Governing the use of big data and digital twin technology for sustainable tourism

Eko Rahmadian

    Research output: ThesisThesis fully internal (DIV)

    720 Downloads (Pure)

    Abstract

    The tourism industry is increasingly utilizing big data to gain valuable insights and enhance decision-making processes. The advantages of big data, such as real-time information, robust data processing capabilities, and improved stakeholder decision-making, make it a promising tool for analyzing various aspects of tourism, including sustainability. Moreover, integrating big data with prominent technologies like machine learning, artificial intelligence (AI), and the Internet of Things (IoT) has the potential to revolutionize smart and sustainable tourism.

    Despite the potential benefits, the use of big data for sustainable tourism remains limited, and its implementation poses challenges related to governance, data privacy, ethics, stakeholder communication, and regulatory compliance. Addressing these challenges is crucial to ensure the responsible and sustainable use of these technologies. Therefore, strategies must be developed to navigate these issues through a proper governing system.

    To bridge the existing gap, this dissertation focuses on the current research on big data for sustainable tourism and strategies for governing its use and implementation in conjunction with emerging technologies. Specifically, this PhD dissertation centers on mobile positioning data (MPD) as a case due to its unique benefits, challenges, and complexity. Also, this project introduces three frameworks, namely: 1) a conceptual framework for digital twins (DT) for smart and sustainable tourism, 2) a documentation framework for architectural decisions (DFAD) to ensure the successful implementation of the DT technology as a governance mechanism, and 3) a big data governance framework for official statistics (BDGF). This dissertation not only presents these frameworks and their benefits but also investigates the issues and challenges related to big data governance while empirically validating the applicability of the proposed frameworks.
    Original languageEnglish
    QualificationDoctor of Philosophy
    Awarding Institution
    • University of Groningen
    Supervisors/Advisors
    • Zwitter, Andrej, Supervisor
    • Feitosa, Daniel, Co-supervisor
    Award date15-Mar-2024
    Place of Publication[Groningen]
    Publisher
    DOIs
    Publication statusPublished - 2024

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