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dc.contributor.authorde Curtò i Díaz, Joaquimes-ES
dc.contributor.authorde Zarzà i Cubero, Irenees-ES
dc.contributor.authorFervier, Leandro Sebastiánes-ES
dc.contributor.authorSanagustín Fons, Victoriaes-ES
dc.contributor.authorCalafate, Carlos T.es-ES
dc.date.accessioned2025-03-18T15:22:13Z
dc.date.available2025-03-18T15:22:13Z
dc.date.issued2025-02-20es_ES
dc.identifier.issn1999-5903,es_ES
dc.identifier.urihttps://doi.org/10.3390/fi17030096es_ES
dc.descriptionArtículos en revistases_ES
dc.description.abstract.es-ES
dc.description.abstractThis 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.en-GB
dc.format.mimetypeapplication/pdfes_ES
dc.language.isoen-GBes_ES
dc.rightsCreative Commons Reconocimiento-NoComercial-SinObraDerivada Españaes_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/es_ES
dc.sourceRevista: Future internet, Periodo: 1, Volumen: 17, Número: 3, Página inicial: 96, Página final: .es_ES
dc.titleAn Institutional Theory Framework for Leveraging Large Language Models for Policy Analysis and Intervention Designes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.description.versioninfo:eu-repo/semantics/publishedVersiones_ES
dc.rights.holderes_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.keywords.es-ES
dc.keywordsdata-driven policy analysis; institutional theory; decision support systems; visual analytics; AI; large language models; predictive modeling; intervention design; graph neural networksen-GB


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