ANALYSING VISITOR BEHAVIOUR IN TOURISM DESTINATIONS: EVIDENCE FROM USER RATINGS AND PREFERENCES
DOI:
https://doi.org/10.69980/958mc855Abstract
The growing application of the digital platforms has changed the manner in which visitor behaviour in the tourism destinations is analysed with the user-generated ratings and preferences being some of the crucial indicators of the visitor’s response. This research explored visitor behaviour in a quantitative research methodology that was based on secondary data. The problem was analysed in terms of the effects of destination attributes on the ratings of visitors that were considered as proxies of behavioural responses. The descriptive statistics, correlation and regression analyses were used to establish the patterns and relationships in the data. The analysis noted that visitor rating was largely skewed towards positive ratings, which suggest that there are positive perceptions of tourism destinations. The destination characteristics that were identified to have a significant impact on visitor reaction were: category, popularity, and accessibility. Also, there were several behavioural patterns identified between tourists, and this indicated that there was a difference in the evaluation styles and preferences. This discovery of the various visitor segments further highlighted the heterogeneity of tourist behaviour and the need to consider such differences in the study of tourist behaviour. These results are shown to indicate that user created rating data can be a useful source of information concerning visitor behaviour in tourism situations. The research has a contribution to the research on behavioural tourism by offering a data-grounded view of the perception of destination by tourists and the development of tastes and preferences. It also provides practical implication to tourism stakeholders in enhancing the management of the destination, and the experience of the visitor.
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References
1.Burkov, I., & Gorgadze, A. (2023). From text to insights: understanding museum consumer behavior through text mining TripAdvisor reviews. International Journal of Tourism Cities, 9(3), 712-728.
2.Dong, W., Kang, Q., Wang, G., Zhang, B., & Liu, P. (2023). Spatiotemporal behavior pattern differentiation and preference identification of tourists from the perspective of ecotourism destination based on the tourism digital footprint data. Plos one, 18(4), e0285192.
3.Gan, M., & Ouyang, Y. (2022). Study on tourism consumer behavior characteristics based on big data analysis. Frontiers in psychology, 13, 876993.
4.Garner, B., & Kim, D. (2022). Analyzing user-generated content to improve customer satisfaction at local wine tourism destinations: an analysis of Yelp and TripAdvisor reviews. Consumer behavior in tourism and hospitality, 17(4), 413-435.
5.Ghosh, P., & 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, 6(2), 754-778.
6.Islam, M. R., Abdul Kader Jilani, M. M., Miah, S. J., Akter, S., & Ulhaq, A. (2021). Discovering tourist preference for electing destinations: A pattern mining based approach. Asia Pacific Journal of Tourism Research, 26(10), 1081-1096.
7.Krataithong, P., Anutariya, C., & Buranarach, M. (2025). A tourist behavior analysis using probabilistic-based tourist trip inference from taxi GPS and social media data. New Generation Computing, 43(4), 16.
8.Leung, R., & Handler, I. (2024). Unveiling tourist typology: using online reviews and LDA to understand motivations for visiting Kyoto's prominent temples. International Journal of Tourism Cities.
9.Li, J., & Cao, B. (2022). Study on tourism consumer behavior and countermeasures based on big data. Computational Intelligence and Neuroscience, 2022(1), 6120511.
10.Li, X., Yang, T., Meng, B., Chen, S., & Zhang, S. (2025). Analysis of Tourist Behaviour Patterns and Perceptions in Beijing Based on User-Generated Content Data. Applied Spatial Analysis and Policy, 18(3), 108.
11.Möhring, M., Keller, B., Schmidt, R., & Dacko, S. (2021). Google popular times: towards a better understanding of tourist customer patronage behaviour. Tourism Review, 76(3), 533-569.
12.Peng, J., & Deng, J. (2024). Predicting Chinese tourists’ B&B preferences through a method of online reviews causality analytic. Information Processing & Management, 61(3), 103634.
13.Prabowo, A. (2021). Indonesia tourism destination [Data set]. Kaggle. https://www.kaggle.com/datasets/aprabowo/indonesia-tourism-destination.
14.Qori'atunnadyah, M., Murni, C. K., Choiri, A. F., Marianto, H., & Yazid, M. (2025). Tourist Preference Analysis Based on Google Reviews Using the DBSCAN Method. Enthusiastic: International Journal of Applied Statistics and Data Science, 178-189.
15.Ramadhani, D. P., Alamsyah, A., Febrianta, M. Y., & Damayanti, L. Z. A. (2024). Exploring tourists’ behavioral patterns in Bali’s top-rated destinations: perception and mobility. Journal of Theoretical and Applied Electronic Commerce Research, 19(2), 743-773.
16.Ramadhani, D. P., Alamsyah, A., Febrianta, M. Y., Fajriananda, M. N., Nada, M. S., & Hasanah, F. (2025). Large-scale cross-cultural tourism analytics: Integrating transformer-based text mining and network analysis. Computers, 14(1), 27.
17.Riswanto, A. L., Kim, S., & Kim, H. S. (2023). Analyzing online reviews to uncover customer satisfaction factors in Indian cultural tourism destinations. Behavioral Sciences, 13(11), 923.
18.Saydam, M. B., Olorunsola, V. O., Avci, T., Dambo, T. H., & Beyar, K. (2022). How about the service perception during the COVID-19 pandemic: an analysis of tourist experiences from user-generated content on TripAdvisor. Tourism Critiques: Practice and Theory, 3(1), 16-41.
19.Singgalen, Y. A. (2024). Understanding Visitor Sentiment of Batu Cave Destination through TripAdvisor and Vlogger Content Reviews. Journal of Information Systems and Informatics, 6(2), 899-918.
20.Sulu, D., Arasli, H., & Saydam, M. B. (2021). Air-travelers’ perceptions of service quality during the COVID-19 pandemic: evidence from tripadvisor sites. Sustainability, 14(1), 435.
21.Tripathi, G., & Wasan, P. (2021). Positioning of tourist destinations in the digital era: a review of online customer feedback. Worldwide Hospitality and Tourism Themes, 13(2), 275-290.
22.Wang, W. C., & Lin, C. H. (2024). Exploring the importance of destination attributes of sustainable urban waterfronts: Text and data mining of tourists’ online reviews. Sustainability, 16(6), 2271.
23.Xue, L., & Zhang, Y. (2020). The effect of distance on tourist behavior: A study based on social media data. Annals of Tourism Research, 82, 102916.
24.Yang, C., Luchen, Z., & Yuexing, T. (2025). Understanding and Managing Behavioral Preferences of Tourists and Local Residents: An Empirical Study on Ice-and-Snow Tourism in Northeast China. China City Planning Review, 34(4), 32-43.
