Conceptualizing Post-COVID-19 Malaysia’s Tourism Recovery: An Auto-Regressive Neural Network Analysis
The pandemic caused by the SARS-CoV-2 virus (COVID-19) has significantly affected the tourism industry. Tourist destinations have adopted emergency measures and restrictions that have affected the mobility of individuals around the world. This study aims to analyze the effects of the COVID-19 pandem...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Ital Publication
2021-09-01
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| Series: | Emerging Science Journal |
| Subjects: | |
| Online Access: | https://www.ijournalse.org/index.php/ESJ/article/view/673 |
| _version_ | 1818550893261357056 |
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| author | Anantha Raj A. Arokiasamy Philip Michael Ross Smith Thanapat Kijbumrung |
| author_facet | Anantha Raj A. Arokiasamy Philip Michael Ross Smith Thanapat Kijbumrung |
| author_sort | Anantha Raj A. Arokiasamy |
| collection | DOAJ |
| description | The pandemic caused by the SARS-CoV-2 virus (COVID-19) has significantly affected the tourism industry. Tourist destinations have adopted emergency measures and restrictions that have affected the mobility of individuals around the world. This study aims to analyze the effects of the COVID-19 pandemic on the tourism industry in Malaysia and its overall economic performance. This research used an extensive set of statistical tests, including a newly constructed Auto-Regressive Neural Network-ADF (ARNN-ADF) test, to determine if foreign visitor arrivals from 10 main source markets in Malaysia will revert to normal. Secondary data from various government published sources were used in this conceptual methodology technique for this study. Based on the research results and exploratory research of the literature, we listed in a synthesizing manner several measures to ensure the resilience of the tourism sector during the COVID-19 pandemic period. This research makes a significant contribution to the literature in terms of validating a new framework that emphasizes the effects of tourists that are largely transitory. In conclusion, this conceptual study will further help the authorities to take precautions and the best policy to be implemented in the future.
Doi: 10.28991/esj-2021-SPER-10
Full Text: PDF |
| first_indexed | 2024-12-12T08:52:36Z |
| format | Article |
| id | doaj.art-2f3f59bcca8b4fe6a8e73b813cb35095 |
| institution | Directory Open Access Journal |
| issn | 2610-9182 |
| language | English |
| last_indexed | 2024-12-12T08:52:36Z |
| publishDate | 2021-09-01 |
| publisher | Ital Publication |
| record_format | Article |
| series | Emerging Science Journal |
| spelling | doaj.art-2f3f59bcca8b4fe6a8e73b813cb350952022-12-22T00:30:08ZengItal PublicationEmerging Science Journal2610-91822021-09-015011912910.28991/esj-2021-SPER-10218Conceptualizing Post-COVID-19 Malaysia’s Tourism Recovery: An Auto-Regressive Neural Network AnalysisAnantha Raj A. Arokiasamy0Philip Michael Ross Smith1Thanapat Kijbumrung2School of Business and Management, RMIT International University, Ho Chi Minh City,School of Business and Management, RMIT International University, Ho Chi Minh City,School of Business and Management, RMIT International University, Ho Chi Minh City,The pandemic caused by the SARS-CoV-2 virus (COVID-19) has significantly affected the tourism industry. Tourist destinations have adopted emergency measures and restrictions that have affected the mobility of individuals around the world. This study aims to analyze the effects of the COVID-19 pandemic on the tourism industry in Malaysia and its overall economic performance. This research used an extensive set of statistical tests, including a newly constructed Auto-Regressive Neural Network-ADF (ARNN-ADF) test, to determine if foreign visitor arrivals from 10 main source markets in Malaysia will revert to normal. Secondary data from various government published sources were used in this conceptual methodology technique for this study. Based on the research results and exploratory research of the literature, we listed in a synthesizing manner several measures to ensure the resilience of the tourism sector during the COVID-19 pandemic period. This research makes a significant contribution to the literature in terms of validating a new framework that emphasizes the effects of tourists that are largely transitory. In conclusion, this conceptual study will further help the authorities to take precautions and the best policy to be implemented in the future. Doi: 10.28991/esj-2021-SPER-10 Full Text: PDFhttps://www.ijournalse.org/index.php/ESJ/article/view/673covid-19 pandemictourismhospitalitynon-linearityauto-regressive neural networkunit root. |
| spellingShingle | Anantha Raj A. Arokiasamy Philip Michael Ross Smith Thanapat Kijbumrung Conceptualizing Post-COVID-19 Malaysia’s Tourism Recovery: An Auto-Regressive Neural Network Analysis Emerging Science Journal covid-19 pandemic tourism hospitality non-linearity auto-regressive neural network unit root. |
| title | Conceptualizing Post-COVID-19 Malaysia’s Tourism Recovery: An Auto-Regressive Neural Network Analysis |
| title_full | Conceptualizing Post-COVID-19 Malaysia’s Tourism Recovery: An Auto-Regressive Neural Network Analysis |
| title_fullStr | Conceptualizing Post-COVID-19 Malaysia’s Tourism Recovery: An Auto-Regressive Neural Network Analysis |
| title_full_unstemmed | Conceptualizing Post-COVID-19 Malaysia’s Tourism Recovery: An Auto-Regressive Neural Network Analysis |
| title_short | Conceptualizing Post-COVID-19 Malaysia’s Tourism Recovery: An Auto-Regressive Neural Network Analysis |
| title_sort | conceptualizing post covid 19 malaysia s tourism recovery an auto regressive neural network analysis |
| topic | covid-19 pandemic tourism hospitality non-linearity auto-regressive neural network unit root. |
| url | https://www.ijournalse.org/index.php/ESJ/article/view/673 |
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