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An Institutional Theory Framework for Leveraging Large Language Models for Policy Analysis and Intervention Design

Author

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  • J. de Curtò

    (Department of Computer Applications in Science & Engineering, BARCELONA Supercomputing Center, 08034 Barcelona, Spain
    Escuela Técnica Superior de Ingeniería (ICAI), Universidad Pontificia Comillas, 28015 Madrid, Spain
    Estudis d’Informàtica, Multimèdia i Telecomunicació, Universitat Oberta de Catalunya, 08018 Barcelona, Spain)

  • I. de Zarzà

    (Estudis d’Informàtica, Multimèdia i Telecomunicació, Universitat Oberta de Catalunya, 08018 Barcelona, Spain
    Departamento de Informática e Ingeniería de Sistemas, Universidad de Zaragoza, 50009 Zaragoza, Spain)

  • Leandro Sebastián Fervier

    (Departamento de Psicología y Sociología, Universidad de Zaragoza, 50009 Zaragoza, Spain)

  • Victoria Sanagustín-Fons

    (Departamento de Psicología y Sociología, Universidad de Zaragoza, 50009 Zaragoza, Spain)

  • Carlos T. Calafate

    (Departamento de Informática de Sistemas y Computadores, Universitat Politècnica de València, 46022 València, Spain)

Abstract

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.

Suggested Citation

  • J. de Curtò & I. de Zarzà & Leandro Sebastián Fervier & Victoria Sanagustín-Fons & Carlos T. Calafate, 2025. "An Institutional Theory Framework for Leveraging Large Language Models for Policy Analysis and Intervention Design," Future Internet, MDPI, vol. 17(3), pages 1-28, February.
  • Handle: RePEc:gam:jftint:v:17:y:2025:i:3:p:96-:d:1595506
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    References listed on IDEAS

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    1. David J. Teece, 2007. "Explicating dynamic capabilities: the nature and microfoundations of (sustainable) enterprise performance," Strategic Management Journal, Wiley Blackwell, vol. 28(13), pages 1319-1350, December.
    2. Schneider, Anne & Ingram, Helen, 1988. "Systematically Pinching Ideas: A Comparative Approach to Policy Design," Journal of Public Policy, Cambridge University Press, vol. 8(1), pages 61-80, January.
    3. Kathleen M. Eisenhardt & Jeffrey A. Martin, 2000. "Dynamic capabilities: what are they?," Strategic Management Journal, Wiley Blackwell, vol. 21(10‐11), pages 1105-1121, October.
    4. Howlett, Michael, 2009. "Process Sequencing Policy Dynamics: Beyond Homeostasis and Path Dependency," Journal of Public Policy, Cambridge University Press, vol. 29(3), pages 241-262, December.
    5. Stephen H. Linder & B. Guy Peters, 1987. "A Design Perspective On Policy Implementation: The Fallacies Of Misplaced Prescription," Review of Policy Research, Policy Studies Organization, vol. 6(3), pages 459-475, February.
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