Unlocking Environmental Innovation Through Board Diversity and Governance: A Machine Learning Approach
运用机器学习技术，分析酒店旅游业数据，发现董事会多样性（如性别、非执行董事）和特定战略技能能显著提升企业的环境创新，尤其当与主动的可持续实践结合时。
ABSTRACT 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.