Search query and tourism forecasting during the pandemic: When and where can digital footprints be helpful as predictors?

Yang, Yang; Fan, Yawen; Jiang, Lan; Liu, Xiaohui

doi:10.1016/j.annals.2022.103365

Cited by 47 publications

(21 citation statements)

References 75 publications

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“… Yang et al (2022) find insufficient evidence regarding the usefulness of Google Tends data in their forecasts of daily tourism demand across 74 countries in 2020 and therefore highlight the only relative effectiveness of such data in tourism forecasting. Another study employs a learning machine approach to forecast the pandemic effect on Chinese arrivals to the United States and Australia.…”

Section: Literature Reviewmentioning

confidence: 96%

Forecasting Tourism in the EU after the COVID-19 Crisis

Gunter

Smeral

Zekan

2022

Journal of Hospitality & Tourism Research

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The COVID-19 pandemic has restricted both business and social life over the last two years. Stop-and-go policies enacted as containment measures have further impacted the global economy, and tourism in particular. Tourism demand shows only weak signs of a sustainable recovery. The medium-term outlook remains highly uncertain, and yet few studies have addressed the development of the tourism and leisure industries in the years ahead. In this context, we forecast demand in selected EU countries in terms of total expenditure on outbound travel (tourism imports) using a panel pooled Fully Modified Ordinary Least Squares (FMOLS) approach. Baseline and downside scenarios are elaborated to project demand for foreign travel until 2025.

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Section: Literature Reviewmentioning

confidence: 96%

Forecasting Tourism in the EU after the COVID-19 Crisis

Gunter

Smeral

Zekan

2022

Journal of Hospitality & Tourism Research

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“…During this highly uncertain time, tourism forecasting has come to play a more important role than ever in informing decisions among government personnel, industry professionals, and other tourism stakeholders (Yang et al, 2022). First, forecasting allows for tourism recovery predictions and can provide valuable insight for tourism policy design and implementation.…”

Section: Conclusion and Suggestions For Future Researchmentioning

confidence: 99%

“…On the other hand, because of the war, Ukrainians cannot travel either. In addition to industry forecasting reports, tourism researchers have striven to develop and evaluate multiple forecasting methods to predict tourism demand at different levels (Kourentzes et al, 2021;Liu et al, 2021;Qiu et al, 2021;Yang et al, 2022). Various forecasting methods have since been leveraged and compared in terms of forecasting accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…Various forecasting methods have since been leveraged and compared in terms of forecasting accuracy. Examples include time series models, artificial neural network (ANN) models, stacking models, forecasting combinations, and the scenario-based mixed judgmental forecasting method (Yang et al, 2022). The purpose of such studies is not only to help to speed up the recovery of the tourism industry from short-term shocks that were induced by the pandemic and economic crisis, but also to accelerate the recovery of the national economy (Haryanto, 2020).…”

Section: Introductionmentioning

confidence: 99%

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Recovery of Slovenian Tourism After Covid-19 and Ukraine Crisis

Ljubotina

Raspor

2022

Economics

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The profound impact of the COVID-19 pandemic and the Ukraine crisis 2022 on tourism has rendered forecasts of tourism demand obsolete. Accordingly, scholars have begun to look for the best methods to predict the recovery of tourism. In this study, econometric and judgmental methods were combined in order to to forecast the possible ways to tourism recovery in Slovenia. The goals of this paper are to show and assess the current offerings and potential for the development of tourism in Slovenia, and to point out the advantages and challenges in the development of tourism in this type of destination. Statistical methods were used for benchmark analysis, while Box and Jenkins approach and ARIMA modeling were used for forecasting Slovenian tourism until 2030. The datasets are analyzed using Tableau 2022.1. Our findings show that Slovenian tourism will be slower on the uptake and is not likely to fully recover until 2026, due to COVID-19 related uncertainty. A steep jump in daily COVID-19 infections in China and the Ukraine crisis which is affecting political and economic stability in the world is also slowing down the speed of recovery. In 2019, Chinese, Russian and Ukrainian tourists generated 5% of all tourist arrivals and overnight stays. In particular Russian tourists were well-known for their high consumer expenditure and thus desirable to the destination places. To mitigate the drop in tourist arrivals, Slovenia should do two things, namely concentrate on traditional markets, i.e. Austria, Italy, and Germany, which altogether represent 1/3 of all tourist arrivals and overnight stays, and secondly, it should target new markets, bringing new, innovative products, services, and experiences.

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“…Additional socioeconomic factors, such as national and regional gross domestic product (GDP), income levels, population, employment rates, and special events, were used to fine-tune the forecast [ 21 , 25 ]. Some studies have also attempted to predict travel demands with unconventional factors such as search engine queries [ 26 , 27 ] and social media data [ 28 ]. However, the outbreak of the COVID-19 pandemic has substantially increased the uncertainty of air passenger demand, an effect that is difficult to address with the traditional predictors described above.…”

Section: Introductionmentioning

confidence: 99%