Combining Virtual Reality and Machine Learning for Leadership Styles Recognition - Parra, Elena; García Delgado, Aitana; Carrasco-Ribelles, Lucía Amalia; Chicchi Giglioli, Irene Alice; Marín-Morales, Javier; Giglio, Cristina; Alcañiz Raya, Mariano (Frontiers in Psychology, 2022)
The aim of this study was to evaluate the viability of a new selection procedure based on machine learning and virtual reality. Specifically, decision-making behaviours and eye-gaze patterns were used to classify individuals based on their leadership styles while immersed in virtual environments that represented social workplace situations. The virtual environments were designed using an evidence-centred design (ECD) approach. Interaction and gaze patterns were recorded in 83 subjects, who were classified as having either high or low leadership style, which was assessed using the Multifactor Leadership Questionnaire (MLQ). A machine learning model that combined behaviour outputs and eye-gaze patterns was developed to predict subjects’ leadership styles (high vs low). The results indicated that the different styles could be differentiated by eye-gaze patterns and behaviours carried out during immersive virtual reality. Eye-tracking measures contributed more significantly to this differentiation than behavioural metrics. Although the results should be taken with caution as the small sample does not allow generalization of the data, this study illustrates the potential for a future research roadmap that combines virtual reality, implicit measures, and machine learning for personnel selection.