Monitoring and forecasting COVID-19 impacts on hotel occupancy rates with daily visitor arrivals and search queries. (Special Issue: COVID-19 and tourism.)
Current Issues in Tourism
; 25(3):490-507, 2022.
Article
in English
| CAB Abstracts | ID: covidwho-1722015
ABSTRACT
The COVID-19 pandemic is greatly affecting the hospitality industry worldwide. Lodging demand is severely reduced as people's fear of coronavirus spreading risk in hotels. This research makes a timely contribution to the hospitality literature by proposing the mixed data sampling models (MIDAS) to monitor and forecast latest hotel occupancy rates with high-frequency big data sources, such as daily visitor arrivals and search query data. Quantitative evidence from Macau from January to July 2020 confirms that MIDAS models can measure the dynamic impacts of the COVID-19 pandemic on hotel occupancy and have a better prediction accuracy and explanation ability than competitive models. Industry practitioners can adopt this big data analytical framework to make daily or monthly updates of lodging demand, conduct scenario analysis, plan and trace the recovery schedule during and post COVID-19 phases. Finally, managerial implications and future work are highlighted.
Tourism and Travel [UU700]; Leisure, Recreation and Tourism Economics [EE119]; hotels; hospitality industry; demand; forecasting; literature; models; monitoring; occupancy rates; prediction; accommodation; Macao; Central Southern China; China; APEC countries; East Asia; Asia; high Human Development Index countries; upper-middle income countries; Macau
Full text:
Available
Collection:
Databases of international organizations
Database:
CAB Abstracts
Type of study:
Experimental Studies
Language:
English
Journal:
Current Issues in Tourism
Year:
2022
Document Type:
Article
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