Abstract
The unpredictable nature of crises poses a significant challenge to the operation of businesses. Hotels often resort to reactive measures, specifically cost reduction strategies, as their primary means of surviving a crisis. Despite the challenges that firms often face in determining which expenses to prioritize to improve their chance of survival, no prior studies have delved into this pressing matter. Thus, this study applied machine learning classification
Savemodels to prove which expense should be targeted for cuts first to increase the odds of surviving a crisis. We proposed that food and beverage salaries were the most important operating expense as a determinant of a good-performing hotel during the COVID-19 crisis, and marketing expenses were the second most important operating expense. We also found that the relative importance of hotel operating expenses had changed before and during COVID-19.
Original language | English |
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Journal | Tourism Economics |
Early online date | Feb 2025 |
DOIs | |
Publication status | Published - Feb 2025 |
Keywords
- cost-reduction strategy
- crisis management
- hotel industry
- machine learning
- random forest classification
ASJC Scopus subject areas
- Geography, Planning and Development
- Tourism, Leisure and Hospitality Management