Using Supervised Machine Learning to Scale Human-Coded Data : A Method and Dataset in the Board Leadership Context

Accepted and in-press at Strategic Management Journal. Unedited Author Version – please cite using final version at: https://doi.org/10.1002/smj.3480Human coding of unstructured text can enable scholars to measure complex latent constructs for use in empirical analysis, but also requires substantial time and resources that limit the number and sample sizes of studies using this approach. We demonstrate how supervised machine learning (ML) can overcome these constraints by allowing scholars to scale human-coded data. Using board leadership as an illustrative context, we apply this method to create a large-scale dataset (N = 22,388) from smaller scale human codings of CEO duality and board chair orientations from company proxy statements. We further demonstrate the potential value of this approach by using the resulting dataset to examine the relationships among board leadership, firm performance, and CEO dismissal. The ML code and dataset are available at 10.5281/zenodo.7304697