Analyzing the Asymmetric Effects of COVID-19 on Hotel Selection Attributes and Customer Satisfaction Through AIPA

Abstract

The COVID-19 pandemic reshaped travel patterns and customer expectations, generating profound challenges for the hotel industry. This study analyzes 50,000 TripAdvisor reviews of New York hotels to examine how customer satisfaction with hotel selection attributes shifted before and during the pandemic. BERTopic was applied to extract eight key attributes, while VADER, PRCA, and Asymmetric Impact–Performance Analysis (AIPA) were used to capture asymmetric effects and prioritize improvements. Comparative analyses by hotel classification, travel type, and customer residence reveal significant shifts in food and beverage, location, and staff, particularly among lower-tier hotels, business travelers, and international guests. The novelty of this study lies in integrating BERTopic and AIPA to overcome survey-based limitations and provide a robust, data-driven view of COVID-19’s impact on hotel satisfaction. Theoretically, it advances asymmetric satisfaction research by linking text-derived attributes with AIPA. Practically, it offers actionable guidance for hotel managers to strengthen hygiene, expand contactless services, and reallocate resources effectively in preparation for future crises. In addition, this study contributes to sustainability by showing how data-driven analysis can enhance service resilience and support the long-term socio-economic viability of the hotel industry under global crises.