Artificial intelligence adoption, transparency, and organizational change in GCC insurers: Disclosure-based evidence

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Type of the article: Research Article

Abstract
Artificial intelligence is diffusing across Gulf Cooperation Council insurance markets, yet disclosure-based evidence remains fragmented on whether adoption is associated with organizational change or localized automation. This study examines a purposive disclosure-based sample of 120 insurers from Saudi Arabia, the United Arab Emirates, Qatar, Kuwait, Oman, and Bahrain. Because inclusion required sufficient disclosure of artificial intelligence practices, the sample is not intended to represent the insurance market. The study examines whether disclosed artificial intelligence adoption is associated with organizational change through financial transparency and operational efficiency. The dataset is constructed from annual reports, audited financial statements, governance reports, environmental, social, and governance reports, investor materials, and regulatory documents. Documents from 2017 to 2023 are treated as an observation window, coded at the item level, and aggregated into one firm-level score per insurer for cross-sectional structural equation modeling. Results indicate positive associations from artificial intelligence adoption to financial transparency (β = 0.52, p < 0.001) and operational efficiency (β = 0.49, p < 0.001). Financial transparency (β = 0.41, p = 0.003) and operational efficiency (β = 0.38, p = 0.012) are associated with organizational change. The indirect paths through transparency and efficiency are statistically distinguishable from zero within the model. Because all variables are derived from similar disclosure evidence, the pattern is interpreted as disclosure co-patterning rather than proof of a mechanism. The findings are associational, not causal, representative, or longitudinal.

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    • Figure 1. Conceptual framework linking AI adoption, financial transparency, operational efficiency, and organizational change
    • Table 1. Country-level descriptive statistics for AI adoption, transparency, efficiency, and organizational change in GCC insurance companies
    • Table 2. Reliability and validity results for disclosure-based insurance-company constructs
    • Table 3. Structural paths, bootstrapped indirect disclosure-based pathways, and model R2 values linking AI adoption to organizational change
    • Table 4. Bayesian robustness estimates for direct and indirect paths in the insurance-company model
    • Table 5. Summary of directional-expectation results for AI adoption, transparency, efficiency, and organizational change
    • Table 6. Sensitivity and bias checks used to avoid over-interpreting the uniformly positive disclosure paths
    • Table A1. Company sample list
    • Table B1. Source-coded examples for converting textual disclosures into item scores
    • Conceptualization
      Amer Morshed, Ayman Bader
    • Data curation
      Amer Morshed, Ayman Bader, Abdulhadi Ramadan, Mohamad Othman
    • Formal Analysis
      Amer Morshed, Ayman Bader, Abdulhadi Ramadan, Mohamad Othman, Almotasem Al Huniti
    • Investigation
      Amer Morshed, Ayman Bader
    • Methodology
      Amer Morshed, Abdulhadi Ramadan, Mohamad Othman, Almotasem Al Huniti
    • Project administration
      Amer Morshed
    • Resources
      Amer Morshed, Ayman Bader, Abdulhadi Ramadan
    • Supervision
      Amer Morshed
    • Validation
      Amer Morshed, Ayman Bader
    • Writing – original draft
      Amer Morshed, Ayman Bader, Almotasem Al Huniti
    • Visualization
      Ayman Bader
    • Software
      Abdulhadi Ramadan, Almotasem Al Huniti
    • Writing – review & editing
      Abdulhadi Ramadan, Mohamad Othman