Unlocking Environmental Innovation Through Board Diversity and Governance: A Machine Learning Approach

This study advances governance scholarship by applying robust machine learning techniques, bagging, random forest, boosting, SHapley Additive exPlanations (SHAP), and partial dependence plots (PDPs), to systematically explore how diverse board compositions (gender diversity, nonexecutive member diversity, independent board diversity) and the presence of board members with specific strategic skills (board‐specific skills percent) impact firms' environmental innovation outcomes. Using comprehensive governance data from the hospitality and tourism sector (Refinitiv, 2015–2024), results reveal strong predictive relationships, highlighting product responsibility as the most influential factor. The analysis further indicates that board‐specific skills and external diversity significantly amplify firms' environmental innovation, particularly when combined with proactive sustainability practices. SHAP and PDP analyses provide deeper insights into these nonlinear interactions, enriching theoretical perspectives drawn from Resource Dependency Theory, Upper Echelons Theory, and Stakeholder Theory. This study offers valuable strategic implications for industry practitioners aiming to leverage targeted governance structures to enhance sustainability‐driven innovation.