Conceptualizing Post-COVID-19 Malaysia’s Tourism Recovery: An Auto-Regressive Neural Network Analysis

Anantha Raj A. Arokiasamy, Philip Michael Ross Smith, Thanapat Kijbumrung


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

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COVID-19 Pandemic; Tourism; Hospitality; Non-linearity; Auto-Regressive Neural Network; Unit Root.


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DOI: 10.28991/esj-2021-SPER-10


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