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Revisiting industry effects: The assignment of firms to industries
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DOI：10.1002/smj.70106.png)
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Abstract
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Strategy research examines the drivers of firm performance. Variance decomposition studies have analyzed industry's impact, with little attention paid to the industry to which a firm is assigned. This study investigates how industry assignment affects the findings about industry's impact. We analyze existing industry classification schemes and introduce a machine learning-based scheme that leverages an embedding model to process text from annual reports. We find that industry assignment differs substantially across schemes, and that certain firms are more consistently grouped together. Schemes that group more similar firms together exhibit stronger industry effects. Given the literature's frequent reliance on the criticized Standard Industrial Classification scheme, industry's role in firm performance has likely been systematically underestimated.
Keywords:
Bayesian hierarchical modeling
embedding model
industry classification
industry effects
variance decomposition
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