Source of Publication
Information and Management
© 2016 Elsevier B.V. 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.
Computer Sciences | Tourism and Travel
Big data, Predictive analytics, Strategic decision support, Tourism destination management, Tourist behaviour
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Miah, Shah Jahan; Vu, Huy Quan; Gammack, John; and McGrath, Michael, "A Big Data Analytics Method for Tourist Behaviour Analysis" (2017). All Works. 39.
Indexed in Scopus
Open Access Type
Green: A manuscript of this publication is openly available in a repository