Exploring neural networks in the analysis of variables that affect the employee turnover in the organization

The phenomenon of job satisfaction generates high costs for organizations, as it impacts on the processes of selection, training, and motivation of their human resources, while affecting the productivity and quality of organizations, even impacting on the loyalty of costumers. For this reason, forecasting or controlling the behavior of the staff turnover is of great importance for a company. However, predicting the behavior of turnover is an almost impossible intention due to the number of variables that condition this behavior. The objective of this research was to try to identify, through the use of neural network analysis, which internal variables of the organization, of an objective nature, of a demographic nature and associated with their human resources, showed a relationship or incidence on the employee turnover. For this purpose, the databases were analyzed with the turnover behavior of personnel in business organizations with different characteristics. The analysis through neural networks allowed to establish a significant relationship between variables such as average income, school level, and age; likewise, no significant differences were found in other variables such as the type of sector, years of experience in the sector, years of work in the position or years of work, the hierarchical position occupied in the organization, and the number of dependents.