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Market segments based on the dominant movement patterns of tourists

Author

Listed:
  • Xia, Jianhong (Cecilia)
  • Evans, Fiona H.
  • Spilsbury, Katrina
  • Ciesielski, Vic
  • Arrowsmith, Colin
  • Wright, Graeme

Abstract

This paper presents an innovative method for tourist market segmentation-based on dominant movement patterns of tourists; that is, the travel sequences or patterns used by tourists most frequently. There were three steps to achieve this goal. In the first step, general log-linear models were adopted to identify the dominant movement patterns, while the second step was to discover the characteristics of the groups of tourists who travelled with these patterns. The Expectation–Maximisation algorithm was then used to partition tourist segments in terms of socio-demographic and travel behavioural variables. The third step was to select target markets based upon the earlier analysis. These methods were applied to a sample of tourists, over the period of a week, on Phillip Island, Victoria, Australia. A significant outcome of this research is that it will assist tourism organisations to identify tourism market segments and develop better tour packages and more efficient marketing strategies aligned to the characteristics of the tourists.

Suggested Citation

  • Xia, Jianhong (Cecilia) & Evans, Fiona H. & Spilsbury, Katrina & Ciesielski, Vic & Arrowsmith, Colin & Wright, Graeme, 2010. "Market segments based on the dominant movement patterns of tourists," Tourism Management, Elsevier, vol. 31(4), pages 464-469.
    • Handle: RePEc:eee:touman:v:31:y:2010:i:4:p:464-469
    DOI: 10.1016/j.tourman.2009.04.013
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    Citations

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    Cited by:

    1. Ana Muñoz-Mazón & Laura Fuentes-Moraleda & Angela Chantre-Astaiza & Marlon-Felipe Burbano-Fernandez, 2019. "The Study of Tourist Movements in Tourist Historic Cities: A Comparative Analysis of the Applicability of Four Different Tools," Sustainability, MDPI, vol. 11(19), pages 1-26, September.
    2. OROIAN, Maria & RATIU, Ramona-Flavia & GHERES, Marinela, 2013. "Using The Residents’ Profile As Potential Tourists In Tourist Market Segmentation: The Case Of Mures County, Romania," Academica Science Journal, Economica Series, Dimitrie Cantemir University, Faculty of Economical Science, vol. 1(2), pages 21-34, May.
    3. Smallwood, Claire B. & Beckley, Lynnath E. & Moore, Susan A., 2012. "An analysis of visitor movement patterns using travel networks in a large marine park, north-western Australia," Tourism Management, Elsevier, vol. 33(3), pages 517-528.
    4. Vu, Huy Quan & Li, Gang & Law, Rob & Ye, Ben Haobin, 2015. "Exploring the travel behaviors of inbound tourists to Hong Kong using geotagged photos," Tourism Management, Elsevier, vol. 46(C), pages 222-232.
    5. Dahao Zhang & Chunshan Zhou & Dongqi Sun & Ying Qian, 2022. "The influence of the spatial pattern of urban road networks on the quality of business environments: the case of Dalian City," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(7), pages 9429-9446, July.
    6. Seohee Park & Seongeun Kim & Ji Hoon Ryoo, 2020. "Latent Class Regression Utilizing Fuzzy Clusterwise Generalized Structured Component Analysis," Mathematics, MDPI, vol. 8(11), pages 1-16, November.
    7. De Cantis, Stefano & Ferrante, Mauro & Kahani, Alon & Shoval, Noam, 2016. "Cruise passengers' behavior at the destination: Investigation using GPS technology," Tourism Management, Elsevier, vol. 52(C), pages 133-150.
    8. Jesús Barreal & Berta Ferrer-Rosell & Eduard Cristobal-Fransi & Gil Jannes, 2021. "Influence of Service Valuation and Package Cost on Market Segmentation: The Case of Online Demand for Spanish and Andorra Ski Resorts," Sustainability, MDPI, vol. 13(5), pages 1-20, March.
    9. Lorenzo Masiero & Judit Zoltan, 2012. "Tourists intra-destination visits and transportation mode : a bivariate model," Quaderni della facoltà di Scienze economiche dell'Università di Lugano 1205, USI Università della Svizzera italiana.
    10. Hugo Padrón-Ávila & Raúl Hernández-Martín, 2019. "Preventing Overtourism by Identifying the Determinants of Tourists’ Choice of Attractions," Sustainability, MDPI, vol. 11(19), pages 1-17, September.
    11. Angela Chantre-Astaiza & Laura Fuentes-Moraleda & Ana Muñoz-Mazón & Gustavo Ramirez-Gonzalez, 2019. "Science Mapping of Tourist Mobility 1980–2019. Technological Advancements in the Collection of the Data for Tourist Traceability," Sustainability, MDPI, vol. 11(17), pages 1-32, August.
    12. Alessandro Crivellari & Euro Beinat, 2020. "LSTM-Based Deep Learning Model for Predicting Individual Mobility Traces of Short-Term Foreign Tourists," Sustainability, MDPI, vol. 12(1), pages 1-18, January.
    13. Rodríguez, Beatriz & Molina, Julián & Pérez, Fátima & Caballero, Rafael, 2012. "Interactive design of personalised tourism routes," Tourism Management, Elsevier, vol. 33(4), pages 926-940.
    14. Xia, Jianhong (Cecilia) & Zeephongsekul, Panlop & Packer, David, 2011. "Spatial and temporal modelling of tourist movements using Semi-Markov processes," Tourism Management, Elsevier, vol. 32(4), pages 844-851.
    15. Zheng, Weimin & Liao, Zhixue & Qin, Jing, 2017. "Using a four-step heuristic algorithm to design personalized day tour route within a tourist attraction," Tourism Management, Elsevier, vol. 62(C), pages 335-349.
    16. Zheng, Weimin & Huang, Xiaoting & Li, Yuan, 2017. "Understanding the tourist mobility using GPS: Where is the next place?," Tourism Management, Elsevier, vol. 59(C), pages 267-280.
    17. Zhou, Heng & Norman, Richard & Kelobonye, Keone & Xia, Jianhong (Cecilia) & Hughes, Brett & Nikolova, Gabi & Falkmer, Torbjorn, 2020. "Market segmentation approach to investigate existing and potential aviation markets," Transport Policy, Elsevier, vol. 99(C), pages 120-135.

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