Digital technology and Artificial Intelligence (AI) are transforming corporate governance and financial decision-making worldwide, especially among emerging-market firms aiming to join global digital value chains. This study examines how AI-enhanced digital governance affects the relationship between ownership and capital structure in export-focused emerging-market manufacturers. We analyzed 300 Bangladeshi firms employing a hybrid methodology that integrates traditional econometrics with machine learning (ML) algorithms and explainable AI techniques, including SHapley Additive exPlanations (SHAP) values. We developed and validated innovative digital governance metrics that assess AI adoption, digital maturity, data-driven decision-making, and advanced risk management. Our analysis applied panel regression with instrumental variables alongside 10 ML algorithms spanning linear models, ensemble tree methods, support vector machines, and neural networks, validated through multiple cross-validation techniques. Results show that conventional determinants of capital structure have surprisingly limited explanatory power, with ownership concentration accounting for only 1.4% of leverage variation and profitability just 0.2%. Governance flow analysis reveals ownership influences leverage through governance mediation (χ²=47.83, p < 0.001). Owner-led decisions tend to be conservative (67%), while board-led decisions lean aggressive (44%). Regularized linear models outperform complex algorithms (Friedman χ² = 23.7, p < 0.001), underscoring simplicity in emerging markets. Behavioral and governance factors (Risk Tolerance 0.187, Decision Maker 0.162) are more influential than traditional financial metrics. Traditional capital structure theories require rethinking in emerging markets due to institutional constraints, causing different financing patterns. Using explainable AI in conjunction with financial theory provides actionable insights for managers, investors, and policymakers in digital corporate finance transformation.
Chowdhury et al. (Fri,) studied this question.