151195 20251017144620.0 doi 10.3390/fi17030096 sideral 142974 ART-2025-142974 eng de Curtò, J. An Institutional Theory Framework for Leveraging Large Language Models for Policy Analysis and Intervention Design 2025 Access copy available to the general public Unrestricted This study proposes a comprehensive framework for integrating data-driven approaches into policy analysis and intervention strategies. The methodology is structured around five critical components: data collection, historical analysis, policy impact assessment, predictive modeling, and intervention design. Leveraging data-driven approaches capabilities, the line of work enables advanced multilingual data processing, advanced statistics in population trends, evaluation of policy outcomes, and the development of evidence-based interventions. A key focus is on the theoretical integration of social order mechanisms, including communication modes as institutional structures, token optimization as an efficiency mechanism, and institutional memory adaptation. A mixed methods approach was used that included sophisticated visualization techniques and use cases in the hospitality sector, in global food security, and in educational development. The framework demonstrates its capacity to inform government and industry policies by leveraging statistics, visualization, and AI-driven decision support. We introduce the concept of “institutional intelligence”—the synergistic integration of human expertise, AI capabilities, and institutional theory—to create adaptive yet stable policy-making systems. This research highlights the transformative potential of data-driven approaches combined with large language models in supporting sustainable and inclusive policy-making processes. info:eu-repo/semantics/openAccess by https://creativecommons.org/licenses/by/4.0/deed.es info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion de Zarzà, I. Universidad de Zaragoza (orcid)0000-0002-5844-7871 Fervier, L.S. Sanagustín-Fons, V. Universidad de Zaragoza (orcid)0000-0002-3957-2466 Calafate, C.T. 4009 775 Universidad de Zaragoza Dpto. Psicología y Sociología Área Sociología 5007 570 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Lenguajes y Sistemas Inf. 17, 3 (2025), 96 [28 pp.] FUTURE INTERNET 1999-5903 ADR Interactive Map https://public.flourish.studio/story/2733675/ Visualization sequence with booking patterns and cancellation rates https://public.tableau.com/app/profile/decurto/viz/Tendnciesdereservesdhotelsilescancellacions/Story1 Sunburst visualization https://decurto01.netlify.app/ GNN training metrics, model performance data, and visualization outputs are available via the Weights & Biases platform https://api.wandb.ai/links/decurto-universidad-pontificia-comillas/kvhl87um Food Security, Dashboard: https://foodsecurity-decurto.streamlit.app/ Educational Development, Dashboard: https://globaleducation-dezarza.streamlit.app/ Visualizations in Tableau. Food Security: https://public.tableau.com/app/profile/decurto/viz/GlobalFoodSecurityComparingKeyIndicatorsAcrossCountries/Sheet1 Visualizations in Tableau. Educational Development: https://public.tableau.com/app/profile/dezarza/viz/AdultLiteracyRatesinSub-SaharanAfrica/Sheet1 16866352 http://zaguan.unizar.es/record/151195/files/texto_completo.pdf Versión publicada 2548095 http://zaguan.unizar.es/record/151195/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:151195 articulos driver 2025-10-17-14:21:36 ARTICLE