Digital transformation in tourism: bibliometric literature review based on machine learning approach

Peter Madzík, Lukáš Falát, Lukáš Copuš and Marco Valeri

European Journal of Innovation Management, 2023, vol. 26, issue 7, 177-205

Abstract: Purpose - This bibliometric study provides an overview of research related to digital transformation (DT) in the tourism industry from 2013 to 2022. The goals of the research are as follows: (1) to identify the development of academic papers related to DT in the tourism industry, (2) to analyze dominant research topics and the development of research interest and research impact over time and (3) to analyze the change in research topics during the pandemic. Design/methodology/approach - In this study, the authors processed 3,683 papers retrieved from the Web of Science and Scopus. The authors performed different types of bibliometric analyses to identify the development of papers related to DT in the tourism industry. To reveal latent topics, the authors implemented topic modeling based on latent Dirichlet allocation with Gibbs sampling. Findings - The authors identified eight topics related to DT in the tourism industry: City and urban planning, Social media, Data analytics, Sustainable and economic development, Technology-based experience and interaction, Cultural heritage, Digital destination marketing and Smart tourism management. The authors also identified seven topics related to DT in the tourism industry during the Covid-19 pandemic; the largest ones are smart analytics, marketing strategies and sustainability. Originality/value - To identify research topics and their development over time, the authors applied a novel methodological approach – a smart literature review. This machine learning approach is able to analyze a huge amount of documents. At the same time, it can also identify topics that would remain unrevealed by a standard bibliometric analysis.

Keywords: Digital; Digitization; Digitalization; Digital transformation; Tourism; Bibliometric; Topic modeling; Machine learning; LDA; Latent Dirichlet allocation; Covid; Pandemic (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eme:ejimpp:ejim-09-2022-0531

DOI: 10.1108/EJIM-09-2022-0531

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