## 6 Conclusions  
This study has demonstrated that a random forest-based framework can predict suitable research methodologies for varied project management scenarios with high accuracy (93.8 %) and robust discriminative capacity (ROC-AUC = 0.94). By training and validating three shallow classifiers on a carefully curated corpus of 156 literature-derived instances spanning cost estimation, performance analysis, risk assessment, prediction, and comparison, we have shown that ensemble learning not only generalises effectively across multiple domains but also retains interpretability through feature importance measures.  
Despite these promising results, the research is subject to certain complexities and general applicability limitations. The dataset, although diverse, comprises only 156 examples and may not fully represent emerging or hybrid methodologies, potentially constraining the model's capacity to generalise to more complex methodological frameworks. Furthermore, while the random forest ensemble achieves satisfactory performance, its computational demands increase with feature dimensionality and dataset size, which may impede scalability in large-scale applications.  
To extend this framework's applicability, the data pipeline and feature engineering routines can be abstracted into configurable modules that accommodate domain-specific literature sources and annotation protocols. Adopting transformer-based models pretrained on expansive academic corpora may facilitate transfer learning and domain adaptation, enabling the method to capture nuanced methodological patterns beyond project management.  
Predictive accuracy can be further enhanced by integrating advanced text representations, such as contextual embeddings derived from models like BERT or RoBERTa, which offer a finer-grained semantic understanding of methodological descriptions. Combining ensemble learning with neural meta-learners and implementing active learning strategies, wherein experts iteratively annotate cases with high uncertainty, may address class imbalance and improve robustness against rare method categories.  
Future research should focus on expanding the dataset through semi-automated article mining and crowd-sourced annotation to encompass a broader spectrum of methodologies and disciplines. Investigations into scalable model architectures, including distributed training and model compression techniques, will be essential for enterprise-level deployment. Moreover, incorporating explainability mechanisms, such as SHAP or LIME, will provide instance-level transparency, thus fostering user trust and facilitating evidence-based decision support. Longitudinal studies examining the influence of model recommendations on methodological choices over time would offer valuable insights into the practical impact of the system.  
A.A.: conceptualisation, investigation, data curation, writing, original draft preparation; D.B.: methodology, software, validation, writing, original draft preparation; O.D. ([PERSON]): validation, formal analysis, project administration; V.N.: resources, visualization, funding acquisition; O.D. ([PERSON]): supervision, resources, funding acquisition. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author(s).  
We would like to thank the participants of the IE2024 Conference for their insightful feedback.  
The authors declare no conflicts of interest.