Replication Data for: SmartPLS Analysis Report Description

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 Capability, and Governance are modeled as predictors of AI Outcomes. Results highlight the dominant role of Technical Infrastructure (β ≈ 0.657) and significant contribution of Governance (β ≈ 0.206), while Organizational Capability 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.

SmartPLS分析报告（SmartPLS Analysis Report）记录了用于验证海湾合作委员会（Gulf Cooperation Council，GCC）人工智能采用指数的偏最小二乘结构方程模型（Partial Least Squares Structural Equation Modeling，PLS-SEM）流程。该报告以透明、分步的方式阐述了测量模型与结构模型的评估过程，确保本研究的结果具备可复现性、可解释性与稳健性。

核心内容
测量模型检验：包含信度指标（克朗巴哈α系数（Cronbach’s α）、组合信度（Composite Reliability）、平均方差抽取量（Average Variance Extracted，AVE））、反映性构念与构成性构念的外部载荷与权重，以及区分效度（福内尔-拉克准则（Fornell–Larcker criterion）、异特质-单特质比率（Heterotrait-Monotrait Ratio，HTMT））。
结构模型结果：包含经自助法（Bootstrap）检验得到t值与p值的路径系数（β值）、表征模型解释能力的R²与调整后R²值、效应量（f²）与预测相关性（Q²），以及用于评估模型整体适配度的模型拟合指标（标准化根均方残差（Standardized Root Mean Square Residual，SRMR）、规范拟合指数（Normed Fit Index，NFI））。
构念关系：将技术基础设施（Technical Infrastructure）、组织能力（Organizational Capability）与治理（Governance）设定为人工智能产出（AI Outcomes）的预测变量。研究结果显示，技术基础设施（β≈0.657）发挥主导作用，治理（β≈0.206）具有显著贡献，而组织能力的影响可忽略不计（β≈0.016）。
指数推导：本报告还包含用于计算海湾合作委员会人工智能采用指数的路径权重，并将其重新缩放至0-100区间。
研究目的：本报告作为本研究的技术支撑框架，可供其他研究人员审核、复现或拓展该模型。通过完整呈现输出结果（表格、图表与算法设置），本报告提升了研究的可信度，并为海湾合作委员会区域人工智能采用相关的透明化、循证政策制定提供支持。