## 3 Materials and Methods  
### Materials  
To build a robust, literature-derived dataset, we systematically searched Scopus and Web of Science (January 2015-December 2023). Search terms included combinations of \"project management,\" \"research method,\" \"machine learning,\" \"case study,\" \"survey,\" and \"regression.\" We applied the following inclusion criteria:  
* Peer-reviewed journal articles in English. * Explicit description of the project management application (e.g., cost estimation, risk assessment) and the research method employed. * Accessibility of full text. After removing duplicates and screening titles/abstracts, 128 articles remained. A full-text review yielded 102 articles that met all criteria. From each selected article, two independent researchers extracted:
* Application domain (one of five: cost estimation, performance analysis, risk assessment, prediction, comparison). * Brief textual description of the use case (e.g., \"forecasting budget overruns using historical cost data\"). * Research method label (one of seven: regression analysis, time-series analysis, case study, ethnographic research, experimental design, grounded theory, interview). Discrepancies (\(\approx\)8% of instances) were reconciled through discussion; [PERSON]'s \(\kappa\) = 0.87 indicated high inter-annotator reliability. A third researcher adjudicated the remaining conflicts. The final tabular dataset comprises 156 examples. The input features are:
* Domain (categorical; five values). * Description (textual; preprocessed into TF-IDF features). As shown in Figure 2, we have used tabular datasets, such as CSV files or spreadsheets, because they are the foundation for Python 3.10 shallow machine learning model training. Their basic structure, in which columns represent features and data points represent rows, precisely aligns with the expectations of models such as decision trees and linear regression [26]. The target label is the research method (seven classes). As shown in Table 3, regression analysis emerged as the most frequently employed research method, accounting for 34 out of 156 studies (21.8%). Time-series analysis followed closely at 17.9%, while case studies comprised 15.4%. Together, these three approaches represent the dominant quantitative traditions in our corpus. Experimental designs--another hallmark of quantitative inquiry--contribute an additional 11.5%, bringing the aggregate share of quantitative methods (regression, time-series, experimental design) to 51.2% of all studies (Table 3). Conversely, qualitative methodologies are also well represented. Ethnographic research accounts for 12.8% of studies, whereas grounded theory and interview-based investigations constitute 10.3%. Collectively, these methods comprise 48.8% of the total, indicating a near balance between quantitative and qualitative orientations in the field. This methodological diversity underscores the multifaceted nature of financial analytics research; quantitative techniques offer robust statistical and forecasting capabilities, while qualitative and mixed-methods contribute rich contextual insights into organisational and human factors. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Count** & **Proportion (\%)** \\ \hline Regression Analysis & 34 & 21.8 \\ \hline Time-Series Analysis & 28 & 17.9 \\ \hline Case Study & 24 & 15.4 \\ \hline Ethnographic Research & 20 & 12.8 \\ \hline Experimental Design & 18 & 11.5 \\ \hline Grounded Theory & 16 & 10.3 \\ \hline Interview & 16 & 10.3 \\ \hline Total & 156 & 100.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Class frequencies of the methods. Figure 2: Scheme of the main procedures: from article extraction and annotation to model prediction of research methods. The relatively even distribution across seven distinct classes suggests that our AI agent must be capable of accommodating numerical time series and text-driven qualitative inputs.