Tourist Perceptions of Crowding and Satisfaction (2023)

The data for this study were collected in the summer of 2023 in three European capitals: London, Paris, and Rome. The data collection process was based on the Computer-Assisted Personal Interviewing (CAPI) method, which involves computer-assisted direct interviews. Surveys were conducted in key tourist locations in each city to obtain a representative sample of tourists. A total of 1,859 respondents participated in the study, with half being domestic tourists and the other half international tourists. The questionnaire was prepared in English and translated into four other languages. It was then verified by members of the research team and native speakers to ensure maximum clarity and comprehensibility of the statements. The choice of the CAPI technique enabled the collection of high-quality data by increasing the response rate, minimizing errors resulting from misinterpretation of questions, and eliminating missing responses. The collected data covered a wide range of variables, including perception of crowding in tourist spaces, destination attractiveness, travel satisfaction, and tourist loyalty. Additionally, various forms of mobility were considered, such as public transport, active mobility (walking, cycling), and micromobility. The data were subjected to statistical analysis using Partial Least Squares Structural Equation Modeling (PLS-SEM) and multi-group analysis, allowing for the identification of key factors influencing tourists' perceptions in different urban conditions.

本研究的数据于2023年夏季在伦敦、巴黎和罗马三个欧洲首都收集。数据收集过程采用计算机辅助个人访谈法（Computer-Assisted Personal Interviewing, CAPI），该方法通过计算机辅助进行直接访谈。调查在每个城市的核心旅游地点开展，以获取具有代表性的游客样本。共有1859名受访者参与研究，其中一半为国内游客，另一半为国际游客。问卷以英文编制，并被翻译成另外四种语言。随后，研究团队成员及母语使用者对其进行验证，以确保问卷表述的清晰度与可理解性达到最优。选择CAPI技术有助于通过提高应答率、减少因问题误解导致的误差及消除缺失应答来收集高质量数据。收集的数据涵盖多种变量，包括旅游空间拥挤感知、目的地吸引力、旅行满意度及游客忠诚度。此外，研究还考虑了多种出行方式，如公共交通、主动出行（active mobility，步行、骑行）及微出行。数据采用偏最小二乘结构方程模型（Partial Least Squares Structural Equation Modeling, PLS-SEM）及多组分析进行统计分析，从而能够识别影响不同城市环境下游客感知的关键因素。