IDEAS home Printed from https://ideas.repec.org/a/sae/toueco/v14y2008i3p445-468.html
   My bibliography  Save this article

Developing a Web-Based Tourism Demand Forecasting System

Author

Listed:
  • Haiyan Song
  • Stephen F. Witt
  • Xinyan Zhang

Abstract

Tourism demand is the foundation on which all tourism-related business decisions ultimately rest and so accurate forecasts of tourism demand are crucial for tourism industry practitioners. From the functional point of view, a tourism demand forecasting system (TDFS) is a forecasting support system capable of providing quantitative tourism demand forecasts and allowing users to make their own ‘what-if’ scenario forecasts. From the technical point of view, a TDFS is an information system consisting of a set of computer-based modules or components that support tourism demand forecasting and scenario analysis. This paper establishes a widely accessible Web-based TDFS which not only takes advantage of advanced econometric tourism demand forecasting techniques but also incorporates the real-time judgemental contribution of experts in the field. Furthermore, scenario forecasts are permitted within the system. Built on Web-based technology, the system provides advanced information sharing and communication and brings considerable convenience to various stakeholders engaged in tourism demand forecasting at different locations. In attempting to generate more accurate tourism demand forecasts, the system is designed to incorporate a two-stage forecasting methodology, which integrates judgemental adjustments with statistically based forecasts. The software architecture, detailed components and development environment of the Web-based TDFS are described in detail. A three-tiered client–server architecture is employed, which offers great flexibility, reusability and reliability. The prototype system has been developed and screen shots of interaction with the system are presented using Hong Kong tourism as an example.

Suggested Citation

  • Haiyan Song & Stephen F. Witt & Xinyan Zhang, 2008. "Developing a Web-Based Tourism Demand Forecasting System," Tourism Economics, , vol. 14(3), pages 445-468, September.
    • Handle: RePEc:sae:toueco:v:14:y:2008:i:3:p:445-468
    DOI: 10.5367/000000008785633578
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.5367/000000008785633578
    Download Restriction: no
    File URL: https://libkey.io/10.5367/000000008785633578?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Paul Goodwin, 2005. "How to Integrate Management Judgment with Statistical Forecasts," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 1, pages 8-12, June.
    2. Haiyan Song & Peter Romilly & Xiaming Liu, 2000. "An empirical study of outbound tourism demand in the UK," Applied Economics, Taylor & Francis Journals, vol. 32(5), pages 611-624.
    3. Song, Haiyan & Witt, Stephen F. & Jensen, Thomas C., 2003. "Tourism forecasting: accuracy of alternative econometric models," International Journal of Forecasting, Elsevier, vol. 19(1), pages 123-141.
    4. Kuo, R. J., 2001. "A sales forecasting system based on fuzzy neural network with initial weights generated by genetic algorithm," European Journal of Operational Research, Elsevier, vol. 129(3), pages 496-517, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Kim, Jae H. & Wong, Kevin & Athanasopoulos, George & Liu, Shen, 2011. "Beyond point forecasting: Evaluation of alternative prediction intervals for tourist arrivals," International Journal of Forecasting, Elsevier, vol. 27(3), pages 887-901.
    2. Liu, Anyu & Vici, Laura & Ramos, Vicente & Giannoni, Sauveur & Blake, Adam, 2021. "Visitor arrivals forecasts amid COVID-19: A perspective from the Europe team," Annals of Tourism Research, Elsevier, vol. 88(C).
    3. Arvan, Meysam & Fahimnia, Behnam & Reisi, Mohsen & Siemsen, Enno, 2019. "Integrating human judgement into quantitative forecasting methods: A review," Omega, Elsevier, vol. 86(C), pages 237-252.
    4. Song, Haiyan & Gao, Bastian Z. & Lin, Vera S., 2013. "Combining statistical and judgmental forecasts via a web-based tourism demand forecasting system," International Journal of Forecasting, Elsevier, vol. 29(2), pages 295-310.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ferda Halicioglu, 2010. "An Econometric Analysis of the Aggregate Outbound Tourism Demand of Turkey," Tourism Economics, , vol. 16(1), pages 83-97, March.
    2. Ferda Halicioglu, 2005. "An Ardl Model Of Aggregate Tourism Demand For Turkey," International Trade 0503005, University Library of Munich, Germany.
    3. Gunter, Ulrich & Önder, Irem, 2016. "Forecasting city arrivals with Google Analytics," Annals of Tourism Research, Elsevier, vol. 61(C), pages 199-212.
    4. Song, Haiyan & Qiu, Richard T.R. & Park, Jinah, 2019. "A review of research on tourism demand forecasting," Annals of Tourism Research, Elsevier, vol. 75(C), pages 338-362.
    5. Willem A. Naudé & Andrea Saayman, 2005. "Determinants of Tourist Arrivals in Africa: A Panel Data Regression Analysis," Tourism Economics, , vol. 11(3), pages 365-391, September.
    6. Peng, Bo & Song, Haiyan & Crouch, Geoffrey I., 2014. "A meta-analysis of international tourism demand forecasting and implications for practice," Tourism Management, Elsevier, vol. 45(C), pages 181-193.
    7. Jacky S. Charles & Thomas M. Fullerton Jr, 2012. "Research Note: An Error Correction Analysis of Visitor Arrivals in the Bahamas," Tourism Economics, , vol. 18(1), pages 253-259, February.
    8. Haiyan Song & Egon Smeral & Gang Li & Jason L. Chen, 2008. "Tourism Forecasting: Accuracy of Alternative Econometric Models Revisited," WIFO Working Papers 326, WIFO.
    9. Li, Xin & Pan, Bing & Law, Rob & Huang, Xiankai, 2017. "Forecasting tourism demand with composite search index," Tourism Management, Elsevier, vol. 59(C), pages 57-66.
    10. Gunter, Ulrich & Önder, Irem, 2015. "Forecasting international city tourism demand for Paris: Accuracy of uni- and multivariate models employing monthly data," Tourism Management, Elsevier, vol. 46(C), pages 123-135.
    11. Guizzardi, Andrea & Mazzocchi, Mario, 2010. "Tourism demand for Italy and the business cycle," Tourism Management, Elsevier, vol. 31(3), pages 367-377.
    12. Syntetos, Aris A. & Kholidasari, Inna & Naim, Mohamed M., 2016. "The effects of integrating management judgement into OUT levels: In or out of context?," European Journal of Operational Research, Elsevier, vol. 249(3), pages 853-863.
    13. Allison Zhou & Carl Bonham & Byron Gangnes, 2007. "Modeling the supply and demand for tourism: a fully identified VECM approach," Working Papers 200717, University of Hawaii at Manoa, Department of Economics.
    14. Carl Bonham & Peter Fuleky & James Jones & Ashley Hirashima, 2015. "Nowcasting Tourism Industry Performance Using High Frequency Covariates," Working Papers 2015-3, University of Hawaii Economic Research Organization, University of Hawaii at Manoa.
    15. Ioannis Chatziantoniou & Stavros Degiannakis & Bruno Eeckels & George Filis, 2016. "Forecasting tourist arrivals using origin country macroeconomics," Applied Economics, Taylor & Francis Journals, vol. 48(27), pages 2571-2585, June.
    16. Nyoni, Thabani, 2019. "Where is Eritrea going in terms of population growth? Insights from the ARIMA approach," MPRA Paper 92435, University Library of Munich, Germany.
    17. Cernat, Lucian & Gourdon, Julien, 2005. "Is the concept of sustainable tourism sustainable? Developing the Sustainable Tourism Benchmarking Tool," MPRA Paper 4178, University Library of Munich, Germany.
    18. Stephen F. Witt & Haiyan Song & Stephen Wanhill, 2004. "Forecasting Tourism-Generated Employment: The Case of Denmark," Tourism Economics, , vol. 10(2), pages 167-176, June.
    19. Chien-Ming Wang & Su-Lan Pan & Alastair M. Morrison & Tsung-Pao Wu, 2022. "The dynamic linkages among outbound tourism, economic growth, and international trade: empirical evidence from China," SN Business & Economics, Springer, vol. 2(11), pages 1-18, November.
    20. -, 2011. "An assessment of the economic impact of climate change on the tourism sector in Jamaica," Sede Subregional de la CEPAL para el Caribe (Estudios e Investigaciones) 38606, Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:toueco:v:14:y:2008:i:3:p:445-468. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.