Big data and analytics in hospitality and tourism: a systematic literature review

Purpose – This work surveys the body of research revolving around big data and analytics in hospitality and tourism by detecting macro topical areas, research streams and gaps, and develops an agenda for future research. Design/methodology/approach – This research is based on a systematic literature review of academic papers indexed in the Scopus and Web of Science databases published up to 31 December 2020. The outputs were analyzed using bibliometric techniques, network analysis and topic modeling. Findings – The number of scientific outputs in research with hospitality and tourism settings has been expanding over the period 2015–2020, with a substantial stability of the areas examined. The vast majority are published in academic journals where the main reference area is neither hospitality nor tourism. The body of research is rather fragmented and studies on relevant aspects, such as big data analytics capabilities, are virtually missing. Most of the outputs are empirical. Moreover, many of the articles collected relatively small quantities of records and, regardless of the time period considered, only a handful of articles mix a number of different techniques. Research limitations/implications – This study is centered on academic outputs published to the end of 2020 (the last year for which we have full-year data available). Implications are discussed. Originality/value – This work sheds new light on the emergence of a body of research at the intersection of hospitality and tourism management and data science. It enriches and complements extant literature reviews on big data and analytics, combining these two interconnected topics.