[EN] The aim of this study was to evaluate the viability of a new selection procedure based on machine learning (ML) and virtual reality (VR). 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 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. A ML 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 VR. Eye-tracking measures contributed more significantly to this
ifferentiation than behavioural metrics. Although the results should
be taken with caution as the small sample does not allow eneralization of the data, this study illustrates the potential for a future research roadmap that combines VR, implicit measures, and ML for personnel selection.This work was co-founded by the European Union through the Operational Program of the European Regional Development Fund (FEDER) of the Valencian Community 2014-2020 (IDIFEDER/2018/029). This work is part of the "Rebrand" project with reference PROMETEU/2019/105 funded by the Generalitat Valenciana.Parra Vargas, E.; García-Delgado, A.; Carrasco-Ribelles, LA.; Chicchi Giglioli, IA.; Marín-Morales, J.; Giglio, C.; Alcañiz Raya, ML. (2022). Combining Virtual Reality and Machine Learning for Leadership Styles Recognition. Frontiers in Psychology. 13:1-15. https://doi.org/10.3389/fpsyg.2022.8642661151
