## 5 Conclusions  
The findings of this research highlight a significant relationship between Quantitative Emotional Salary (QES) and talent loyalty, showing similar behavioural patterns to those observed in the Economic Compensation (EC) sub-dataset. However, this study emphasises, that both QES and EC independently contribute to academic talent loyalty, suggesting that while both factors influence commitment, they do so in complementary ways. This study reinforces the growing recognition that emotional salary plays a fundamental role in academic talent commitment, alongside economic factors. The developed methodological framework proved effective in identifying latent patterns in the data while also reducing dimensionality, thereby enhancing interpretability. This improved clarity makes the results more accessible and applicable for decision-makers in higher education institutions. Nevertheless, it is important to underscore that this research is based on a single institutional case study. As such, its findings are context-specific and should not be generalised across the entire higher education sector without further validation. Institutional structures, regulatory frameworks, and cultural contexts vary widely between universities and national systems, and these factors could significantly affect the relevance and replicability of the observed patterns. Despite promising results, this study has certain limitations. One key limitation is that the projection of original variables into the PCA space may obscure significant nonlinear relationships, potentially limiting a deeper understanding of underlying data structures. Additionally, while the K-Means clustering algorithm is computationally efficient, it assumes spherical clusters of similar sizes, which may not fully capture the actual distribution of data points. Another limitation pertains to the possible exclusion of essential qualitative variables during the initial data selection process. This study focused primarily on quantitative dimensions of emotional salary and economic compensation; however, qualitative factors--such as personal perceptions, institutional culture, and professional experiences--could provide additional insights into talent loyalty. Future research should address these methodological limitations by exploring hybrid approaches that integrate both qualitative and quantitative dimensions. Expanding the scope of analysis to broader and more diverse organisational contexts would allow for a more comprehensive understanding of talent commitment. This study also opens new pathways for data-driven talent management research. By leveraging data analytics, future studies can explore critical strategic lines in human resource management, helping institutions develop more targeted and effective policies for fostering long-term faculty engagement and commitment. Conceptualization, A.-I.A.-S., J.P., O.C. and A.F. ; methodology, J.P. and A.F. ; software, A.-I.A.-S.; validation, J.P. and A.F. ; formal analysis, J.P. and A.F. ; investigation, A.-I.A.-S. and O.C. ; resources, A.-I.A.-S. and O.C. ; data curation, A.-I.A.-S.; writing--original draft preparation, A.-I.A.-S. and J.P.; writing--review and editing, A.-I.A.-S., J.P., O.C. and A.F. ; visualization, A.-I.A.-S.; supervision, J.P.; project administration, J.P.; funding acquisition, A.F. All authors have read and agreed to the published version of the manuscript. This research was funded by Universidad CEU Cardenal Herrera with the grant number INDI24/17. This study did not require approval from an ethics committee, as it was conducted using aggregated data with no individual-level identifiers. The data used in the analysis are fully anonymised and grouped in such a way that it is not possible to trace back any information to individual participants. Therefore, according to applicable regulations and ethical standards, ethical approval was not necessary.