IDEAS home Printed from https://ideas.repec.org/a/sae/toueco/v26y2020i7p1129-1151.html
   My bibliography  Save this article

Modelling international monthly tourism demand at the micro destination level with climate indicators and web-traffic data

Author

Listed:
  • Silvia Emili

    (9296University of Bologna, Italy)

  • Paolo Figini

    (9296University of Bologna, Italy; North-West University, South Africa)

  • Andrea Guizzardi

    (9296University of Bologna, Italy)

Abstract

We investigate if and how climate indicators and web-traffic data may improve the estimates of demand functions’ parameters, considering specific origins and destinations. Overall, augmented demand functions show better fit and more reliable price and income elasticities whether the demand is measured with arrivals or with overnights. However, heterogeneity stemming from the main type of tourism (business vs. cultural vs. sea and sun) affects both the web-based and the climate indicators better describing tourists demand as well as their optimal lags. Our findings highlight the utility of such prompt and territorial detailed information for local policymakers, showing, however, how sensitive different demand segments are to policy intervention.

Suggested Citation

  • Silvia Emili & Paolo Figini & Andrea Guizzardi, 2020. "Modelling international monthly tourism demand at the micro destination level with climate indicators and web-traffic data," Tourism Economics, , vol. 26(7), pages 1129-1151, November.
    • Handle: RePEc:sae:toueco:v:26:y:2020:i:7:p:1129-1151
    DOI: 10.1177/1354816619867804
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/1354816619867804
    Download Restriction: no
    File URL: https://libkey.io/10.1177/1354816619867804?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. Cuccia, Tiziana & Rizzo, Ilde, 2011. "Tourism seasonality in cultural destinations: Empirical evidence from Sicily," Tourism Management, Elsevier, vol. 32(3), pages 589-595.
    2. Rainer Andergassen & Guido Candela & Paolo Figini, 2017. "The management of tourism destinations," Tourism Economics, , vol. 23(1), pages 49-65, February.
    3. Martins, Luís Filipe & Gan, Yi & Ferreira-Lopes, Alexandra, 2017. "An empirical analysis of the influence of macroeconomic determinants on World tourism demand," Tourism Management, Elsevier, vol. 61(C), pages 248-260.
    4. Seetaram, Neelu & Forsyth, Peter & Dwyer, Larry, 2016. "Measuring price elasticities of demand for outbound tourism using competitiveness indices," Annals of Tourism Research, Elsevier, vol. 56(C), pages 65-79.
    5. Gunter, Ulrich & Önder, Irem, 2016. "Forecasting city arrivals with Google Analytics," Annals of Tourism Research, Elsevier, vol. 61(C), pages 199-212.
    6. Hyunyoung Choi & Hal Varian, 2012. "Predicting the Present with Google Trends," The Economic Record, The Economic Society of Australia, vol. 88(s1), pages 2-9, June.
    7. John Trevor Coshall, 2005. "A Selection Strategy for Modelling UK Tourism Flows by Air to European Destinations," Tourism Economics, , vol. 11(2), pages 141-158, June.
    8. Haiyan Song & Gang Li & Stephen F. Witt & Baogang Fei, 2010. "Tourism Demand Modelling and Forecasting: How Should Demand Be Measured?," Tourism Economics, , vol. 16(1), pages 63-81, March.
    9. Paul Smith, 2016. "Google's MIDAS Touch: Predicting UK Unemployment with Internet Search Data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 35(3), pages 263-284, April.
    10. Yang, Xin & Pan, Bing & Evans, James A. & Lv, Benfu, 2015. "Forecasting Chinese tourist volume with search engine data," Tourism Management, Elsevier, vol. 46(C), pages 386-397.
    11. Sainaghi, Ruggero & Baggio, Rodolfo, 2017. "Complexity traits and dynamics of tourism destinations," Tourism Management, Elsevier, vol. 63(C), pages 368-382.
    12. Schiff, Aaron & Becken, Susanne, 2011. "Demand elasticity estimates for New Zealand tourism," Tourism Management, Elsevier, vol. 32(3), pages 564-575.
    13. Andergassen, Rainer & Candela, Guido & Figini, Paolo, 2013. "An economic model for tourism destinations: Product sophistication and price coordination," Tourism Management, Elsevier, vol. 37(C), pages 86-98.
    14. 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.
    15. Witt, Stephen F. & Witt, Christine A., 1995. "Forecasting tourism demand: A review of empirical research," International Journal of Forecasting, Elsevier, vol. 11(3), pages 447-475, September.
    16. Peter Fuleky & Carl S. Bonham & Qianxue Zhao, 2013. "Estimating Demand Elasticities in Non-Stationary Panels: The Case of Hawaii Tourism," Working Papers 2013-2R, University of Hawaii Economic Research Organization, University of Hawaii at Manoa, revised Aug 2013.
    17. Li, Gang & Law, Rob & Vu, Huy Quan & Rong, Jia & Zhao, Xinyuan (Roy), 2015. "Identifying emerging hotel preferences using Emerging Pattern Mining technique," Tourism Management, Elsevier, vol. 46(C), pages 311-321.
    18. Bangwayo-Skeete, Prosper F. & Skeete, Ryan W., 2015. "Can Google data improve the forecasting performance of tourist arrivals? Mixed-data sampling approach," Tourism Management, Elsevier, vol. 46(C), pages 454-464.
    19. Dogru, Tarik & Sirakaya-Turk, Ercan & Crouch, Geoffrey I., 2017. "Remodeling international tourism demand: Old theory and new evidence," Tourism Management, Elsevier, vol. 60(C), pages 47-55.
    20. Crouch, Geoffrey I., 1996. "Demand elasticities in international marketing : A meta-analytical application to tourism," Journal of Business Research, Elsevier, vol. 36(2), pages 117-136, June.
    21. Rivera, Roberto, 2016. "A dynamic linear model to forecast hotel registrations in Puerto Rico using Google Trends data," Tourism Management, Elsevier, vol. 57(C), pages 12-20.
    22. Christine Lim, 1997. "An Econometric Classification and Review of International Tourism Demand Models," Tourism Economics, , vol. 3(1), pages 69-81, 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. Silva, Emmanuel Sirimal & Hassani, Hossein, 2022. "‘Modelling’ UK tourism demand using fashion retail sales," Annals of Tourism Research, Elsevier, vol. 95(C).

    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. Law, Rob & Li, Gang & Fong, Davis Ka Chio & Han, Xin, 2019. "Tourism demand forecasting: A deep learning approach," Annals of Tourism Research, Elsevier, vol. 75(C), pages 410-423.
    3. A Fronzetti Colladon & B Guardabascio & R Innarella, 2021. "Using social network and semantic analysis to analyze online travel forums and forecast tourism demand," Papers 2105.07727, arXiv.org.
    4. Guizzardi, Andrea & Pons, Flavio Maria Emanuele & Angelini, Giovanni & Ranieri, Ercolino, 2021. "Big data from dynamic pricing: A smart approach to tourism demand forecasting," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1049-1060.
    5. Ulrich Gunter & Irem Önder & Stefan Gindl, 2019. "Exploring the predictive ability of LIKES of posts on the Facebook pages of four major city DMOs in Austria," Tourism Economics, , vol. 25(3), pages 375-401, May.
    6. 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).
    7. Shaolong Suna & Dan Bi & Ju-e Guo & Shouyang Wang, 2020. "Seasonal and Trend Forecasting of Tourist Arrivals: An Adaptive Multiscale Ensemble Learning Approach," Papers 2002.08021, arXiv.org, revised Mar 2020.
    8. Eden Xiaoying Jiao & Jason Li Chen, 2019. "Tourism forecasting: A review of methodological developments over the last decade," Tourism Economics, , vol. 25(3), pages 469-492, May.
    9. 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.
    10. Liu, Yuan-Yuan & Tseng, Fang-Mei & Tseng, Yi-Heng, 2018. "Big Data analytics for forecasting tourism destination arrivals with the applied Vector Autoregression model," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 123-134.
    11. Long Wen & Chang Liu & Haiyan Song, 2019. "Forecasting tourism demand using search query data: A hybrid modelling approach," Tourism Economics, , vol. 25(3), pages 309-329, May.
    12. Katerina Volchek & Anyu Liu & Haiyan Song & Dimitrios Buhalis, 2019. "Forecasting tourist arrivals at attractions: Search engine empowered methodologies," Tourism Economics, , vol. 25(3), pages 425-447, May.
    13. Jaume Rosselló-Nadal & Jianan HE, 2020. "Tourist arrivals versus tourist expenditures in modelling tourism demand," Tourism Economics, , vol. 26(8), pages 1311-1326, December.
    14. 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.
    15. Li, Hengyun & Hu, Mingming & Li, Gang, 2020. "Forecasting tourism demand with multisource big data," Annals of Tourism Research, Elsevier, vol. 83(C).
    16. Doris Chenguang Wu & Shiteng Zhong & Richard T R Qiu & Ji Wu, 2022. "Are customer reviews just reviews? Hotel forecasting using sentiment analysis," Tourism Economics, , vol. 28(3), pages 795-816, May.
    17. Hulya Bakirtas & Vildan Gulpinar Demirci, 2022. "Can Google Trends data provide information on consumer’s perception regarding hotel brands?," Information Technology & Tourism, Springer, vol. 24(1), pages 57-83, March.
    18. Javier Sebastian, 2016. "Blockchain in financial services: Regulatory landscape and future challenges," Working Papers 16/21, BBVA Bank, Economic Research Department.
    19. Jong, Meng-Chang & Hong, Puah & Arip, Mohammad Affendy, 2020. "Modelling Tourism Demand: An Augmented Gravity Model," Jurnal Ekonomi Malaysia, Faculty of Economics and Business, Universiti Kebangsaan Malaysia, vol. 54(2), pages 105-112.
    20. Bi, Jian-Wu & Liu, Yang & Li, Hui, 2020. "Daily tourism volume forecasting for tourist attractions," Annals of Tourism Research, Elsevier, vol. 83(C).

    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:26:y:2020:i:7:p:1129-1151. 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.