A Data-Driven Machine Learning Framework Proposal for Selecting Project Management Research Methodologies
Year: 2025
Type: article
Abstract: Selecting appropriate research methodologies in project management traditionally relies on individual expertise and intuition, leading to variability in study design and reproducibility challenges. To address this gap, we introduce a machine learning-driven recommendation system that objectively matches project management use cases to suitable research methods. Leveraging a curated dataset of 156 instances extracted from over 100 peer-reviewed articles, each example is annotated by one of five application domains (cost estimation, performance analysis, risk assessment, prediction, comparison) and one of seven methodology classes (e.g., regression analysis, time-series analysis, case study). We transformed textual descriptions into TF-IDF features and one-hot-encoded contextual domains, then trained and rigorously tuned three classifiers—random forest, support vector machine, and K-nearest neighbours—using stratified five-fold cross-validation. The random forest model achieved superior performance (93.8% ± 1.9% accuracy, macro-F1 = 0.93, ROC-AUC = 0.94), demonstrating robust generalisability across diverse scenarios, while SVM offered the highest precision on dominant classes. Our framework establishes a transparent, reproducible workflow—from literature extraction and annotation to model evaluation—and promises to standardise methodology selection, enhancing consistency and rigour in project management research design.
Source: Applied Sciences
DOI: 10.3390/app15137263
Institution: West University of Timişoara
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W4411887183