Abstract
We used a lodging platform’s big data to investigate the factors that were associated with consumers’ reviews during the pandemic. A univariate analysis and fixed effects model analysis were conducted on an unbalanced panel dataset of 84,915 listings in Los Angeles and 16,143 listings in San Francisco over 21 months. Results showed that review ratings were higher for more COVID-19 cases, super host, and instantly bookable; however, negative associations were found with price, number of reviews, and host listing numbers. The profound implications brought by COVID-19 required online booking platforms to seek ways to improve customer satisfaction to survive. The key was to understand the new needs and requirements for travelers during COVID-19.
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