Big Data Research in the Tourism Industry: Requirements and Challenges

Introduction

Over the last decades, people and machines interactions have overloaded the world with an incredible digital data deluge. Indeed, roughly most of the world population interact with online services, publish content and perform electronic purchasing, etc. resulting thus in generating much more data (Haught, Wei, & Karlis, 2016). Besides, data generation has also been expanded due to the various Internet of Things (IoT) devices use modes. These examples are a part of a more general trend, commonly known as Big Data. This socio-technical phenomenon, identified as being one of the prominent IT trends in the world, has been observed in every sector and revolutionized almost every industry.

In the realm of tourism research, the existing literature reviews mainly focused either on the tourism destination (Giglio, Bertacchini, Bilotta, & Pantano, 2019; Höpken, Fuchs, & Lexhagen, 2017), the tourist behavior (Liu, Zhang, Zhang, Sun, & Qiu, 2019; Zheng et al., 2019) or the tourism class. As the tourism industry thrives on information, the potential of Big Data in tourism is immense; considering the increased capacity for professionals and tourism scientists to gather useful information from all travel stages, which will contribute to better understanding and exploring tourists’ behavior and to enhance the management and marketing of tourism destinations (Mariani, Baggio, Fuchs, & Höepken, 2018). Indeed, different Big Data sources concerning all tourism sectors have emerged, including mobile applications that incorporate user information, interactions and feedback (i.e. user-generated content); smart devices (e.g. sensors) deployed in IoT; Open Data portals and organizational and government-supported information systems (Li, Xu, Tang, Wang, & Li, 2018). Such data sources should be managed to promote innovation and contribute to tourism destination success.

Nevertheless, and to the best of our knowledge, there were only four relevant literature reviews about the application of Big Data to the tourism industry. Schuckert et al. examined the studies focusing on online reviews in the tourism sector (Schuckert, Liu, & Law, 2015); Shoval and Ahas conducted a literature review on tracking and monitoring mechanisms in tourism field (Shoval & Ahas, 2016); Rashidi et al. investigated the capacity of social media data for modeling travel behavior (Rashidi, Abbasi, Maghrebi, Hasan, & Waller, 2017); and Li et al. reviewed the different tourism-related Big Data types, their related issues, characteristics, and analytic techniques (Li et al., 2018). Obviously, each review deals with a particular Big Data source (i.e. online reviews blogs, tracking sensors, or social media platforms) except Li et al. that conduct an overall investigation of the different Big Data types in the tourism industry. Furthermore, all these studies were carried out from specific research perspectives (e.g. tourists' satisfaction and tourists' behavior) without fully considering Big Data aspects and the complexity of its management. Big Data is large and complex, with various formats, and continuously gathered from multiple sources, which requires convenient strategies and technologies to manage it.

Big Data has various facets of complexity and yields challenges associated with its exploitation (Siddiqa et al., 2016). Specifically, Big Data differs in terms of data source type, information and data characteristics, which certainly impacts its processing techniques, especially during the acquisition process which is a major component of smart tourism development (Gretzel, Sigala, Xiang, & Koo, 2015). In reality, the nature of the data sources and the way data is collected affect notably the way it is processed. Also, the manner data would be accessed and exploited determines how it should be manipulated. In this regard, this paper is primarily concerned to present a literature review on Big Data, particularly in tourism, in terms of data sources types and data characteristics. Then, this paper provides a systematical investigation on the process of acquisition of Big Data and expand its associated issues, including data access and gathering. Eventually, the current tools enabling the acquisition of data from a variety of sources are highlighted and some best practices are discussed for implementing effective data acquisition systems.