RT Journal Article
T1 Identifying employee engagement drivers using multilayer perceptron classifier and sensitivity analysis
A1 Núñez-Sánchez, José Manuel
A1 Molina-Gómez, Jesús
A1 Mercade-Mele, Pere
A1 Fernández Miguélez, Sergio Manuel
K1 Capacidad de trabajo - Evaluación
K1 Gestión de personal
AB Employee engagement is increasingly important, as it can become a competitive advantage for companies, helping them increase productivity, attract talent and improve customer satisfaction. Numerous works have studied the drivers that encourage employee engagement and have developed models to identify them. However, the existing models have limitations, and the literature demands more research on the subject since the precision of the models still needs to improve. This paper presents a computational model that can estimate the drivers of employee engagement accurately. A sample of 205 Spanish employees was used, allowing us to consider a wide sectorial heterogeneity. Different methods have been applied to the sample under study to achieve a high-precision model, selecting drivers using the Multilayer Perceptron Classifier and quantifying the impact of the drivers with Sensitivity Analysis. The results obtained in this research present important implications for the managerial improvement of human resources departments by facilitating the design of strategies and policies that foster employee engagement, which significantly influences corporate results.
PB Springer Nature
YR 2024
FD 2024-12-16
LK https://hdl.handle.net/10630/35835
UL https://hdl.handle.net/10630/35835
LA eng
NO Núñez-Sánchez, J.M., Molina-Gómez, J., Mercadé-Melé, P. , Fernández-Miguélez, Sergio M.(2024). Identifying employee engagement drivers using multilayer perceptron classifier and sensitivity analysis. Eurasian Bus Rev
NO Funding for open access publishing: Universidad Málaga/CBUA
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 21 ene 2026