## 2 Materials and Methods
### Employee Global Indicator in Industry 5.0  
The employee global indicator (\(EGI\)) integrates the \(WBI\) and the \(DSC\). Each component contributes equally, with both metrics ranging from 1 to 10. This means that both the \(WBI\) and \(DSC\) are weighted 50% in the calculation of the global indicator. The resulting \(EGI\) will also range from 1 to 10. A higher \(EGI\) indicates more favorable overall well-being and satisfaction, while a lower \(EGI\) signifies increased employee dissatisfaction. The \(EGI\) is computed using Equation (11):  
\[EGI_{i}=ROUND\left(\frac{WBI_{i}+DSC_{i}}{2}\right) \tag{11}\]  
Figure 7: Azure Text Analytics service configuration screenshot. where \(WBI_{i}\) is the Well-Being Index for day \(i\); \(DSC_{i}\) is the dissatisfaction score for day \(i\). The algorithm evaluates _EGI_ over ten days by analyzing the \(WBI\) and the sentiments expressed in the messages sent to colleagues (\(DSC\)). The final output is an _EGI_ that reflects the employee's overall state, focusing on their emotional well-being and the positivity of their communication. The \(WBI\) measures the employee's well-being on a scale from 1 to 10 daily. A higher \(WBI\) indicates a better state of well-being. The \(DSC\) assesses the negativity of messages sent by the employee compared to the total number of messages sent. It is also scaled from 1 to 10, where a higher score indicates a more positive communication style. The employee is evaluated over the last ten days, and if the calculated _EGI_ exceeds a specified threshold of 4, a KPI evaluation is generated. This KPI is used to assess performance, well-being, and potential areas for improvement. This intervention attitude is a key factor in Industry 5.0. Figure 8 presents the logic diagram of the Employee KPI Algorithm (EKA), including the _EGI_. The flowchart in Figure 8 summarizes the steps involved in the EKA, showing how data are collected, processed, and evaluated to generate the final KPI. This algorithm presents a framework for assessing employee well-being by integrating self-reported metrics and communication analysis in the context of Industry 5.0. It can provide valuable insights into the work environment and help identify employees who need support. Our proposed framework is the first in the literature to integrate the \(WBI\) and \(DSC\), offering a customized assessment of employee well-being that has improved upon existing methods. Unlike traditional KPI indicators, which may provide only general or fragmented insights, our framework introduces uniquely tailored metrics that capture the organiza  
Figure 8: Bloc diagram of the EKA. tion's commitment to employee care and satisfaction. Furthermore, the model's novelty is underscored by the distinct calculation formula of the WBI. This innovative approach makes our framework a one-of-a-kind solution in current well-being research. The proposed framework aligns with Industry 5.0's human-centric values by prioritizing well-being as a core component of workplace assessment. Unlike previous industry paradigms, Industry 5.0 emphasizes employee well-being, satisfaction, personal development, and productivity. This framework captures holistic well-being by integrating physiological data and sentiment analysis, giving equal attention to mental and physical health. Including real-time well-being tracking and _EGI_ assessments enables proactive interventions, promoting a supportive work environment--a key goal of Industry 5.0. To implement the EKA, organizations could start by integrating wearable devices and text analytics tools to capture relevant data. Regularly monitoring _EGI_ scores and setting automated alerts for scores below a specific threshold would enable timely interventions. Collaborating with Human Resources (HR) and wellness experts to address KPIs when the _EGI_ declines can help ensure appropriate, targeted support.