## 1 Introduction  
Project management researchers and practitioners traditionally select study designs--such as regression analysis, time-series forecasting, case studies, or surveys--based on personal expertise and intuition. While this subjective approach leverages domain experience, it can also yield inconsistent methodology choices, reduce the reproducibility of findings, and hinder cross-study comparability. Despite growing calls for evidence-based decision support in research design, no systematic framework currently exists to guide methodology selection in project management research.  
Over the past decade, machine learning (ML) has demonstrated remarkable success in operational project management tasks, from forecasting cost and schedule deviations to classifying risk factors and optimising resource allocation [1, 2]. These studies illustrate ML's ability to extract actionable insights from large, dynamic datasets, enabling more accurate risk assessments and decision making in complex project environments. Yet, ML's potential for \"meta-research\"--recommending the most suitable research method for a given project scenario--remains untapped.  
Concurrently, the project management literature has long recognised the value of rigorous quantitative, qualitative, and mixed research methodologies for advancing theory and practice [3]. Standard techniques include regression variants (linear, logistic, Poisson, panel data), time-series analysis, experimental designs, case studies, grounded theory, and ethnography. Each methodology offers unique strengths--for example, regression models illuminate variable interactions, while case studies capture contextual depth--but choosing among them often rests on subjective judgment.  
To address this gap, we propose a data-driven ML framework that objectively recommends research methods tailored to specific project management use cases. Drawing on insights from the systematic literature review, we compiled a curated dataset of 156 instances extracted from over 100 peer-reviewed articles [4]. Each instance is annotated with one of five application domains--cost estimation, performance analysis, risk assessment, prediction, and comparison--and labelled by the research method employed.  
The six main objectives of this paper are the following (see Figure 1):  
1. Data Extraction: Retrieve detailed information on research methods and their applications from academic journals.
2. Dataset Construction: Assemble a tabular dataset mapping project management use cases to research methodologies.
3. Preprocessing: Clean, encode, and prepare the dataset for ML modelling.
4. Model Development: Train and validate three classifiers (random forest, K-nearest neighbours, support vector machine) to predict suitable research methods.
5. Evaluation: Assess model generalisability and performance using accuracy, precision, recall, ROC-AUC, and other relevant metrics.
6. Model Selection: Identify the most effective classifier for each project management application.  
Our framework aims to enhance objectivity, reproducibility, and scalability in project management research design by automating methodology selection. The following sections detail the literature foundations, dataset development, modelling techniques, experimental results, and practical implications of integrating predictive analytics into research planning.  
Figure 1: Research method prediction process.