Understanding tourist behaviour towards destination selection based on social media information: an evaluation using unsupervised clustering algorithms
Journal of Hospitality and Tourism Insights
ISSN: 2514-9792
Article publication date: 19 April 2022
Issue publication date: 6 April 2023
Abstract
Purpose
The study aims to cluster the travellers based on their social media interactions as well as to find the different segments with similar and dissimilar categories according to traveller's choice. The study also aims to understand the behaviour of clusters of the travellers towards destination selection and accordingly make the tour packages in order to improve tourists' satisfaction and gain viable benefits.
Design/methodology/approach
Agglomerative hierarchical clustering with Ward's minimum variance linkage algorithm and model-based clustering with parameterized finite Gaussian mixture models has been implemented to achieve the respective goals. The dimension reduction (DR) technique was introduced for better visualizing clustering structure obtained from a finite mixture of Gaussian densities.
Findings
A total of 980 travellers have been clustered into 8 different interest groups according to their tourism destinations selection across East Asia based on individual social media feedback. For selecting the optimal number of clusters as well as the behaviour of the interested travellers groups, both these proposed methods have shown remarkable similarities. DR technique ensures the reduction in dimensionality with seven directions, of which the first two directions explained 95% of total variability.
Practical implications
Tourism organizations focus on marketing efforts to promote the most attractive benefits to the clusters of travellers. By segmenting travellers of East Asia into homogeneous groups, it is feasible to choose a similar area to test different marketing techniques. Finally, it can be identified to which segments, new respondents or potential clients belong; consequently, the tourism organizations can design the tour packages.
Originality/value
The study has uniqueness in two aspects. Firstly, the study empirically revealed tourists' experience and behavioural intention to select tourism destinations and secondly, it finds quantifiable insights into the tourism phenomenon in East Asia, which helps tourism organizations to understand the buying behaviours of tourists' segments. Finally, the application of clustering algorithms to achieve the purpose of this study and the findings are very new in the literature on tourism, to understand the tourist behaviour towards destination selection based on social media reviews.
Keywords
Acknowledgements
The authors would like to acknowledge the guidance and support received from the editor of this journal and the reviewers who helped to improve the work with their constructive feedback. The authors are very much thankful to the data donor Prof. Shini Renjith. The authors would like to acknowledge the UCI Machine Learning Repository for making this dataset available for future research.
Funding: No funding received.
Declaration of competing interest: None. The authors declare that there are no potential conflicts of interest with respect to the research, authorship and/or publication of this article.
Citation
Ghosh, P. and Mukherjee, S. (2023), "Understanding tourist behaviour towards destination selection based on social media information: an evaluation using unsupervised clustering algorithms", Journal of Hospitality and Tourism Insights, Vol. 6 No. 2, pp. 754-778. https://doi.org/10.1108/JHTI-11-2021-0317
Publisher
:Emerald Publishing Limited
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