## 4 Discussion  
This study applied unsupervised machine learning methods to analyse a dataset related to the university academic environment, with a specific focus on university professors and researchers, and their relationship with talent commitment. One of the key aspects to consider when analysing talent commitment in higher education institutions is the specific nature of the university environment compared to other sectors [37], particularly the business sector. Unlike commercial organisations, universities are subject to additional legal and administrative regulations that significantly influence human resource management. These include public regulations governing recruitment, promotion, and job stability, externally determined salary scales, and an institutional framework in which non-monetary incentives carry considerable weight. In this regard, although the present study draws on concepts such as Quantitative Emotional Salary (QES), which are inspired by practices from the business world, their application has been carefully contextualised. The aim is not to directly transfer private \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Current Category Level** & **Cluster 1** & **Cluster 2** & **Cluster 3** & **Cluster 4** \\ \hline  
esearch Professor** & 19.57 & - & 0.50 & - \\ \hline \hline \end{tabular}  
* N = 599; values represent the percentage of professors in each category within the clusters; (-) indicates the absence of values in the corresponding category within the cluster.  
\end{table} 
Table 4: Percentage distribution of a qualitative variable by clusters with EC sub-dataset. sector models, but rather to explore internal patterns within the university system that may serve as meaningful indicators of motivation and retention, based on their statistical behaviour resembling that of economic compensation. Acknowledging these structural differences is essential for a proper interpretation of the results. Thus, the contribution of this study lies in offering a methodological framework that can be adapted to different institutional contexts, always considering the regulatory and organisational constraints inherent to the education sector. The findings offer valuable insights into the factors influencing talent loyalty within academic institutions. The composition of the variables that make up the two sub-datasets is detailed in Appendix A, Table A1. This table provides a complete description of the variables included in each sub-dataset, offering a clear view of the elements that constitute the data used in the analysis. Through dimensionality reduction and clustering techniques, significant patterns were identified--some aligning with previous research while others providing novel interpretations of professional behaviours and needs. The proposed methodology proved effective in not only simplifying the inherent complexity of the dataset but also generating actionable insights that can inform strategies to improve talent commitment in academic settings. Various analytical tools were employed, including correlation matrix analysis, which was computed using [PERSON]'s coefficient, Principal Component Analysis (PCA), and the K-Means clustering algorithm. These techniques helped reduce the dataset's dimensionality, facilitating the identification of key relationships and sub-datasets of interest. Two primary sub-datasets emerged from the analysis: Quantitative Emotional Salary (QES) and Economic Compensation (EC). The QES sub-dataset, comprising nineteen correlated variables, highlights the critical role of emotional factors in faculty commitment. In contrast, the EC sub-dataset, consisting of ten correlated variables, demonstrates how economic compensation is also linked to talent commitment. PCA was instrumental in reducing the dataset's complexity while preserving its most significant information. In the QES sub-dataset, five principal components explained more than 70% of the total variance, whereas in the EC sub-dataset, just two principal components accounted for 80% of the total variance.