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Combining Virtual Reality and Machine Learning for Leadership Styles Recognition.

Elena Parra1, Aitana García Delgado1, Lucía Amalia Carrasco-Ribelles1,2

  • 1Institute for Research and Innovation in Bioengineering, Polytechnic University of Valencia, Valencia, Spain.

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Summary

This study shows that machine learning (ML) and virtual reality (VR) can identify leadership styles by analyzing eye movements and behaviors in virtual workplaces. Eye-tracking data proved more effective than behavioral metrics for this classification.

Keywords:
eye-trackingleadershipleadership style recognitionmachine learningvirtual reality

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Area of Science:

  • Psychology
  • Computer Science
  • Human-Computer Interaction

Background:

  • Traditional personnel selection methods may not capture nuanced leadership qualities.
  • Virtual reality (VR) offers immersive environments for behavioral assessment.
  • Machine learning (ML) can analyze complex datasets for predictive insights.

Purpose of the Study:

  • To evaluate a novel selection procedure combining ML and VR for leadership style assessment.
  • To classify individuals' leadership styles using decision-making behaviors and eye-gaze patterns in virtual social workplace scenarios.
  • To determine the efficacy of implicit measures (behavior and gaze) in predicting leadership styles.

Main Methods:

  • Developed immersive virtual environments simulating social workplace situations using an evidence-centered design.
  • Recorded interaction and gaze patterns from 83 subjects during VR immersion.
  • Assessed leadership styles using the Multifactor Leadership Questionnaire (high vs. low).
  • Created an ML model integrating behavioral outputs and eye-gaze data to predict leadership styles.

Main Results:

  • Distinct leadership styles (high vs. low) were successfully differentiated by eye-gaze patterns and behaviors within VR.
  • Eye-tracking measures were found to be more significant differentiators than behavioral metrics.
  • The ML model demonstrated the potential to predict leadership styles based on VR-derived implicit measures.

Conclusions:

  • The combination of VR, implicit measures, and ML shows promise for future personnel selection.
  • Eye-gaze patterns in VR are a valuable indicator of leadership style.
  • Further research with larger sample sizes is needed to generalize findings.