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

Traditional management research methods have trouble capturing the richness of context, the way elements depend on each other, and cultural differences in complex global workforces. Big Data Analytics (BDA) and Machine Learning (ML) can spot patterns well, but their “black‑box” nature limits transparency, fairness, and connection to theory. To address these issues, the paper proposes an eight‑step methodological framework organized into three core phases: 1. Define the research questions and gather datasets that contain rich contextual information. 2. Apply BDA and ML techniques while building in transparency and testing results across different contexts. 3. Extract interpretable outcomes by linking them to theory and practice. The framework weaves explainability into every

stage of the research lifecycle, helping researchers uncover hidden patterns in managerial domains and supporting the development of context‑sensitive theory. Because the paper only illustrates the framework with use cases, a key limitation is that each researcher must implement and adapt the steps for their own data sets and theoretical constraints. Fried Engineers offers guidance and structure to help students turn this conceptual framework into simulated machine‑learning pipelines that include strong explainability metrics.

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