Toward a methodological framework for big data and machine learning analysis in management research

As the global workforce becomes increasingly complex and interconnected, traditional management research methods often struggle to capture contextual richness, interdependence, and cultural variation. Big data analytics (BDA) and machine learning (ML) offer unprecedented opportunities to analyze complex patterns, yet their black box nature raises concerns regarding transparency, fairness, and theoretical integration. To address these challenges, this paper develops an eight‐step methodological framework designed to enhance explainability in management research using BDA and ML. The framework unfolds across three phases: (1) defining questions and assembling context‐rich datasets, (2) applying BDA and ML techniques with embedded transparency and cross‐context validation, and (3) extracting interpretable results by integrating theory and practice to generate actionable insights. Our framework demonstrates how explainability can be systematically embedded throughout the research process. Drawing on illustrative use cases and real‐world examples, we show how the framework uncovers hidden patterns across managerial domains while supporting nuanced, context‐sensitive theory development. The paper contributes by providing a replicable framework for integrating BDA and ML into research, advancing explainable AI debates in management, demonstrating how BDA supports theoretical advancement, and embedding granularity, explainability, and contextual sensitivity throughout the analytical process.