## 4 Conclusions  
This study was employed for the evaluation of the firm performance and employee wellbeing parameters using the ANN-ICA technique. Outputs of the models have been compared using evaluation criteria with the target values. In the second phase, the effect of each independent category was considered compared to the output values to find the most effective variables. According to the findings, the model architecture of 11-30-20-10-2 (11 inputs interconnected with 30 neurons in the first hidden layer, 20 neurons in the second hidden layer, 10 neurons in the third hidden layer, and 2 outputs) was selected as the best model for the prediction of the firm performance and employee wellbeing with the lowest MAPE. According to the findings, it can be mentioned that, when predicting company success (firm performance) using working organizations, the mean value of linearity was around 74%, which is about 10% higher than that for predicting employee wellbeing. This pattern showed that the impact of work organization-related characteristics on firm performance was around 15% greater than the impact of these same factors on employee wellbeing. The mean value of linearity, on the other hand, was around 67% for forecasting firm performance using skill  
Figure 12: Analyzing the effects of independent parameters on output values.  
needs and skill matches, which was roughly 19% lower than that for predicting employee wellbeing. This tendency indicates that the influence of the qualities linked to the skills needed and skill matching on firm performance was approximately 22% less than the impact of the same characteristics on employee wellbeing. Additionally, based on employee feedback, the mean linearity for predicting firm performance was about 94%, which was around 7% higher than that for predicting employee wellbeing. This trend showed that the impact of characteristics linked to employee voice on firm performance was around 8% more than the impact of the same characteristics on employee wellbeing. Furthermore, the average linearity for forecasting firm performance using the external environment was about 94%, which was roughly 42% higher than that for forecasting employee wellbeing. According to this pattern, the influence of factors connected to the external environment on a company's success was almost 80% bigger than its influence on employee wellbeing. It would be exciting to use this method (ANN-ICA) to identify the cross-country differences covering EU-27 countries involved in the ECS 2019 survey. It would be exciting to locate country group differences (e.g., Nordic countries, continental countries, Mediterranean countries, etc.).  
Conceptualization, A.M., C.M. and M.I.; methodology, S.A.; software, S.A. and A.M.; validation, S.A. and A.M.; formal analysis, S.A. and A.M.; investigation, S.A., C.M. and A.M.; resources, M.I., J.P. and Z.D.; data curation, S.A., J.P. and A.M.; writing--original draft preparation, S.A., J.P. and A.M.; writing--review and editing, S.A., C.M. and A.M.; visualization, S.A. and A.M.; supervision, S.A., B.T. and A.M.; project administration, J.P. and Z.D.; All authors have read and agreed to the published version of the manuscript.  
The research was supported by the European Union within the framework of the RRF-2.3.1-21-2022-0004 Artificial Intelligence National Laboratory Program.  
Not applicable.  
The authors declare no conflict of interest.