Joseph S. Harrison

Research Summary Human 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 . Managerial Summary Manually converting unstructured text into usable data requires considerable time and resources. This article outlines a replicable process for applying supervised machine learning (ML) to overcome these constraints by scaling manually coded data. While ML is often used to identify patterns or predict relationships within a given dataset, we show how scholars and practitioners can build valuable custom algorithms at an earlier stage in the process—when first building a dataset. We illustrate this approach by training ML algorithms to replicate human codings of CEO duality and board chair control and collaboration orientations from over 22,000 company filings. We then show how this approach can support new knowledge development by using these data to explore the relationships among board leadership, company performance, and CEO dismissal.

Strategic entrepreneurship integrates the fields of strategy and entrepreneurship to consider firms’ simultaneous engagement in opportunity- and advantage-seeking behaviors to create wealth. Since its conceptualization in the early 2000s, the study of SE has gained considerable momentum. At the same time, some scholars criticize SE and question its comprehensiveness as a way to understand the strategic application of entrepreneurship. We present a comprehensive review and integration of research related to the study of SE as a construct, theoretical model, and research domain. In doing this, we offer a research agenda across these different approaches to the study of SE.

Research Summary How CEO cognitive complexity influences firm outcomes raises an intriguing theoretical tension. While more cognitively complex CEOs can potentially bolster firm performance through their more elaborate and multifaceted information processing, those tendencies can also hurt performance because they require more time and energy, delaying decision making. We posit and show a nuanced effect of CEO cognitive complexity on firm performance, contingent on industry conditions. CEO cognitive complexity benefits performance under more complex, stable, and munificent industry conditions, but hurts performance under simpler, more dynamic, and more constrained conditions. Post‐hoc analyses further show that these effects are similar when considering firm‐level factors reflecting munificent and dynamic internal conditions. Our study highlights the boundary conditions under which CEO cognitive complexity may be beneficial or detrimental for firms. Managerial Summary CEOs have different cognitive styles that can impact how they approach decision making. Whereas some exhibit greater cognitive complexity, that is, by engaging in broader and deeper information search and considering more differentiated and nuanced perspectives and alternatives, others engage in simpler and less comprehensive information processing when making decisions. While it seems intuitive to assume that CEOs' cognitive complexity should be beneficial for firms, collecting and processing a large amount of complex information can also complicate and delay decision‐making. Our results show that S&P 1500 CEOs who are more cognitively complex improve firm performance when their firms operate in more complex, stable, and resource‐rich environments but hurt firm performance when their firms operate in simpler, more dynamic, and resource‐constrained environments.