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Abstract
With the increasing popularity of big data analytics, significant research has been conducted in the field of tourism and hospitality studies, particularly in the form of reviews that examine current works. Even though tourism is closely intertwined with the movements of tourists, a comprehensive review that investigates how big data analytics is utilised to track tourist mobility is still lacking. Hence, the primary objective of this study is to delve into the understanding of tracking tourists’ mobility through the utilisation of big data analytics, considering data sources, methodologies, theoretical contributions, limitations, and future research directions. To accomplish this, an extensive literature review was conducted, encompassing publications from tourism and hospitality journals spanning a decade, from 2013 to 2023. This paper thoroughly examines five distinct types of data sources and explains how they enable researchers to monitor and track tourists’ mobility for various research objectives. Moreover, the paper contributes to the academic discourse by identifying the gap in the literature where the application of theoretical frameworks has not kept pace with the advancements in data collection and analysis technologies. To bridge this gap, we introduce an innovative conceptual model that aligns the theoretical aspects of tourist studies with the practical application of big data, thereby offering a richer understanding of tourist mobility patterns. In addition, the research identifies and discusses the limitations of current studies, such as concerns regarding data privacy, representativeness, and the dichotomy between qualitative and quantitative data analysis in the field. By establishing a foundational framework and advocating for theoretical development, this paper sets a new standard for the integration of big data into tourism research, paving the way for future scholarly exploration.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Additional information
Notes on contributors
Jinyan Chen
Jinyan Chen is a Research Assistant Professor in the School of Hotel and Tourism Management at the Hong Kong Polytechnic University. She is also an adjunct fellow with the Griffith Institute for Tourism, Australia. Her main research interests include understanding tourist behaviour, modelling tourist travel patterns, sentiment analysis, network analysis, and relying on social media and big data analytics.
Noam Shoval
Noam Shoval is the Professor in the Department of Geography and the Institute for Urban and Regional Studies at the Hebrew University of Jerusalem. He is also Director of the European Forum and the Director of the Center for Urban Innovation at the Hebrew University of Jerusalem. His main research interests are urban geography and planning, urban tourism and the implementation of advanced tracking technologies in various areas of spatial research such as tourism and urban studies and medicine.
Bela Stantic
Bela Stantic is Professor in Computer Science and founder and Director of ‘Big Data and Smart Analytics’ Lab at Griffith University. Professor Stantic is internationally recognised in field of data analytics and efficient management of complex data structures, such as found in big data. He was invited to give many Keynotes and invited talks at highly ranked International conferences and prestigious institutions as well as he has been doing the journal editorial duties and reviews of many top ranked journals.