Forecasting tourism recovery amid COVID-19
The profound impact of the coronavirus disease 2019 (COVID-19) pandemic on global tourism activity has rendered forecasts of tourism demand obsolete. Accordingly, scholars have begun to seek the best methods to predict the recovery of tourism from the devastating effects of COVID-19. In this study,...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier Ltd
2021
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| Online Access: | https://eprints.nottingham.ac.uk/64690/ |
| _version_ | 1848800154929332224 |
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| author | Zhang, Hanyuan Song, Haiyan Wen, Long Liu, Chang |
| author_facet | Zhang, Hanyuan Song, Haiyan Wen, Long Liu, Chang |
| author_sort | Zhang, Hanyuan |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | The profound impact of the coronavirus disease 2019 (COVID-19) pandemic on global tourism activity has rendered forecasts of tourism demand obsolete. Accordingly, scholars have begun to seek the best methods to predict the recovery of tourism from the devastating effects of COVID-19. In this study, econometric and judgmental methods were combined to forecast the possible paths to tourism recovery in Hong Kong. The autoregressive distributed lag-error correction model was used to generate baseline forecasts, and Delphi adjustments based on different recovery scenarios were performed to reflect different levels of severity in terms of the pandemic’s influence. These forecasts were also used to evaluate the economic effects of the COVID-19 pandemic on the tourism industry in Hong Kong. |
| first_indexed | 2025-11-14T20:47:03Z |
| format | Article |
| id | nottingham-64690 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T20:47:03Z |
| publishDate | 2021 |
| publisher | Elsevier Ltd |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-646902021-03-10T06:16:40Z https://eprints.nottingham.ac.uk/64690/ Forecasting tourism recovery amid COVID-19 Zhang, Hanyuan Song, Haiyan Wen, Long Liu, Chang The profound impact of the coronavirus disease 2019 (COVID-19) pandemic on global tourism activity has rendered forecasts of tourism demand obsolete. Accordingly, scholars have begun to seek the best methods to predict the recovery of tourism from the devastating effects of COVID-19. In this study, econometric and judgmental methods were combined to forecast the possible paths to tourism recovery in Hong Kong. The autoregressive distributed lag-error correction model was used to generate baseline forecasts, and Delphi adjustments based on different recovery scenarios were performed to reflect different levels of severity in terms of the pandemic’s influence. These forecasts were also used to evaluate the economic effects of the COVID-19 pandemic on the tourism industry in Hong Kong. Elsevier Ltd 2021-01-16 Article PeerReviewed application/pdf en cc_by https://eprints.nottingham.ac.uk/64690/1/Forecasting%20tourism%20recovery%20amid%20COVID-19.pdf Zhang, Hanyuan, Song, Haiyan, Wen, Long and Liu, Chang (2021) Forecasting tourism recovery amid COVID-19. Annals of Tourism Research, 87 . p. 103149. ISSN 01607383 COVID-19; tourism demand; crisis management; Delphi method; forecasting scenarios http://dx.doi.org/10.1016/j.annals.2021.103149 doi:10.1016/j.annals.2021.103149 doi:10.1016/j.annals.2021.103149 |
| spellingShingle | COVID-19; tourism demand; crisis management; Delphi method; forecasting scenarios Zhang, Hanyuan Song, Haiyan Wen, Long Liu, Chang Forecasting tourism recovery amid COVID-19 |
| title | Forecasting tourism recovery amid COVID-19 |
| title_full | Forecasting tourism recovery amid COVID-19 |
| title_fullStr | Forecasting tourism recovery amid COVID-19 |
| title_full_unstemmed | Forecasting tourism recovery amid COVID-19 |
| title_short | Forecasting tourism recovery amid COVID-19 |
| title_sort | forecasting tourism recovery amid covid-19 |
| topic | COVID-19; tourism demand; crisis management; Delphi method; forecasting scenarios |
| url | https://eprints.nottingham.ac.uk/64690/ https://eprints.nottingham.ac.uk/64690/ https://eprints.nottingham.ac.uk/64690/ |