Corporate governance and innovation: a predictive modeling approach using machine learning

The examination of the associations between internal corporate governance (CG) mechanisms and innovation faces challenges due to nonlinear patterns and complex interactions. Consequently, existing literature rarely reaches a consensus on the directions or strengths of these relationships. Furthermore, to investigate the CG–innovation association, prior research has predominantly relied on explanatory modeling, which involves applying statistical models to data to test correlational or causal hypotheses about theoretical constructs. These are the reasons why it remains unclear whether internal CG mechanisms, when considered collectively as an extensive array of interconnected variables, offer valuable insights for accurately predicting innovation. To address this gap, we analyze a dataset of research and development (R&D) projects from the Brazilian electricity sector by employing predictive modeling, which entails using statistical models or data mining algorithms to predict new observations, particularly using supervised machine learning (ML) methods. Our study demonstrates that a comprehensive set of variables representing internal CG mechanisms significantly enhances the predictive capabilities of ML algorithms for innovation. Furthermore, we illustrate how ML can illuminate nonlinear and non‐monotonic patterns, and interactions among variables, in the CG–innovation relationship. Our contribution to the literature encompasses three key aspects: introducing a predictive modeling approach to the discourse on the role of CG in innovation attainment through R&D endeavors, which can complement and enrich existing explanatory research; investigating non‐linear and non‐monotonic relationships, as well as interactions, in innovation prediction; and affirming the emerging body of literature that recognizes supervised ML as a valuable tool accessible to management researchers.