Replication Data for: Appendix A. Codebook for GCC AI Strategy Document Analysis (v2)

This codebook provides a structured framework for analyzing the six Gulf Cooperation Council (GCC) National AI Strategies (NASs) with a focus on capacity-building references. It links policy priorities explicitly to the constructs used in our empirical model, Technical Infrastructure (TI), Organizational Capability (OC), and Governance (GOV), and supports alignment with survey items Q2–Q12. The codebook combines qualitative categorization and quantitative weighting. Each NAS passage is coded into a primary category (TI, OC, or GOV) using well-defined sub-codes, while secondary tags capture intended outcomes (AIO: service efficiency, citizen trust, performance) and contextual dimensions (e.g., sector, localization, cultural factors). This ensures that both policy intentions and implementation pathways are captured consistently across countries. To assess emphasis on specific AI capabilities (for example, NLP, robotics, cybersecurity), the codebook applies term-salience analysis (TF–IDF) within the coded passages. This allows us to differentiate between universally mentioned enablers, such as AI ethics or cybersecurity, and more specialized but heavily emphasized capabilities, such as deep learning or robotics, that feature in only some national strategies. The design emphasizes reproducibility and rigor: coders follow clear inclusion/exclusion rules, use EN/AR synonym lexicons, and apply double-coding with inter-coder reliability checks (target κ ≥ 0.79). Outputs include normalized construct-level signals per country and comparative TF–IDF tables, which are then triangulated with survey and PLS-SEM findings. In sum, the codebook acts as a bridge between policy texts and empirical analysis, enabling transparent, comparable, and replicable measurement of AI adoption enablers across the GCC.