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Artificial Intelligence-Enhanced Environmental, Social, and Governance Disclosure Quality and Financial Performance Nexus in Saudi Listed Companies Under Vision 2030

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  • Mohammed Naif Alshareef

    (Department of Accounting, College of Business and Economics, Umm Al-Qura University, Makkah P.O. Box 715, Saudi Arabia)

Abstract

The integration of artificial intelligence (AI) into environmental, social, and governance (ESG) disclosure represents a critical frontier for corporate transparency in emerging markets. This study investigates the relationship between AI adoption in ESG reporting, disclosure quality, and financial performance among 180 Saudi-listed companies (2021–2024) within Vision 2030’s transformative context. Using the System Generalized Method of Moments (GMM) estimation with panel unit root and cointegration testing to ensure stationarity assumptions and addressing endogeneity through bounding analysis, the study finds that AI adoption intensity significantly enhances ESG disclosure quality (β = 0.289, p < 0.001), with coefficient significance assessed through t -tests using firm-clustered robust standard errors. Enhanced disclosure quality translates into meaningful financial performance improvements: 0.094 percentage points in return on assets (ROA), 0.156 in return on equity (ROE), and 0.0073 units in Tobin’s Q. Mediation analysis reveals that 73% of AI’s total effect operates through improved ESG quality rather than direct operational benefits. The findings demonstrate parametric bounds robust to macroeconomic confounders, suggesting AI-enhanced transparency creates substantial shareholder value through strengthened stakeholder relationships and reduced information asymmetries.

Suggested Citation

  • Mohammed Naif Alshareef, 2025. "Artificial Intelligence-Enhanced Environmental, Social, and Governance Disclosure Quality and Financial Performance Nexus in Saudi Listed Companies Under Vision 2030," Sustainability, MDPI, vol. 17(16), pages 1-39, August.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:16:p:7421-:d:1726023
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