Artificial intelligence, accountability, and sustainability disclosure: a socio-technical analysis of Islamic financial institutions in Saudi Arabia

Abstract This study examines sustainability disclosure practices in Saudi Islamic financial institutions through a comparative assessment of manual content analysis, artificial intelligence (AI)-assisted semantic evaluation. Drawing upon a hybrid environmental, social, governance (ESG)–Shariah framework, the study evaluates disclosure quality across five dimensions: environmental, social, governance, Shariah compliance, Maqasid al-Shariah. The analysis covers the complete population of fully Shariah-compliant commercial banks operating in Saudi Arabia (four banks) over the period 2022–2024. A structured disclosure index is employed for manual assessment, while AI-assisted techniques are used to analyze semantic intensity, thematic emphasis, narrative sentiment within sustainability reports. The findings reveal that sustainability disclosure is governance-driven, socially oriented, whereas environmental, explicitly articulated Maqasid-related disclosures remain comparatively less developed. Manual, AI-assisted approaches identify broadly similar disclosure patterns while exhibiting distinct interpretive sensitivities. The overall mean absolute difference (MAD) of 7.34 percentage points indicates broad cross-method convergence without implying methodological interchangeability, while the larger divergence observed for Maqasid Impact (MAD = 13.80 percentage points) highlights the challenge of evaluating implicit, ethically embedded, context-dependent disclosure through general-purpose semantic models. In addition, sensitivity analysis under alternative weighting scenarios confirms the robustness of the assessment framework, with the overall ranking of banks remaining stable. The study contributes methodologically by demonstrating the complementary value of integrating structured manual evaluation with AI-assisted semantic analysis rather than treating the two approaches as substitutes. Theoretically, it advances the application of an integrated ESG–Maqasid perspective within Islamic finance. Practically, the findings provide insights for regulators, standard setters, Islamic financial institutions seeking to strengthen sustainability disclosure quality, improve transparency, enhance ESG–Maqasid reporting practices

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Publication Details

Journal
Future Business Journal
Published
2026-09-10
DOI
https://doi.org/10.1186/s43093-026-00986-4
Primary Topic
Corporate Social Responsibility Reporting
Type
article
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article

Artificial intelligence, accountability, and sustainability disclosure: a socio-technical analysis of Islamic financial institutions in Saudi Arabia

Hebah Shalhoob
Future Business Journal
Corporate Social Responsibility Reporting
article

Artificial intelligence, accountability, and sustainability disclosure: a socio-technical analysis of Islamic financial institutions in Saudi Arabia

Hebah Shalhoob
article en

Abstract

Abstract This study examines sustainability disclosure practices in Saudi Islamic financial institutions through a comparative assessment of manual content analysis, artificial intelligence (AI)-assisted semantic evaluation. Drawing upon a hybrid environmental, social, governance (ESG)–Shariah framework, the study evaluates disclosure quality across five dimensions: environmental, social, governance, Shariah compliance, Maqasid al-Shariah. The analysis covers the complete population of fully Shariah-compliant commercial banks operating in Saudi Arabia (four banks) over the period 2022–2024. A structured disclosure index is employed for manual assessment, while AI-assisted techniques are used to analyze semantic intensity, thematic emphasis, narrative sentiment within sustainability reports. The findings reveal that sustainability disclosure is governance-driven, socially oriented, whereas environmental, explicitly articulated Maqasid-related disclosures remain comparatively less developed. Manual, AI-assisted approaches identify broadly similar disclosure patterns while exhibiting distinct interpretive sensitivities. The overall mean absolute difference (MAD) of 7.34 percentage points indicates broad cross-method convergence without implying methodological interchangeability, while the larger divergence observed for Maqasid Impact (MAD = 13.80 percentage points) highlights the challenge of evaluating implicit, ethically embedded, context-dependent disclosure through general-purpose semantic models. In addition, sensitivity analysis under alternative weighting scenarios confirms the robustness of the assessment framework, with the overall ranking of banks remaining stable. The study contributes methodologically by demonstrating the complementary value of integrating structured manual evaluation with AI-assisted semantic analysis rather than treating the two approaches as substitutes. Theoretically, it advances the application of an integrated ESG–Maqasid perspective within Islamic finance. Practically, the findings provide insights for regulators, standard setters, Islamic financial institutions seeking to strengthen sustainability disclosure quality, improve transparency, enhance ESG–Maqasid reporting practices

Future Business JournalVol. 12(1)
King Abdulaziz University (SA)
Life in Land, Responsible consumption and production
Openalex Percentile: Top 7%
Corporate Social Responsibility Reporting
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