Quantitative Emotional Salary and Talent Commitment in Universities: An Unsupervised Machine Learning Approach

Ana-Isabel Alonso-Sastre, Juan Pardo (), Oscar Cortijo and Antonio Falcó ()
Additional contact information
Ana-Isabel Alonso-Sastre: Department of Human Resources, Universidad CEU Cardenal Herrera, CEU Universities, Carrer Assegadors, 2, 46115 Alfara del Patriarca (Valencia), Spain
Juan Pardo: Department of Mathematics, Physics and Technology, Universidad CEU Cardenal Herrera, CEU Universities, San Bartolomé 55, 46115 Alfara del Patriarca (Valencia), Spain
Oscar Cortijo: Department of Human Resources, Universidad CEU Cardenal Herrera, CEU Universities, Carrer Assegadors, 2, 46115 Alfara del Patriarca (Valencia), Spain
Antonio Falcó: Department of Mathematics, Physics and Technology, Universidad CEU Cardenal Herrera, CEU Universities, San Bartolomé 55, 46115 Alfara del Patriarca (Valencia), Spain

Merits, 2025, vol. 5, issue 2, 1-17

Abstract: In the world of academia, there is a great mobility of talented university professors with a high level of movement among different entities. This could be a major problem, as universities must retain a minimum level of talent to support their various academic programmes. In this sense, finding out what factors could increase the loyalty of such staff can be of great interest to human resource (HR) departments and the overall administrative management of an organisation. Thus, this area, also known as People Analytics (PA), has become very powerful in human resource management to strategically address challenges in talent management. This paper examines talent commitment within the university environment, focusing on identifying key factors that influence the loyalty of professors and researchers. To achieve this, machine learning (ML) techniques are employed, as Principal Component Analysis (PCA) for dimensionality reduction and clustering techniques for individual segmentation have been employed in such tasks. This methodological approach allowed us to identify such critical factors, which we have termed Quantitative Emotional Salary (QES), enabling us to identify those factors beyond those merely related to compensation. The findings offer a novel data-driven perspective to enhance talent management strategies in academia, promoting long-term engagement and loyalty.

Keywords: people analytics; machine learning; principal component analysis; clustering; emotional salary; talent engagement (search for similar items in EconPapers)
JEL-codes: J L M (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2673-8104/5/2/14/pdf (application/pdf)
https://www.mdpi.com/2673-8104/5/2/14/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmerit:v:5:y:2025:i:2:p:14-:d:1677965

Access Statistics for this article

Merits is currently edited by Ms. Aria Hou

More articles in Merits from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().