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Tourism Demand Analysis Using Structural Equation Modelling

Author

Listed:
  • Lindsay W. Turner

    (Associate Professor in Applied Economics, Department of Applied Economics, Victoria University of Technology, PO Box 14428 MC, Melbourne, Victoria 8001, Australia)

  • Yvette Reisinger

    (Senior Lecturer in Marketing, Department of Marketing, Monash University, Berwick, Melbourne, Victoria 3806, Australia)

  • Stephen F. Witt

    (Professor of Tourism Forecasting, School of Management Studies for the Service Sector, University of Surrey, Guildford, Surrey, GU2 5XH, UK
    Department of Applied Economics, Victoria University of Technology, PO Box 14428 MC, Melbourne, Victoria 8001, Australia)

Abstract

Structural equation modelling is used to analyse quarterly tourist flow data from the UK to seven major destinations over the period 1978 to 1995, where the visit data are disaggregated into holiday, business and VFR categories. The objective is to determine which of the major explanatory variables, such as income, price, retail sales and manufacturing confidence, account for each of the disaggregated flows. Econometric structural equation modelling (LISREL) is used because it is capable of measuring a simultaneous relationship between several explanatory variables and several dependent variables, and also any relationship between the different dependent variables. Historically, tourism demand modelling has focused on total or holiday flows, without attempting to compare the determinants of tourism demand for different purposes of visit to specific destinations. However, an understanding of the differences and similarities in demand for different visit purposes is critical for industry planning. Different industry sectors cater for different types of tourists to varying degrees, and the influencing factors for each purpose of visit are unlikely to be the same.

Suggested Citation

  • Lindsay W. Turner & Yvette Reisinger & Stephen F. Witt, 1998. "Tourism Demand Analysis Using Structural Equation Modelling," Tourism Economics, , vol. 4(4), pages 301-323, December.
    • Handle: RePEc:sae:toueco:v:4:y:1998:i:4:p:301-323
    DOI: 10.1177/135481669800400401
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    Citations

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    Cited by:

    1. Sen Cheong Kon & Lindsay W. Turner, 2005. "Neural Network Forecasting of Tourism Demand," Tourism Economics, , vol. 11(3), pages 301-328, September.
    2. Yap, Ghialy & Allen, David, 2011. "Investigating other leading indicators influencing Australian domestic tourism demand," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(7), pages 1365-1374.
    3. Yang, Yang & Fan, Yawen & Jiang, Lan & Liu, Xiaohui, 2022. "Search query and tourism forecasting during the pandemic: When and where can digital footprints be helpful as predictors?," Annals of Tourism Research, Elsevier, vol. 93(C).
    4. David Prescott & David Wilton & Canan Dadayli & Aaron Dickson, 2005. "Travel to Canada: the role of Canada's immigrant populations," Applied Economics, Taylor & Francis Journals, vol. 37(6), pages 651-663.
    5. Sudeshna Ghosh, 2021. "Business Confidence and Business Tourism in Japan," Journal of International Commerce, Economics and Policy (JICEP), World Scientific Publishing Co. Pte. Ltd., vol. 12(01), pages 1-23, February.
    6. Hassan F. Gholipour & Behzad Foroughi, 2020. "Corruption and outbound business travels," Tourism Economics, , vol. 26(7), pages 1266-1281, November.

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