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  • 1.
    Mariani, Marcello
    et al.
    University of Reading, Berks, England.
    Baggio, Rodolfo
    Bocconi University, Italy; Tomsk Polytechnic University, Tomsk, Russia.
    Fuchs, Matthias
    Mid Sweden University, Faculty of Human Sciences, Department of Tourism Studies and Geography.
    Höpken, Wolfram
    Universtiy of Applied Sience, Weingarten-Ravensburg, Germany.
    Business intelligence and big data in hospitality and tourism: A systematic literature review2018In: International Journal of Contemporary Hospitality Management, ISSN 0959-6119, E-ISSN 1757-1049, Vol. 30, no 12, p. 3514-3554Article in journal (Refereed)
    Abstract [en]

    Purpose

    This paper aims to examine the extent to which Business Intelligence and Big Data feature within academic research in hospitality and tourism published until 2016, by identifying research gaps and future developments and designing an agenda for future research.

    Design/methodology/approach

    The study consists of a systematic quantitative literature review of academic articles indexed on the Scopus and Web of Science databases. The articles were reviewed based on the following features: research topic; conceptual and theoretical characterization; sources of data; type of data and size; data collection methods; data analysis techniques; and data reporting and visualization.

    Findings

    Findings indicate an increase in hospitality and tourism management literature applying analytical techniques to large quantities of data. However, this research field is fairly fragmented in scope and limited in methodologies and displays several gaps. A conceptual framework that helps to identify critical business problems and links the domains of business intelligence and big data to tourism and hospitality management and development is missing. Moreover, epistemological dilemmas and consequences for theory development of big data-driven knowledge are still a terra incognita. Last, despite calls for more integration of management and data science, cross-disciplinary collaborations with computer and data scientists are rather episodic and related to specific types of work and research.

    Research limitations/implications

    This work is based on academic articles published before 2017; hence, scientific outputs published after the moment of writing have not been included. A rich research agenda is designed.

    Originality/value

    This study contributes to explore in depth and systematically to what extent hospitality and tourism scholars are aware of and working intendedly on business intelligence and big data. To the best of the authors’ knowledge, it is the first systematic literature review within hospitality and tourism research dealing with business intelligence and big data.

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