The impact of COVID-19 on tourism sector in India

References

• Ahmed, Z. U., & Krohn, F. B. (1992). Marketing India as a tourist destination in North America—challenges and opportunities. International Journal of Hospitality Management, 11(2), 89–98. https://doi.org/10.1016/0278-4319(92)90003-E
   Google Scholar
• Athanasopoulos, G., & Hyndman, R. J. (2008). Modelling and forecasting Australian domestic tourism. Tourism Management, 29(1), 19–31. https://doi.org/10.1016/j.tourman.2007.04.009
   Web of Science ®Google Scholar
• Bloom, J. Z. (2002). The sequencing of neural networks for segmenting the market of a tourist destination. Tourism, 50(4), 325–338.
   Google Scholar
• Bloom, J. Z. (2004). Tourist market segmentation with linear and non-linear techniques. Tourism Management, 25(6), 723–733. https://doi.org/10.1016/j.tourman.2003.07.004
   Web of Science ®Google Scholar
• Bloom, J. Z. (2005). Market segmentation: A neural network application. Annals of Tourism Research, 32(1), 93–111. https://doi.org/10.1016/j.annals.2004.05.001
   Web of Science ®Google Scholar
• Burger, C. J. S. C., Dohnal, M., Kathrada, M., & Law, R. (2001). A practitioners guide to time-series methods for tourism demand forecasting—a case study of Durban, South Africa. Tourism Management, 22(4), 403–409. https://doi.org/10.1016/S0261-5177(00)00068-6
   Web of Science ®Google Scholar
• Chang, Y. W., & Liao, M. Y. (2010). A seasonal ARIMA model of tourism forecasting: The case of Taiwan. Asia Pacific Journal of Tourism Research, 15(2), 215–221. https://doi.org/10.1080/10941661003630001
   Web of Science ®Google Scholar
• Chen, C. F., Lai, M. C., & Yeh, C. C. (2012). Forecasting tourism demand based on empirical mode decomposition and neural network. Knowledge-Based Systems, 26, 281–287. https://doi.org/10.1016/j.knosys.2011.09.002
   Web of Science ®Google Scholar
• Cho, V. (2001). Tourism forecasting and its relationship with leading economic indicators. Journal of Hospitality & Tourism Research, 25(4), 399–420. https://doi.org/10.1177/109634800102500404
   Google Scholar
• Cho, V. (2003). A comparison of three different approaches to tourist arrival forecasting. Tourism Management, 24(3), 323–330. https://doi.org/10.1016/S0261-5177(02)00068-7
   Web of Science ®Google Scholar
• Claveria, O., & Torra, S. (2014). Forecasting tourism demand to Catalonia: Neural networks vs. Time series models. Economic Modelling, 36, 220–228. https://doi.org/10.1016/j.econmod.2013.09.024
   Web of Science ®Google Scholar
• Cohen, E. (2012). Globalization, global crises and tourism. Tourism Recreation Research, 37(2), 103–111. https://doi.org/10.1080/02508281.2012.11081695
   Google Scholar
• Economicoutlook. (2020). https://economicoutlook.cmie.com/.
   Google Scholar
• FICCI. (June 2020). Travel and Tourism - Survive, revive and thrive in times of COVID-19.
   Google Scholar
• Goh, C., & Law, R. (2002). Modeling and forecasting tourism demand for arrivals with stochastic non-stationary seasonality and intervention. Tourism Management, 23(5), 499–510. https://doi.org/10.1016/S0261-5177(02)00009-2
   Web of Science ®Google Scholar
• Gössling, S., Scott, D., & Hall, C. M. (2020). Pandemics, tourism and global change: A rapid assessment of COVID-19. Journal of Sustainable Tourism, 29(1), 1–20. https://doi.org/10.1080/09669582.2020.1758708
   Web of Science ®Google Scholar
• Gretzel, U., Fuchs, M., Baggio, R., Hoepken, W., Law, R., Neidhardt, J., Pesonen, J., Zanker, M., Xiang, Z., & Xiang, Z. (2020). E-Tourism beyond COVID-19: A call for transformative research. Information Technology & Tourism, 22(2), 187–203. https://doi.org/10.1007/s40558-020-00181-3
   Web of Science ®Google Scholar
• Hadavandi, E., Ghanbari, A., Shahanaghi, K., & Abbasian-Naghneh, S. (2011). Tourist arrival forecasting by evolutionary fuzzy systems. Tourism Management, 32(5), 1196–1203. https://doi.org/10.1016/j.tourman.2010.09.015
   Web of Science ®Google Scholar
• Höpken, W., Eberle, T., Fuchs, M., & Lexhagen, M. (2020). Improving tourist arrival prediction: A Big data and artificial neural network approach. Journal of Travel Research, https://doi.org/10.1177/0047287520921244
   Web of Science ®Google Scholar
• Kulendran, N., & Witt, S. F. (2003). Forecasting the demand for international business tourism. Journal of Travel Research, 41(3), 265–271. https://doi.org/10.1177/0047287502239034
   Google Scholar
• Kulendran, N., & Wong, K. K. (2005). Modeling seasonality in tourism forecasting. Journal of Travel Research, 44(2), 163–170. https://doi.org/10.1177/0047287505276605
   Google Scholar
• Kumar, S. (2011). Neural networks a classroom approach. Tata McGraw-Hill Education Private Limited.
   Google Scholar
• Law, R. (2000). Backpropagation learning in improving the accuracy of neural network-based tourism demand forecasting. Tourism Management, 21(4), 331–340. https://doi.org/10.1016/S0261-5177(99)00067-9
   Web of Science ®Google Scholar
• Law, R., Li, G., Fong, D. K. C., & Han, X. (2019). Tourism demand forecasting: A deep learning approach. Annals of Tourism Research, 75, 410–423. https://doi.org/10.1016/j.annals.2019.01.014
   Web of Science ®Google Scholar
• Li, G., & Jiao, X. (2020). Tourism forecasting research: A perspective article. Tourism Review, 75(1), 263–266. https://doi.org/10.1108/TR-09-2019-0382
   Web of Science ®Google Scholar
• Liu, H., Liu, Y., Wang, Y., & Pan, C. (2019). Hot topics and emerging trends in tourism forecasting research: A scientometric review. Tourism Economics, 25(3), 448–468. https://doi.org/10.1177/1354816618810564
   Web of Science ®Google Scholar
• Longforecast. (2020). https://longforecast.com/usd-to-inr-forecast-2017-2018-2019-2020-2021-indian-rupee.
   Google Scholar
• Nicolaides, C., Avraam, D., Cueto-Felgueroso, L., González, M. C., & Juanes, R. (2020). Hand-hygiene mitigation strategies against global disease spreading through the air transportation network. Risk Analysis, 40(4), 723–740. https://doi.org/10.1111/risa.13438
   PubMed Web of Science ®Google Scholar
• Palmer, A., Montano, J. J., & Sesé, A. (2006). Designing an artificial neural network for forecasting tourism time series. Tourism Management, 27(5), 781–790. https://doi.org/10.1016/j.tourman.2005.05.006
   Web of Science ®Google Scholar
• Polyzos, S., Samitas, A., & Spyridou, A. E. (2020). Tourism demand and the COVID-19 pandemic: An LSTM approach. Tourism Recreation Research, 1–13. https://doi.org/10.1080/02508281.2020.1777053
   Web of Science ®Google Scholar
• Scroll. (2020). India’s Covid-19 lockdown may cause 38 million job losses in the travel and tourism industry, https://scroll.in/article/959045/indias-covid-19-lockdown-may-cause-38-million-job-losses-in-the-travel-and-tourism-industry.
   Google Scholar
• Shahrabi, J., Hadavandi, E., & Asadi, S. (2013). Developing a hybrid intelligent model for forecasting problems: Case study of tourism demand time series. Knowledge-Based Systems, 43, 112–122. https://doi.org/10.1016/j.knosys.2013.01.014
   Web of Science ®Google Scholar
• Sigala, M. (2020). Tourism and COVID-19: Impacts and implications for advancing and resetting industry and research. Journal of Business Research, 117, 312–321. https://doi.org/10.1016/j.jbusres.2020.06.015
   PubMed Web of Science ®Google Scholar
• Silva, E. S., Hassani, H., Heravi, S., & Huang, X. (2019). Forecasting tourism demand with denoised neural networks. Annals of Tourism Research, 74, 134–154. https://doi.org/10.1016/j.annals.2018.11.006
   Web of Science ®Google Scholar
• Sirakaya, E., Delen, D., & Choi, H. S. (2005). Forecasting gaming referenda. Annals of Tourism Research, 32(1), 127–149. https://doi.org/10.1016/j.annals.2004.05.002
   Web of Science ®Google Scholar
• Song, H., Qiu, R. T., & Park, J. (2019). A review of research on tourism demand forecasting: Launching the Annals of tourism research Curated Collection on tourism demand forecasting. Annals of Tourism Research, 75, 338–362. https://doi.org/10.1016/j.annals.2018.12.001
   Web of Science ®Google Scholar
• Song, H., & Witt, S. F. (2003). Tourism forecasting: The general-to-specific approach. Journal of Travel Research, 42(1), 65–74. https://doi.org/10.1177/0047287503253939
   Google Scholar
• Song, H., Witt, S. F., & Jensen, T. C. (2003). Tourism forecasting: Accuracy of alternative econometric models. International Journal of Forecasting, 19(1), 123–141. https://doi.org/10.1016/S0169-2070(01)00134-0
   Web of Science ®Google Scholar
• Statista. (2020). https://www.statista.com/statistics/1104835/coronavirus-travel-tourism-employment-loss/.
   Google Scholar
• TAN. (2020). Foreign tourist arrivals to India tumble over 66% in March owing to coronavirus pandemic, https://travelandynews.com/foreign-tourist-arrivals-to-india-tumble-over-66-in-march-owing-to-coronavirus-pandemic/.
   Google Scholar
• Tsaur, R. C., & Kuo, T. C. (2011). The adaptive fuzzy time series model with an application to Taiwan’s tourism demand. Expert Systems with Applications, 38(8), 9164–9171. https://doi.org/10.1016/j.eswa.2011.01.059
   Web of Science ®Google Scholar
• Tsionas, M. G. (2020). COVID-19 and gradual adjustment in the tourism, hospitality, and related industries. Tourism Economics, 1–5. https://doi.org/10.1177/1354816620933039
   Web of Science ®Google Scholar
• Turner, L. W., & Witt, S. F. (2001). Forecasting tourism using univariate and multivariate structural time series models. Tourism Economics, 7(2), 135–147. https://doi.org/10.5367/000000001101297775
   Google Scholar
• UNWTO. (2020). UNWTO World Tourism Barometer (Vol. 18, Issue 2, May 2020). Madrid, Spain: UNWTO.
   Google Scholar
• Uysal, M., & El Roubi, M. S. (1999). Artificial neural networks versus multiple regression in tourism demand analysis. Journal of Travel Research, 38(2), 111–118. https://doi.org/10.1177/004728759903800203
   Google Scholar
• Wen, J., Wang, W., Kozak, M., Liu, X., & Hou, H. (2020). Many brains are better than one: The importance of interdisciplinary studies on COVID-19 in and beyond tourism. Tourism Recreation Research, 1–4. https://doi.org/10.1080/02508281.2020.1761120
   Web of Science ®Google Scholar
• WHO. (2020). Coronavirus disease (COVID-19) pandemic. https://www.who.int/emergencies/diseases/novel-coronavirus-2019
   Google Scholar
• Witt, S. F., & Martin, C. A. (1987). Econometric models for forecasting international tourism demand. Journal of Travel Research, 25(3), 23–30. https://doi.org/10.1177/004728758702500306
   Google Scholar
• Witt, S. F., Song, H., & Wanhill, S. (2004). Forecasting tourism-generated employment: The case of Denmark. Tourism Economics, 10(2), 167–176. https://doi.org/10.5367/000000004323142407
   Google Scholar
• Wong, K. K., Song, H., & Chon, K. S. (2006). Bayesian models for tourism demand forecasting. Tourism Management, 27(5), 773–780. https://doi.org/10.1016/j.tourman.2005.05.017
   Web of Science ®Google Scholar
• Wong, K. K., Song, H., Witt, S. F., & Wu, D. C. (2007). Tourism forecasting: To combine or not to combine? Tourism Management, 28(4), 1068–1078. https://doi.org/10.1016/j.tourman.2006.08.003
   Web of Science ®Google Scholar
• The World Economic Forum. (2020). https://www.weforum.org/agenda/2020/06/4-charts-airline-crisis-covid-way-ahead/.
   Google Scholar
• WTTC. (2018). Travel & tourism economic impact 2018 world. World Travel and Tourism Council.
   Google Scholar
• WTTC. (2020). Latest research from WTTC shows a 50% increase in jobs at risk in Travel &Tourism. https://www.wttc.org/about/media-centre/press-releases/press releases/ 2020/ latest research- from-wttc-shows-an-increase-in-jobs-at-risk-in-travel-and-tourism.
   Google Scholar
• Yang, X., Pan, B., Evans, J. A., & Lv, B. (2015). Forecasting Chinese tourist volume with search engine data. Tourism Management, 46, 386–397. https://doi.org/10.1016/j.tourman.2014.07.019
   Web of Science ®Google Scholar
• Yao, Y., & Cao, Y. (2020). A Neural network enhanced hidden Markov model for tourism demand forecasting. Applied Soft Computing, 94, 106465. https://doi.org/10.1016/j.asoc.2020.106465.
   Google Scholar
• Yeh, S. S. (2020). Tourism recovery strategy against COVID-19 pandemic. Tourism Recreation Research, 1–7. https://doi.org/10.1080/02508281.2020.1805933
   Web of Science ®Google Scholar
• Ying, T., Wang, K., Liu, X., Wen, J., & Goh, E. (2020). Rethinking game consumption in tourism: A case of the 2019 novel coronavirus pneumonia outbreak in China. Tourism Recreation Research, 1–6. https://doi.org/10.1080/02508281.2020.1743048
   Web of Science ®Google Scholar