## 4 Case Study
### Visualization and Discussion of Results  
The network of relations is represented in Figure 3.  
As it is possible to observe, the map was programmed to show as many layers as the values assumed by TE that are present in the TE data frame, which is two in our case, i.e., 0.3125 (associated with the linguistic evaluation \"VH\") and 0.229167 (associated to the linguistic evaluation \"M\"). This is another significant upgrade concerning paper [3] where, apart from leading calculations in a traditional way without implementing a comprehensive recommendation system, we generated the final network by using the MentalModeler software. While MentalModeler provides valuable insights, it has certain limitations that merit consideration. A significant limitation is its inability to automatically differentiate between distinct layers within the network, each representing various evaluations. This means that users are required to organize and adjust the network nodes manually to reflect these different evaluation layers. Additionally, the visualization generated by MentalModeler does not employ a color-coding approach to enhance clarity. On the contrary, our visualization approach uses a distinct color to highlight the arrows representing connections associated with the highest TE value. This color differentiation serves as a visual aid to draw attention to the most influential relationships within the network, making them easier to identify and interpret. In reviewing the results, it becomes evident that occupational stress is a significant concern, particularly in workplaces characterized by high-risk dynamics. This stress can significantly affect both the wellbeing and performance of employees. Our comprehensive analysis, conducted using FCM, has endowed us with an in-depth understanding of the principal factors responsible for contributing to stress levels at the specific Italian airport object of this case study. This approach, rooted in the versatile FCM methodology, allows for insights into the complex nature of stress-inducing elements within the airport's operational framework. As it can be observed in Figure 3, the FCM analysis has revealed three critical occupational stressors of paramount concern:  
* OSR9. Social and personal life limitations stemming from working during weekends, holidays, or night shifts  
Figure 3: Network of relationships. Figure 2: Final recommendation. * OSR10. High turnover rates due to insecurity that obstruct sustainable practices
* OSR12. Insecure employment undermining employee wellbeing and hindering airport sustainability  
Responding to these findings, our recommendation system automatically generates the set of related measures which had been previously formalized in Table 1 (refer to Figure 2) to be prioritized to mitigate these stressors and cultivate a more supportive and healthier work environment for airport employees. Our core objective is to improve employee wellbeing and performance by systematically addressing these challenges based on evidence. It is essential to note that the successful implementation of these recommended measures depends on the company and its available budgetary resources. While our analysis and recommendations offer a structured, evidence-backed approach to address occupational stressors, the company's commitment to allocate the necessary financial resources will be pivotal in ensuring the effective execution of these measures. Aligning budgetary allocations with these priorities empowers the company to take significant steps toward establishing a more productive and healthier workplace for its employees. Implementing the recommendations can reduce stress levels among airport employees, leading to improved wellbeing, job satisfaction, and overall performance, as outlined below. * M9.1. Flexible scheduling and shift rotation: implementing flexible scheduling and shift rotation can help employees achieve a better work-life balance.