IDEAS home Printed from https://ideas.repec.org/a/sae/toueco/v20y2014i6p1349-1356.html
   My bibliography  Save this article

Research Note: Forecasting Film-Induced Tourism — The Dolphin Tale Case

Author

Listed:
  • Maria Luisa Cortón
  • Maling Ebrahimpour

Abstract

Dolphin Tale is a major motion picture released by Warner Brothers in 2011. The authors forecast the number of visitors to the film location, including the film-induced tourism effect originated by Dolphin Tale. They use intervention analysis to measure this effect, with the pre-film series forecasted trend as the comparison baseline instead of the usual linear trend. They address the proper modelling of the series seasonality in the presence of an abrupt increase in visitors immediately after the premiere and a lack of data on the subsequent months. They find that the film induced a 51% increase in the mean level of visitors to the location.

Suggested Citation

  • Maria Luisa Cortón & Maling Ebrahimpour, 2014. "Research Note: Forecasting Film-Induced Tourism — The Dolphin Tale Case," Tourism Economics, , vol. 20(6), pages 1349-1356, December.
  • Handle: RePEc:sae:toueco:v:20:y:2014:i:6:p:1349-1356
    DOI: 10.5367/te.2013.0339
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.5367/te.2013.0339
    Download Restriction: no

    File URL: https://libkey.io/10.5367/te.2013.0339?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. Kulendran, N. & King, Maxwell L., 1997. "Forecasting international quarterly tourist flows using error-correction and time-series models," International Journal of Forecasting, Elsevier, vol. 13(3), pages 319-327, September.
    2. 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.
    3. Song, Haiyan & Li, Gang & Witt, Stephen F. & Athanasopoulos, George, 2011. "Forecasting tourist arrivals using time-varying parameter structural time series models," International Journal of Forecasting, Elsevier, vol. 27(3), pages 855-869.
    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. Giulia Contu & Sara Pau, 2022. "The impact of TV series on tourism performance: the case of Game of Thrones," Empirical Economics, Springer, vol. 63(6), pages 3313-3341, December.
    2. Sara Nunes & Samiha Chemli & Alejandro del Moral Agúndez & Kang Jin Seo & Julia Fragoso da Fonseca, 2022. "Descriptive Analysis of the Recent Advances of Film-Induced Tourism: Identification of Strengths, Gaps and Opportunities," Academica Turistica - Tourism and Innovation Journal, University of Primorska Press, vol. 15(2), pages 233-247.

    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. 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.
    2. Nicholas Apergis & Andrea Mervar & James E. Payne, 2017. "Forecasting disaggregated tourist arrivals in Croatia," Tourism Economics, , vol. 23(1), pages 78-98, February.
    3. Gang Xie & Xin Li & Yatong Qian & Shouyang Wang, 2021. "Forecasting tourism demand with KPCA-based web search indexes," Tourism Economics, , vol. 27(4), pages 721-743, June.
    4. Nada Kulendran & Sarath Divisekera, 2007. "Measuring the Economic Impact of Australian Tourism Marketing Expenditure," Tourism Economics, , vol. 13(2), pages 261-274, June.
    5. Haodong Sun & Yang Yang & Yanyan Chen & Xiaoming Liu & Jiachen Wang, 2023. "Tourism demand forecasting of multi-attractions with spatiotemporal grid: a convolutional block attention module model," Information Technology & Tourism, Springer, vol. 25(2), pages 205-233, June.
    6. Charles, Jacky S. & Fullerton, Thomas M., Jr., 2012. "An Error Correction Analysis of Visitor Arrivals to the Bahamas," MPRA Paper 43064, University Library of Munich, Germany.
    7. Gunter, Ulrich & Önder, Irem, 2016. "Forecasting city arrivals with Google Analytics," Annals of Tourism Research, Elsevier, vol. 61(C), pages 199-212.
    8. Song, Haiyan & Li, Gang & Witt, Stephen F. & Athanasopoulos, George, 2011. "Forecasting tourist arrivals using time-varying parameter structural time series models," International Journal of Forecasting, Elsevier, vol. 27(3), pages 855-869.
    9. Xie, Gang & Qian, Yatong & Wang, Shouyang, 2020. "A decomposition-ensemble approach for tourism forecasting," Annals of Tourism Research, Elsevier, vol. 81(C).
    10. Elisa Jorge-González & Enrique González-Dávila & Raquel Martín-Rivero & Domingo Lorenzo-Díaz, 2020. "Univariate and multivariate forecasting of tourism demand using state-space models," Tourism Economics, , vol. 26(4), pages 598-621, June.
    11. Athanasopoulos, George & Hyndman, Rob J. & Song, Haiyan & Wu, Doris C., 2011. "The tourism forecasting competition," International Journal of Forecasting, Elsevier, vol. 27(3), pages 822-844.
    12. 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.
    13. António Rua & Carlos Melo Gouveia & Nuno Lourenço, 2020. "Forecasting tourism with targeted predictors in a data-rich environment," Working Papers w202005, Banco de Portugal, Economics and Research Department.
    14. 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.
    15. Jiao, Xiaoying & Li, Gang & Chen, Jason Li, 2020. "Forecasting international tourism demand: a local spatiotemporal model," Annals of Tourism Research, Elsevier, vol. 83(C).
    16. Han Liu & Ying Liu & Yonglian Wang & Changchun Pan, 2019. "Hot topics and emerging trends in tourism forecasting research: A scientometric review," Tourism Economics, , vol. 25(3), pages 448-468, May.
    17. Apergis Nicholas, 2021. "Forecasting US overseas travelling with univariate and multivariate models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(6), pages 963-976, September.
    18. Lourenço, Nuno & Gouveia, Carlos Melo & Rua, António, 2021. "Forecasting tourism with targeted predictors in a data-rich environment," Economic Modelling, Elsevier, vol. 96(C), pages 445-454.
    19. Haiyan Song & Egon Smeral & Gang Li & Jason L. Chen, 2008. "Tourism Forecasting: Accuracy of Alternative Econometric Models Revisited," WIFO Working Papers 326, WIFO.
    20. Chuan Zhang & Ao‐Yun Hu & Yu‐Xin Tian, 2023. "Daily tourism forecasting through a novel method based on principal component analysis, grey wolf optimizer, and extreme learning machine," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(8), pages 2121-2138, December.

    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:20:y:2014:i:6:p:1349-1356. 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.