AI-driven analysis of ESG-linked financial determinants for sustainable growth in Thailand: A hybrid SEM framework

This study examines sustainable growth among Thai listed firms using an AI-enhanced Structural Equation Modeling (AI-SEM) framework that integrates financial and ESG perspectives within contingency theory and stakeholder value maximization. The framework extends conventional SEM by incorporating anomaly detection, feature selection, and dimensionality reduction prior to estimation, followed by post-SEM time-series interpretability analysis to address multicollinearity, data noise, and firm-level heterogeneity. Using panel data from 147 firms listed on the Stock Exchange of Thailand during 2018–2022, fourteen hypotheses are tested across five financial and governance constructs: performance, internal control, economic profit, dividend policy, and market value added, with sustainable growth proxied by ESG outcomes. The results indicate that performance and internal control are key drivers of sustainable growth, both directly and indirectly through market value added. In contrast, economic profit exhibits a negative association with sustainability outcomes, while dividend policy is insignificant, reflecting Thailand’s cyclical dividend behaviour. These findings underscore the context-dependent nature of sustainable growth and reinforce contingency theory by demonstrating how firm characteristics and market conditions jointly shape financial sustainability. The study further shows that integrating AI-enabled analytics into ESG–finance evaluation enhances decision precision and organisational adaptability, offering actionable insights for policymakers and regulators seeking to align data-driven financial strategies with long-term sustainability objectives and advance Sustainable Development Goals 8 and 9.