## 5 Analysis and Findings
### Exploratory Data Analysis
The whiskers of the boxplots, which indicate variability outside the upper and lower quartiles, extend further for Diploma holders. This indicates more variance in perceptions of the benefits of green logistics among this group compared to those with higher education levels. When examining the intention to adopt green logistics across different education levels, it is apparent that this intention does not vary as significantly as perceived benefits do. This suggests that regardless of educational attainment, the intention to adopt green logistics may be influenced by factors other than education. For Diploma holders, the IQR is broader, showing more diversity in their intentions to adopt green logistics. In contrast, the IQR for Bachelor's degree holders and those with a Master's degree or higher is narrower, indicating more agreement within these groups. Similar to the perceived benefits, the whiskers for Diploma holders indicate greater variance in the intention to adopt green logistics, suggesting that individuals with this level of education have a wider range of intentions regarding the adoption of green logistics practices. The analysis of the impact of education level on the perceived benefits and intention to adopt green logistics reveals some combined observations. Education appears to have a more pronounced effect on the perceived benefits of green logistics than on the intention to adopt such practices. This discrepancy may arise because education can enhance awareness and the perceived importance of green practices, while the actual intention to adopt may be more influenced by factors like organizational policies, individual values, or professional responsibilities, which are not directly tied to formal education levels. Across both variables, as the education level increases, variability seems to decrease, suggesting a convergence of attitudes with higher education. However, the median values indicate that higher education does not necessarily equate to stronger intentions to adopt green logistics. This observation suggests that other practical considerations, such as company resources and individual capacity to influence change, might play a more crucial role in determining whether an individual intends to adopt green logistics practices, regardless of their education level. The data suggests that while education may enhance awareness and perceived benefits of green logistics, it does not necessarily translate directly into the intention to adopt these practices. For organizations aiming to promote green logistics, this could imply that educational initiatives should be complemented by other supports, such as providing resources or incentivizing adoption, to effectively translate awareness into action. Figure 7 analyzes the perceived benefits of green logistics based on annual revenue and reveals that companies with \"Less than USD 1 million\" and \"More than USD 1 billion\" annual revenue have lower median perceived benefits compared to those in the middle revenue categories, such as \"1 million-10 million\" and \"10 million-100 million\". Figure 5: Boxplot of the perceived benefits according to education level. Figure 6: Boxplot of Intention to adopt green logistics according to education level. The \"1 million-10 million\" revenue category exhibits the smallest IQR, indicating a more consistent perception of benefits within this group, while larger companies with \"More than USD 1 billion\" in revenue have a wider IQR, suggesting more variability in their perceptions. There are significant outliers in both the smallest and the largest revenue categories, indicating that companies at both ends of the revenue spectrum have perceptions that greatly differ from the median. The longer whiskers in the \"Less than USD 1 million\" category suggest that perceptions of benefits vary widely among these smaller companies, with variance diminishing as revenue increases, up until the \"More than USD 1 billion\" category, where the variance in perception increases again.