@misc{10481/110268,
year = {2026},
month = {1},
url = {https://hdl.handle.net/10481/110268},
abstract = {Energetic Intelligence is a newly defined construct recently validated that offers a thorough understanding of human intelligence by integrating, emotional, spiritual, and physical dimensions. This study applies a data-driven approach to predict Energetic Intelligence. Our research goal lies in the application ofmachine learning techniques to model and predict Energetic Intelligence using data commonly gathered in organizational contexts. We collected responses through structured surveys and employed a range of supervised learning algorithms to build predictive models. Model performance was evaluated using standard metrics, with the best results reaching an R2 of 0.73 through optimized and simplified models, which is a promising outcome for a psychologically grounded prediction task. Key predictors included variables such as Flow, Flourishing, and Emotional Vitality, which consistently emerged as relevant features in model training. These findings demonstrate the potential of machine learning to support psychological research and offer practical tools for the assessment and development of Energetic Intelligence in applied settings. Our work points out the value of integrating AI methodologies with psychological theory to enable data-driven insights into human potential and well-being.},
publisher = {Springer Nature},
keywords = {machine learning},
keywords = {data mining},
keywords = {energetic intelligence},
keywords = {psychology},
title = {A data-driven analysis to predict energetic intelligence},
doi = {10.1007/s41060-026-01026-8},
author = {Baca Ruiz, Luis Gonzaga and Criado-Ramón, David and Serrano-Fernánez, María José and Pérez-Moreiras, Elena and Pegalajar Jiménez, María Del Carmen},
}