APPLICATION OF NEURAL NETWORKS FOR THE IDENTIFICATION OF PSYCHOLOGICAL PREDICTORS OF EFFECTIVE LEADERSHIP - WILSON ALEJANDRO FLORES ORTIZ , NANCY JACHO-GUANOLUISA , LIZETH SOLANO-ROMO (TPM – Testing, Psychometrics, Methodology in Applied Psychology, 2025)
The advancement of artificial intelligence has made it possible to develop sophisticated predictive models in the field of organizational psychology, particularly in the study of leadership. Recent research indicates that trait emotional intelligence, psychological capital, personality, and dark traits explain relevant differences in transformational leadership and leader performance (Islam, Prieto, & Talukder, 2025; Schreyer, Plouffe, Wilson & Saklofske, 2023; Zadorozhny, Petrides, Cheng, Cuppello & van der Linden, 2025). In parallel, deep learning models have demonstrated a high capacity to detect personality traits and complex behavioral patterns (Naz et al., 2025; Naz et al., 2025). The aim of this study was to evaluate the ability of an artificial neural network to predict effective leadership, based on individual psychological factors, and to identify the most important predictors. We worked with a sample of 320 middle and senior managers of service organizations (M_edad = 38.7 years, SD = 8.9), who answered effective leadership scales (360° evaluation), emotional intelligence trait, psychological capital, personality traits and Dark Tetrad traits. A logistic regression model, a random forest and a multilayer neural network were compared with cross-validation. The results show that the neural network obtained the best predictive capacity (accuracy = .82; AUC = .88), outperforming logistic regression (accuracy = .71; AUC = .79) and the random forest (accuracy = .78; AUC = .85). The analysis of variable importance and explainability of the model indicated that trait emotional intelligence (particularly sociability and self-control), self-efficacy, conscientiousness, openness to experience, and low levels of psychopathy were the strongest predictors of effective leadership. The findings support the use of explainable neural networks as a complementary tool for the identification and development of managerial talent, although ethical implications and the need for longitudinal studies are underlined.