Replication Data for: SmartPLS Analysis Report

The SmartPLS Analysis Report documents the Partial Least Squares Structural Equation Modeling (PLS-SEM) procedures used to validate the GCC AI Adoption Index. It provides a transparent, step-by-step account of how measurement and structural models were assessed, ensuring that the study’s findings are reproducible, interpretable, and robust. Key Contents Measurement Model Checks Reliability metrics (Cronbach’s α, Composite Reliability, AVE). Outer loadings/weights for reflective and formative constructs. Discriminant validity (Fornell–Larcker criterion, HTMT ratios). Structural Model Results Path coefficients (β values) with bootstrapped t- and p-values. R² and adjusted R² values showing explanatory power. Effect sizes (f²) and predictive relevance (Q²). Model fit indices (SRMR, NFI) for overall adequacy. Construct Relationships Technical Infrastructure, Organizational Readiness, and Governance Environment are modeled as predictors of AI Outcomes. Results highlight the dominant role of Technical Infrastructure (β ≈ 0.657) and significant contribution of Governance Enviroment (β ≈ 0.206), while Organizational Readiness shows negligible influence (β ≈ 0.016). Index Derivation The report also includes the path-based weights used to compute the GCC AI Adoption Index, rescaled to a 0–100 range. Purpose This report serves as the technical backbone of the study, allowing other researchers to audit, replicate, or extend the model. By providing full outputs (tables, figures, and algorithmic settings), it strengthens the study’s credibility and supports transparent, evidence-based policy design for AI adoption in the GCC.