Detecting inhibition and activation tendencies in organizational behavior: a virtual reality and machine learning-based methodological framework
[EN] This study exploits technological and computational strategies to examine, through a novel methodological framework, motivational dynamics concerning organizational behavior. Drawing on the Reinforcement Sensitivity Theory, a Virtual Reality Organizational Environment (VROE) integrating eye-tra...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglés |
| OAI Identifier: | oai:riunet.upv.es:10251/225492 |
| Acceso en línea: | https://riunet.upv.es/handle/10251/225492 |
| Access Level: | acceso embargado |
| Palabra clave: | Organizational behavior Virtual reality Machine learning BIS/BAS |
| Sumario: | [EN] This study exploits technological and computational strategies to examine, through a novel methodological framework, motivational dynamics concerning organizational behavior. Drawing on the Reinforcement Sensitivity Theory, a Virtual Reality Organizational Environment (VROE) integrating eye-tracking and decision-making metrics was implemented to differentiate individuals with high and low Behavioral Inhibition (BIS) and Behavioral Activation (BAS) systems. A machine learning (ML) approach was used to analyse data from 68 participants in Spain. The results indicated moderate to high discriminative accuracy for BAS identification, achieving up to 75% predominantly through the analysis of eye-tracking data in form of inclusive and averted gaze patterns. The ML models demonstrated a slight capability for BIS, with an accuracy of 67%. The findings underscore the potential of theory-based applications integrating virtual reality and machine learning to yield motivational insights within organizational settings. |
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