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Artificial intelligence, machine learning, and big data: Improvements to the science of people at work and applications to practice
Citation. Woo, S. E., Tay, L., & Oswald, F. (2024). Artificial intelligence, machine learning, and big data: Improvements to the science of people at work and applications to practice. Personnel Psychology, 77(4), 1387–1402. https://doi.org/10.1111/peps.12643
Summary. This special-issue introduction argues that AI, machine learning, and big data can advance organizational science through theory development, construct measurement, and prediction of work outcomes. It frames those opportunities alongside practical, ethical, and institutional constraints, including data access, methodological skills, transparency, privacy, reproducibility, generalizability, and interpretability.
Key claims
- AI/ML and intensive data can broaden organizational theory-building through inductive and abductive inquiry, while theory remains important for data collection, interpretation, explainability, and responsible use.
- New data modes such as language and video can expand construct discovery and measurement, but they require validation approaches suited to dynamic and computational measures.
- Predictive performance is a useful practical complement to explanation, not a substitute for transparent, generalizable, and ethically governed organizational research.
Evidence & limitations
- This is a peer-reviewed conceptual introduction to a special issue rather than a primary empirical study; it synthesizes opportunities and challenges and illustrates them with prior research.
- Its recommendations emphasize that data accessibility, privacy, reproducibility, generalizability, interpretability, and methodological skill gaps constrain responsible AI/ML research and practice.