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Tourism Forecasting of “Unpredictable” Future Shocks: A Literature Review by the PRISMA Model

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  • Sergej Gricar

    (Faculty of Business and Management Sciences, University of Novo Mesto, Na Loko 2, 8000 Novo Mesto, Slovenia)

Abstract

This study delves into the intricate process of predicting tourism demand, explicitly focusing on econometric and quantitative time series analysis. A meticulous review of the existing literature is carried out to comprehensively understand the various methods for forecasting “unpredictable” shocks of tourism demand on an ex-ante basis. The PRISMA method has been implemented. Drawing on scholarly research, this study pinpoints the critical challenges in accurately predicting tourism demand, making it a valuable resource for tourism professionals and researchers seeking to stay on top of the latest forecasting techniques. Moreover, the study includes an overview of published manuscripts from the current decade, with mixed results from the 32 manuscripts reviewed. The study concludes that virtual tourism, augmented reality, virtual reality, big data, and artificial intelligence all have the potential to enhance demand forecasting in time series econometrics.

Suggested Citation

  • Sergej Gricar, 2023. "Tourism Forecasting of “Unpredictable” Future Shocks: A Literature Review by the PRISMA Model," JRFM, MDPI, vol. 16(12), pages 1-13, November.
    • Handle: RePEc:gam:jjrfmx:v:16:y:2023:i:12:p:493-:d:1284903
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    References listed on IDEAS

    as
    1. Sergej Gričar & Violeta Šugar & Štefan Bojnec, 2021. "The missing link between wages and labour productivity in tourism: evidence from Croatia and Slovenia," Economic Research-Ekonomska Istraživanja, Taylor & Francis Journals, vol. 34(1), pages 732-753, January.
    2. Muhammad Umar & Yan Xu & Sultan Sikandar Mirza, 2021. "The impact of Covid-19 on Gig economy," Economic Research-Ekonomska Istraživanja, Taylor & Francis Journals, vol. 34(1), pages 2284-2296, January.
    3. Sun, Ya-Yen & Gossling, Stefan & Zhou, Wanru, 2022. "Does tourism increase or decrease carbon emissions? A systematic review," Annals of Tourism Research, Elsevier, vol. 97(C).
    4. Sergej Gricar & Stefan Bojnec & Tea Baldigara, 2022. "Insight into Predicted Shocks in Tourism: Review of an Ex-Ante Forecasting," JRFM, MDPI, vol. 15(10), pages 1-17, September.
    5. Jaume Rosselló Nadal & María Santana Gallego, 2022. "Gravity models for tourism demand modeling: Empirical review and outlook," Journal of Economic Surveys, Wiley Blackwell, vol. 36(5), pages 1358-1409, December.
    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. Miraj Ahmed Bhuiyan & Tiziana Crovella & Annarita Paiano & Helena Alves, 2021. "A Review of Research on Tourism Industry, Economic Crisis and Mitigation Process of the Loss: Analysis on Pre, During and Post Pandemic Situation," Sustainability, MDPI, vol. 13(18), pages 1-27, September.
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