Tourist behavior patterns and disparities perpetuated in the face of COVID-19 : a data-driven Chinese narrative (2018-2024)

Abstract

The effect of COVID-19 on the global tourism industry triggered much angst and concern over future uncertainties. While extant research has begun to probe these impacts, many studies rely on small-scale surveys or interviews and adopt a snapshot lens of tourist behavior. Consequently, evolving behavioral patterns and disparities remain underexamined, and the socioeconomic underpinnings that lead to these behavioral disparities are also frequently overlooked. This study addresses these gaps by leveraging over 16 million user-generated travel records from the Chinese platform Dianping.com, spanning the period from 2018 to 2024, to investigate the dynamic landscape of Chinese tourist behavior and disparities before, amid, and after the pandemic. The study quantifies behavioral disparities using the Gini coefficient, recognizing that while statistical dispersion reflects disparity, such uneven distributions of tourist behavior often stem from deeper socioeconomic and structural inequalities. The study has three primary objectives: (1) to unpack pre-COVID-19 tourist behavior patterns and disparities among diverse socioeconomic groups; (2) to model the structural and contextual factors contributing to these pre-existing inequalities; and (3) to analyze the evolution of tourist behavior patterns and disparities across distinct pandemic phases. To contextualize these phases, the research incorporates 0.4 million online search indices from Baidu and Google to define four pandemic-related stages: pre-pandemic, high-impact, low-impact, and post-pandemic, for each city's specific situation. Subsequently, the study defines and examines seven dimensions of tourist behavior, including travel frequency, distance, timing, destination choices, attraction categories, satisfaction, and access to high-quality sites. Travelers are stratified by gender and city-level disposable income to enable a nuanced understanding of disparities and their trajectories during the pandemic. The research employs data mining techniques, descriptive statistical analysis, comparative analysis, and spatial econometric modeling across its analytical phases. Key findings reveal complex impacts on both behavior patterns and disparities: (1) Pre-pandemic patterns and disparities: women exhibited more active behavior across multiple dimensions, challenging traditional gender stereotypes in tourism. Disparities were most prominent in behaviors demanding higher monetary or temporal commitments. Men and tourists from higher-income cities exhibited greater disparities, suggesting structural constraints beyond simply resource availability influenced unevenness. (2) Determinants of disparities: Behavior disparities were shaped by distinct factors, and the same factor could sometimes exert divergent or even opposite effects. Overall, economically prosperous cities with higher GDP, abundant catering and hotel facilities, and diverse attractions generally mitigate behavior disparities. Additionally, actors such as public transport availability, expressway density, and elderly population ratios are also correlated with specific forms of behavior disparities. (3) Pandemic-induced shifts: the pandemic's impact was heterogeneous and non-uniform, with negative shifts dominant but positive changes in travel frequency and satisfaction. Female travelers and residents of high-income cities experienced more substantial disruptions and slower recovery. Disparities peaked during the high-impact phase (e.g., travel time flexibility), yet some dimensions (e.g., travel frequency) equalized, highlighting a complex crisis-disparity dynamic. The study concludes by discussing the underlying causes behind these findings and presents policy and practice implications across five areas. This analysis offers a comprehensive understanding of how the characteristics of Chinese tourist behavior, disparities, potential inequalities, and their evolvement in response to major global disruptions.