## 5 Discussion  
The empirical results demonstrate that an ensemble-based random forest classifier can accurately map project descriptions to appropriate research methodologies, achieving a mean accuracy of 93.8% and an F1-score of 0.93. This performance confirms the value of aggregating heterogeneous decision trees to capture quantitative and qualitative features derived from TF-IDF representations and domain encodings. In contrast, margin-based SVM and instance-based KNN models yielded moderate accuracy (-84%), underscoring the superiority of non-linear ensemble methods in this context. This study set out to achieve the following six interrelated objectives: (1) to extract relevant methodological information from the literature, (2) to construct a representative, annotated dataset, (3) to preprocess and engineer features suitable for machine learning, (4) to develop and tune multiple classifiers, (5) to rigorously evaluate their performance, and (6) to identify the optimal model for project management methodology recommendation. The empirical findings substantiate each of these aims and reveal avenues for further enhancement and practical deployment. First, our systematic literature extraction protocol successfully retrieved detailed descriptions of research methods and their applications across five project management domains. High inter-annotator agreement ([PERSON]'s \(\kappa=0.87\)) confirms the reliability of our annotation process, thereby satisfying Objective 1 and laying a robust foundation for downstream modelling. Second, we assembled a tabular corpus of 156 instances mapping project scenarios to seven distinct methodological classes. This dataset, although modest in scale, provides sufficient coverage of both quantitative and qualitative approaches--addressing Objective 2--and enables balanced evaluation across diverse use cases. Third, in pursuing Objective 3, we applied a dual-feature strategy: TF-IDF representations captured the semantic nuances of textual descriptions (e.g., \"longitudinal trend analysis\" correlating with time-series methods), while one-hot encoding of application domains contextualised project attributes. This hybrid feature set empowered classifiers to leverage complementary signals, thereby supporting robust generalisation. Fourth, in line with Objective 4, we developed three classifiers: a random forest, a support vector machine, and a K-nearest neighbours--and performed nested stratified cross-validation to optimise hyperparameters. The random forest model's ensemble structure justified its computational overhead by delivering superior predictive power. In contrast, SVM's margin-based decisions and KNN's instance-based reasoning highlighted the trade-offs inherent in alternative learning paradigms. Fifth, fulfilling Objective 5, we conducted comprehensive performance assessments. Stratified five-fold cross-validation and an independent 20% held-out test set both \begin{table}
\begin{tabular}{c c} \hline \hline
**Tested Model** & **Average Execution Times (Seconds)** \\ \hline SVM & 5.997 \\ \hline KNN & 6.001 \\ \hline RF & 13.801 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Average execution times for model training and inference. demonstrated the random forest classifier's exceptional accuracy (\(\approx\)93.8%) and macro-F\({}_{1}\) (0.93), whereas SVM and KNN attained moderate scores (\(\approx\)84%). Class-wise metrics (Table 5) and the confusion matrix (Figure 10) illuminated systematic patterns; dominant classes, such as regression analysis, achieved F\({}_{1}\)-scores above 0.95, while minority categories (e.g., ethnographic research) remained more challenging (F\({}_{1}\approx\)0.82). Benchmark comparisons against a majority-class predictor (21.8% accuracy) and a balanced logistic regression (72.4%) contextualised these gains and underscored the necessity of non-linear, ensemble approaches.