000151195 001__ 151195
000151195 005__ 20250227101504.0
000151195 0247_ $$2doi$$a10.3390/fi17030096
000151195 0248_ $$2sideral$$a142974
000151195 037__ $$aART-2025-142974
000151195 041__ $$aeng
000151195 100__ $$ade Curtò, J.
000151195 245__ $$aAn Institutional Theory Framework for Leveraging Large Language Models for Policy Analysis and Intervention Design
000151195 260__ $$c2025
000151195 5060_ $$aAccess copy available to the general public$$fUnrestricted
000151195 5203_ $$aThis 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.
000151195 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000151195 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000151195 700__ $$0(orcid)0000-0002-5844-7871$$ade Zarzà, I.$$uUniversidad de Zaragoza
000151195 700__ $$aFervier, L.S.
000151195 700__ $$0(orcid)0000-0002-3957-2466$$aSanagustín-Fons, V.$$uUniversidad de Zaragoza
000151195 700__ $$aCalafate, C.T.
000151195 7102_ $$14009$$2775$$aUniversidad de Zaragoza$$bDpto. Psicología y Sociología$$cÁrea Sociología
000151195 7102_ $$15007$$2570$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Lenguajes y Sistemas Inf.
000151195 773__ $$g17, 3 (2025), 96 [28 pp.]$$tFUTURE INTERNET$$x1999-5903
000151195 787__ $$tADR Interactive Map$$tVisualization sequence with booking patterns and cancellation rates$$tSunburst visualization$$tGNN training metrics, model performance data, and visualization outputs are available via the Weights & Biases platform$$tFood Security, Dashboard:$$tEducational Development, Dashboard:$$tVisualizations in Tableau. Food Security:$$tVisualizations in Tableau. Educational Development:$$whttps://public.flourish.studio/story/2733675/$$whttps://public.tableau.com/app/profile/decurto/viz/Tendnciesdereservesdhotelsilescancellacions/Story1$$whttps://decurto01.netlify.app/$$whttps://api.wandb.ai/links/decurto-universidad-pontificia-comillas/kvhl87um$$whttps://foodsecurity-decurto.streamlit.app/$$whttps://globaleducation-dezarza.streamlit.app/$$whttps://public.tableau.com/app/profile/decurto/viz/GlobalFoodSecurityComparingKeyIndicatorsAcrossCountries/Sheet1$$whttps://public.tableau.com/app/profile/dezarza/viz/AdultLiteracyRatesinSub-SaharanAfrica/Sheet1
000151195 8564_ $$s16866352$$uhttps://zaguan.unizar.es/record/151195/files/texto_completo.pdf$$yVersión publicada
000151195 8564_ $$s2548095$$uhttps://zaguan.unizar.es/record/151195/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000151195 909CO $$ooai:zaguan.unizar.es:151195$$particulos$$pdriver
000151195 951__ $$a2025-02-27-09:27:29
000151195 980__ $$aARTICLE