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Framework for a Tourism Intelligence System Based on Knowledge Governance: A Conceptual Model

In: Tourism and ICTs: Advances in Data Science, Artificial Intelligence and Sustainability

Author

Listed:
  • Luana Emmendoerfer

    (Universidade Federal de Santa Catarina)

  • Alexandre Augusto Biz

    (Universidade Federal de Santa Catarina)

  • Patrícia Sá Ferreira

    (Universidade Federal de Santa Catarina)

Abstract

The objective of this article is to present a framework of Tourism Intelligence System (TIS) with support in Knowledge Governance (GovC) to support decision making in tourist destinations. The form of cooperation and use of knowledge should be structured through mechanisms that allow availability and reliability. The methodological framework is structured in Design Science Research (DSR) of technological and applied nature, with data collection method using a qualitative approach, classified as exploratory and descriptive, from the validation of semi-structured interviews with experts in the tourism sector and technology. This architecture was composed of three layers: knowledge application, knowledge generation and application, and knowledge generation. It focuses on the extraction of data generated by the tourist trip in the pre-trip, during trip, and post-trip phases, using Knowledge Management (KM) processes such as knowledge identification, acquisition, and use. The GovC aspect considered the mechanisms aimed at the sustainability and evolution of the TIS, as well as the hybrid structure through network and market formation, by means of knowledge centers with actors involved in the segments of the tourism production chain.

Suggested Citation

  • Luana Emmendoerfer & Alexandre Augusto Biz & Patrícia Sá Ferreira, 2024. "Framework for a Tourism Intelligence System Based on Knowledge Governance: A Conceptual Model," Springer Proceedings in Business and Economics, in: Antonio J. Guevara Plaza & Alfonso Cerezo Medina & Enrique Navarro Jurado (ed.), Tourism and ICTs: Advances in Data Science, Artificial Intelligence and Sustainability, pages 227-235, Springer.
    • Handle: RePEc:spr:prbchp:978-3-031-52607-7_21
    DOI: 10.1007/978-3-031-52607-7_21
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