. Environmental Awareness is weakly correlated with other factors, suggesting that respondents' awareness of environmental issues does  
Figure 2: The correlation coefficient matrix. not strongly relate to their views or practices concerning the other factors measured. In contrast, Organizational Culture exhibits a moderate positive correlation with Environmental Awareness, indicating that organizations with a culture attuned to environmental issues tend to have slightly higher awareness of environmental concerns. However, Organizational Culture has a small negative correlation with the Intention to Adopt Green Logistics, suggesting that organizational culture may not be a strong driver of the intention to adopt green logistics in this dataset. Technological Readiness and Government Regulation demonstrate a high positive correlation, indicating that they likely move in tandem. This suggests a synergistic relationship between regulatory frameworks and technological preparedness, where government regulations support technological readiness. On the other hand, Financial Considerations display very low correlation values with all factors, indicating that financial aspects may not be the primary determinant of the adoption of green logistics practices or the development of related factors. The correlations between Perceived Benefits and various factors are generally weak, suggesting that Perceived Benefits may not be a strong driver of these factors in the context of green logistics adoption. Specifically, the correlation between Environmental Awareness and Perceived Benefits is very weak, indicating a slight increase in perceived benefits as environmental awareness increases. The relationships between Organizational Culture, Technological Readiness, Government Regulation, and Perceived Benefits could not be determined due to the absence of displayed correlation values in the provided data. The correlation between Financial Considerations and Perceived Benefits is negligible, suggesting no practical linear relationship. Regarding the intention to adopt green logistics, Government Regulation, Financial Considerations, and Perceived Benefits show very low positive correlations, indicating negligible linear relationships. This suggests that these factors may not be strong drivers of the intention to adopt green logistics within this dataset.