The intellectual structure of learning agility: A case study using a modified BERT model for topic modeling

Auteurs

DOI :

https://doi.org/10.31637/epsir-2025-1416

Mots-clés :

learning agility, intellectual structure, topic modeling;, BERT, leadership, potential;, identification, development.

Résumé

Introduction: The study aims to deepen the understanding of learning agility, a relatively new construct in the field. Learning agility is essential for identifying and developing leadership talent in organizations, particularly in environments of constant change. Methodology: A thematic analysis was conducted on the titles and abstracts of 112 significant works on learning agility. The analysis utilized abstract clustering and a modified version of the BERT model for topic modeling. These influential works were identified through a prior study using bibliometric citation techniques. Results: Nine intellectual topics or patterns related to learning agility were identified, along with the influential works within each topic. The results were then compared to another intellectual structure derived from a co-citation analysis of the same set of works. Correspondences between the topics identified through both methods were established. Discussion: The comparison between topics identified through thematic analysis and co-citation analysis provides a comprehensive perspective. This integrated approach helps to advance towards a unified conceptualization of learning agility, which is essential for standardizing its measurement and application. Conclusions: The study demonstrates that combining bibliometric techniques and Natural Language Processing (NLP) facilitates academic exploration in complex research areas. This approach enables the development of more objective and reliable tools for organizations to identify and develop leadership talent.

Téléchargements

Les données relatives au téléchargement ne sont pas encore disponibles.

Bibliographies de l'auteur

Gonzalo Grau García, Comillas Pontifical University

Doctorando en Competitividad Empresarial, Innovación y Sostenibilidad, Universidad Pontificia Comillas, Universidad de Deusto e IQS School of Management (Universitat Ramon Llull). Ingeniero Superior Agrónomo (UPM), MBA por el IE Business School, Master in Economics and Business Reseach por la Universidad Pontificia Comillas y Miembro del Alumni Community y certificado en Marketing y Ventas por Kellogg School of Management. Cuenta además con programas executive en New York University (HR Engineering and Analytics Concentration), The University of Chicago - Booth School of Business (Strategic Sales), Stanford University Graduate School of Business (Human Resources for Strategic Advantage), Universidad de Nebrija (Strategic leadership) y Stanford University Graduate School of Business (Strategic Marketing Management). Lleva toda su carrera profesional ligado al aprendizaje y al desarrollo del liderazgo, así como a la consultoría en talent management. Profesor colaborador en IE Business School y EAE Business School.

María José Martín Rodrigo, Comillas Pontifical University

La profesora Mª José Martín Rodrigo es Licenciada en Filosofía y Ciencias de la Educación por la Universidad Pontificia Comillas, donde se graduó en 1984, y Máster en Dirección y Desarrollo de los Recursos Humanos por IDE-CESEM (1993). En el inició de su trayectoria profesional simultaneó la docencia en diferentes centros universitarios con la consultoría de empresa. En el año 2003 obtuvo el título de Doctora por la Universidad Pontificia Comillas, institución en la que continúa ejerciendo la docencia como profesora propia agregada del departamento de Gestión Empresarial de la Facultad de CC.EE. y EE., en las áreas de Organización y RR. HH. Supervisa investigadores interesados en estudios sobre: clima laboral, satisfacción y rendimiento; el análisis de rendimiento académico e innovación educativa; Impacto de las políticas de personal en la productividad empresarial; Conciliación y corresponsabilidad; Brechas de género; y, Análisis del Mercado laboral Juvenil.

Antonio Rua Vieites, Comillas Pontifical University

Doctor por la Universidad Complutense. Investigador y Profesor en la Universidad Pontificia Comillas. Director del Departamento de Métodos Cuantitativos. Director de la Cátedra MESIAS de Modelización. Profesor Propio Ordinario (Catedrático Universidad) en la Universidad Pontificia Comillas de Madrid. Doctor en Ciencias Físicas y Licenciado en Ciencias y Técnicas Estadísticas por la Universidad Complutense. EMBA por Comillas. Docente e Investigador  en el campo de los métodos cuantitativos aplicados a la educación, Administración de Empresas, Economía y Sociología. Director del Departamento de Métodos Cuantitativos. Director de la Cátedra MESIAS de Modelización.

Références

Abuzayed, A., & Al-Khalifa, H. (2021). BERT for Arabic topic modeling: An experimental study on BERTopic technique. Procedia Computer Science, 189, 191-194. https://doi.org/10.1016/j.procs.2021.05.096 DOI: https://doi.org/10.1016/j.procs.2021.05.096

Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805. https://doi.org/10.18653/v1/N19-1423 DOI: https://doi.org/10.18653/v1/N19-1423

George, L., & Sumathy, P. (2023). An integrated clustering and BERT framework for improved topic modeling. International Journal of Information Technology, 15(4), 2187-2195. https://doi.org/10.1007/s41870-023-01268-w DOI: https://doi.org/10.1007/s41870-023-01268-w

Grau-Garcia, G., Rua-Vieites, A. & Martín, M. J. (2024). The intellectual structure of learning agility: A bibliometric study. Consulting Psychology Review. (Under review)

McInnes, L., Healy, J., & Melville, J. (2020). UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. arXiv preprint, arXiv:1802.03426. https://arxiv.org/abs/1802.03426

Peters, M. E., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L. (2018). Deep contextualized word representations. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pp. 2227–2237. Association for Computational Linguistics. https://doi.org/10.18653/v1/N18-1202 DOI: https://doi.org/10.18653/v1/N18-1202

Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2018). Improving language understanding by generative pre-training. OpenAI Technical Report. https://doi.org/10.5281/zenodo.3247217

Reimers, N., & Gurevych, I. (2019). Sentence-BERT: Sentence embeddings using Siamese BERT networks. arXiv preprint, arXiv:1908.10084. https://doi.org/10.18653/v1/2019.acl-main.657 DOI: https://doi.org/10.18653/v1/D19-1410

Ritzer, G., Zhao, S., & Murphy, J. (2001). Metatheorizing in Sociology: The Basic Parameters and the Potential Contributions of Postmodernism. En J.H. Turner (Ed.), Handbook of Sociological Theory. Springer. DOI: https://doi.org/10.1007/0-387-36274-6_6

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. In Advances in neural information processing systems (pp. 1112–1122). https://doi.org/10.5555/3295222.3295349

Williams, A., Nangia, N., & Bowman, S. R. (2018). A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Vol. 1 (Long Papers), pp. 1112–1122, Association for Computational Linguistics. DOI: https://doi.org/10.18653/v1/N18-1101

Téléchargements

Publiée

2025-02-03

Comment citer

Grau García, G., Martín Rodrigo, M. J., & Rua Vieites, A. (2025). The intellectual structure of learning agility: A case study using a modified BERT model for topic modeling. European Public & Social Innovation Review, 10, 1–22. https://doi.org/10.31637/epsir-2025-1416

Numéro

Rubrique

Miscelánea