A Big Data Analytics Method for Tourist Behaviour Analysis

By Shah Jahan Miah, Huy Quan Vu, John G. Gammack, Michael McGrath. 2017

339 citations recorded by the source corpus.

Abstract Big data generated across social media sites have created numerous opportunities for bringing more insights to decision-makers. Few studies on big data analytics, however, have demonstrated the support for strategic decision-making. Moreover, a formal method for analysing social media-generated big data for decision support is yet to be developed, particularly in the tourism sector. Using a design science research approach, this study aims to design and evaluate a ‘big data analytics’ method to support strategic decision-making in tourism destination management. Using geotagged photos uploaded by tourists to the photo-sharing social media site, Flickr, the applicability of the method in assisting destination management organisations to analyse and predict tourist behavioural patterns at specific destinations is shown, using Melbourne, Australia, as a representative case. Utility was confirmed using both another destination and directly with stakeholder audiences. The developed artefact demonstrates a method for analysing unstructured big data to enhance strategic decision making within a real problem domain. The proposed method is generic, and its applicability to other big data streams is discussed.

Subfields: Sociology and Political Science

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Citations in the corpus

A survey of data fusion in smart city applications (2019, methodology): “Examples of papers using such approaches can be found in [76], [77] • T9: Visualization Visualization is a technique used for the presentation of output to the end users via some platform.”