IDEAS home Printed from https://ideas.repec.org/a/rmk/rmkbae/v12y2025i2p135-147.html
   My bibliography  Save this article

Tourist Sites and Visitor Numbers in Taiwan: An Online Buzz Analysis

Author

Listed:
  • Chien-Jung Ting
  • Yi-Long Hsiao
  • Jia-Jhen Shen

Abstract

This study highlights the influence of online buzz by examining the effect of tourism and recreational site-related keywords on inbound tourism to Taiwan. Using large-scale, real-time data from Google Trends, we assess how search interest in key attractions predicts tourist arrivals. Principal Component Analysis (PCA) is applied to construct a composite index of search activity, allowing us to explore its relationship with inbound tourist numbers. The results indicate that six keywords - Longshan Temple, Jiufen, Miramar Entertainment Park, Fushoushan Farm, Yangmingshan National Park, and Raohe Street Night Market - are significantly associated with tourist arrivals. The growing use of social media and real-time information platforms has enhanced tourists’ awareness and interest in Taiwanese attractions, supporting the steady growth of inbound tourism. Government initiatives promoting smart tourism through digital technology and big data further amplify the visibility of Taiwan’s tourism resources and contribute to tourism development. The study’s principal contribution lies in leveraging real-time data; by providing richer and timelier information than conventional official statistics, these data yield more precise and statistically significant results. Moreover, because few studies use real-time data to examine the direct effect of online buzz on inbound tourist arrivals to Taiwan, this paper fills that gap.

Suggested Citation

  • Chien-Jung Ting & Yi-Long Hsiao & Jia-Jhen Shen, 2025. "Tourist Sites and Visitor Numbers in Taiwan: An Online Buzz Analysis," Bulletin of Applied Economics, Risk Market Journals, vol. 12(2), pages 135-147.
    • Handle: RePEc:rmk:rmkbae:v:12:y:2025:i:2:p:135-147
    as

    Download full text from publisher

    File URL: https://www.riskmarket.co.uk/bae/journals-articles/issues/tourist-sites-and-visitor-numbers-in-taiwan-an-online-buzz-analysis/?download=attachment.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. D'Amuri, Francesco & Marcucci, Juri, 2009. "‘Google it!’ Forecasting the US unemployment rate with a Google job search index," ISER Working Paper Series 2009-32, Institute for Social and Economic Research.
    2. Nikolaos Askitas & Klaus F. Zimmermann, 2009. "Google Econometrics and Unemployment Forecasting," Applied Economics Quarterly (formerly: Konjunkturpolitik), Duncker & Humblot, Berlin, vol. 55(2), pages 107-120.
    3. Konstantin Kholodilin & Maximilian Podstawski & Boriss Siliverstovs, 2010. "Do Google Searches Help in Nowcasting Private Consumption?," KOF Working papers 10-256, KOF Swiss Economic Institute, ETH Zurich.
    4. Takeda, Fumiko & Wakao, Takumi, 2014. "Google search intensity and its relationship with returns and trading volume of Japanese stocks," Pacific-Basin Finance Journal, Elsevier, vol. 27(C), pages 1-18.
    5. Simeon Vosen & Torsten Schmidt, 2012. "A monthly consumption indicator for Germany based on Internet search query data," Applied Economics Letters, Taylor & Francis Journals, vol. 19(7), pages 683-687, May.
    6. Liebermann, Joëlle, 2012. "Short-term forecasting of quarterly gross domestic product growth," Quarterly Bulletin Articles, Central Bank of Ireland, pages 74-84, February.
    7. repec:diw:diwwpp:dp997 is not listed on IDEAS
    8. Karim Barhoumi & Olivier Darné & Laurent Ferrara, 2010. "Are disaggregate data useful for factor analysis in forecasting French GDP?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 132-144.
    9. 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.
    10. Simeon Vosen & Torsten Schmidt, 2011. "Forecasting private consumption: survey‐based indicators vs. Google trends," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(6), pages 565-578, September.
    11. Jean Boivin & Serena Ng, 2005. "Understanding and Comparing Factor-Based Forecasts," International Journal of Central Banking, International Journal of Central Banking, vol. 1(3), December.
    12. D’Amuri, Francesco & Marcucci, Juri, 2010. "“Google it!” Forecasting the US Unemployment Rate with a Google Job Search index," Global Challenges Papers 60680, Fondazione Eni Enrico Mattei (FEEM).
    Full references (including those not matched with items on IDEAS)

    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. Chien-jung Ting & Yi-Long Hsiao, 2022. "Nowcasting the GDP in Taiwan and the Real-Time Tourism Data," Advances in Management and Applied Economics, SCIENPRESS Ltd, vol. 12(3), pages 1-2.
    2. Chien-jung Ting & Yi-Long Hsiao & Rui-jun Su, 2022. "Application of the Real-Time Tourism Data in Nowcasting the Service Consumption in Taiwan," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 12(4), pages 1-4.
    3. Chien-Jung Ting & Yu-Jung Chen, 2025. "Recreational Activities and Tourism Expenditure in Taiwan: An Online Buzz Perspective," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 15(6), pages 1-1.
    4. Jacques Bughin, 2015. "Google searches and twitter mood: nowcasting telecom sales performance," Netnomics, Springer, vol. 16(1), pages 87-105, August.
    5. Siliverstovs, Boriss & Wochner, Daniel S., 2018. "Google Trends and reality: Do the proportions match?," Journal of Economic Behavior & Organization, Elsevier, vol. 145(C), pages 1-23.
    6. Vicente, María Rosalía & López-Menéndez, Ana J. & Pérez, Rigoberto, 2015. "Forecasting unemployment with internet search data: Does it help to improve predictions when job destruction is skyrocketing?," Technological Forecasting and Social Change, Elsevier, vol. 92(C), pages 132-139.
    7. Chien-Jung Ting & Hsing-Mei Juan, 2025. "Assessing the Association of Popular Attractions with Taiwan’s Inbound Tourist Numbers: The Case of Night-Market Keywords," Advances in Management and Applied Economics, SCIENPRESS Ltd, vol. 15(6), pages 1-4.
    8. Nuno Barreira & Pedro Godinho & Paulo Melo, 2013. "Nowcasting unemployment rate and new car sales in south-western Europe with Google Trends," Netnomics, Springer, vol. 14(3), pages 129-165, November.
    9. Jianchun Fang & Wanshan Wu & Zhou Lu & Eunho Cho, 2019. "Using Baidu Index To Nowcast Mobile Phone Sales In China," The Singapore Economic Review (SER), World Scientific Publishing Co. Pte. Ltd., vol. 64(01), pages 83-96, March.
    10. Gomes, Pedro & Taamouti, Abderrahim, 2016. "In search of the determinants of European asset market comovements," International Review of Economics & Finance, Elsevier, vol. 44(C), pages 103-117.
    11. Anastasiou, Dimitrios & Bragoudakis, Zacharias & Giannoulakis, Stelios, 2021. "Perceived vs actual financial crisis and bank credit standards: Is there any indication of self-fulfilling prophecy?," Research in International Business and Finance, Elsevier, vol. 58(C).
    12. Jichang Dong & Wei Dai & Ying Liu & Lean Yu & Jie Wang, 2019. "Forecasting Chinese Stock Market Prices using Baidu Search Index with a Learning-Based Data Collection Method," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(05), pages 1605-1629, September.
    13. Karaman Örsal, Deniz Dilan, 2021. "Onlinedaten und Konsumentscheidungen: Voraussagen anhand von Daten aus Social Media und Suchmaschinen," Edition HWWI: Chapters, in: Straubhaar, Thomas (ed.), Neuvermessung der Datenökonomie, volume 6, pages 157-172, Hamburg Institute of International Economics (HWWI).
    14. Nikolaos Askitas & Klaus F. Zimmermann, 2015. "The internet as a data source for advancement in social sciences," International Journal of Manpower, Emerald Group Publishing Limited, vol. 36(1), pages 2-12, April.
    15. Götz, Thomas B. & Knetsch, Thomas A., 2019. "Google data in bridge equation models for German GDP," International Journal of Forecasting, Elsevier, vol. 35(1), pages 45-66.
    16. Fondeur, Y. & Karamé, F., 2013. "Can Google data help predict French youth unemployment?," Economic Modelling, Elsevier, vol. 30(C), pages 117-125.
    17. Schmidt, Torsten & Vosen, Simeon, 2012. "Using Internet Data to Account for Special Events in Economic Forecasting," Ruhr Economic Papers 382, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    18. D’Amuri, Francesco & Marcucci, Juri, 2017. "The predictive power of Google searches in forecasting US unemployment," International Journal of Forecasting, Elsevier, vol. 33(4), pages 801-816.
    19. David Iselin & Boriss Siliverstovs, 2013. "Mit Zeitungen Konjunkturprognosen erstellen: Eine Vergleichsstudie für die Schweiz und Deutschland," KOF Analysen, KOF Swiss Economic Institute, ETH Zurich, vol. 7(3), pages 104-117, September.
    20. Ramya Rajajagadeesan Aroul & Sanjiv Sabherwal & Sergiy Saydometov, 2022. "FEAR Index, city characteristics, and housing returns," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 50(1), pages 173-205, March.

    More about this item

    Keywords

    ;
    ;
    ;

    JEL classification:

    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General
    • O11 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Macroeconomic Analyses of Economic Development
    • R11 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes

    Statistics

    Access and download statistics

    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:rmk:rmkbae:v:12:y:2025:i:2:p:135-147. 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: Eleftherios Spyromitros-Xioufis (email available below). General contact details of provider: http://www.riskmarket.co.uk/ .

    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.