Intellectual landscape and emerging trends of big data research in hospitality and tourism: A scientometric analysis

Abstract

Big data contain a vast amount of information which is valuable for researchers and decision-makers both in normal and crisis situations. This bibliometric study aims to present the progress, theoretical foundations, and intellectual structure of big data analytics in the hospitality and tourism research domain. Literature records were collected via the Web of Science and screened to maximize relevance. The overall literature dataset included 1184 papers, comprising both review and empirical articles. From this dataset, 47 publications related to the COVID-19 pandemic were identified and formed a sub-dataset to capture the specific research focuses during the crisis. The research themes and their evolutionary paths were identified by keyword clustering and keyword Time Zone analysis. Co-citation analysis was implemented to visualize the intellectual structure. Based on the systematic review, this study proposes future research directions.