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This study aims to define the concept of slow tourism, describe tourists’ slow food experiences and examine the relationship between the tourists’ slow food experience and their quality of life (QOL).

An online survey distributed through the Amazon MTurk platform yields 453 valid questionnaires. Confirmatory factor analysis and structure equational modeling are used to analyze the data.

The results reveal that authenticity and slowness significantly affect tourists’ slow food experience, while environmental consciousness does not influence tourists’ slow food experiences. Further, the slow food experience elicits a positive impact on the tourists’ QOL.

This study contributes to delineating the scope of slow food experiences, which remains unexplored in previous studies and gives further insight into how they impact life quality. This paper expands the body of knowledge by establishing three factors influencing slow food experiences and further provides valuable advice for tourism marketers at slow destinations.

本研究旨在定义慢活旅游的概念、描述游客的慢食体验以及探讨游客慢食体验与生活品质之间的关系

本研究采用线上调查于亚马逊MTurk平台发放问卷, 共回收453份有效问卷, 本研究采用验证性因素分析及结构方程式模型分析数据

本研究发现『真实性』和『放慢步调』会显著影响游客的慢食体验, 但『环境意识』并不会影响游客的慢食体验。此外, 慢食体验对游客的生活品质有正向的影响。

本研究贡献为描述慢食体验的范畴, 且深入探讨慢食体验如何影响生活品质, 以上都是在先前的研究中未被探讨的。本研究延伸和建立三个影响慢食体验的因素, 且提供慢活旅游目的地行销者珍贵的意见

Este estudio tiene como objetivo definir el concepto de turismo lento (slow), describir las experiencias de “slow food” (“comida lenta”) de los turistas y examinar la relación entre la experiencia de comida lenta de los turistas y su calidad de vida.

Encuesta online distribuida a través de la plataforma Amazon MTurk con 453 cuestionarios válidos. Se ha utilizado el Análisis Factorial Confirmatorio y los Modelos de Ecuaciones Estructurales para el análisis de los datos.

Los resultados revelan que la “autenticidad” y “lentitud” afectan significativamente a la experiencia de la comida lenta en los turistas, mientras que la conciencia ambiental no influye en las experiencias de comida lenta de estos. Además, la experiencia de comida lenta genera un impacto positivo en la calidad de vida de los turistas.

Este estudio contribuye al diseño sobre el alcance de las experiencias de comida lenta, no exploradas en estudios previos y a una mayor comprensión de cómo afectan a la calidad de vida. Este trabajo amplía el conjunto de conocimientos, al establecer tres factores que influyen en las experiencias de comida lenta y ofrece valiosas reflexiones para los especialistas de marketing turístico en destinos lentos.

This research takes Taijiang National Park (TNP) tourists as the study population while gathering the survey data via an online questionnaire. For the data analyses, it uses the importance–performance analysis (IPA) and the Kano two-dimensional quality model to evaluate the tourist satisfaction of TNP. Specifically, it considers the importance of service quality, classifies its service quality attributes, and suggests the priority for service improvement, rendering the TNP valuable reference points to realign service strategies. The study shows that the service quality attributes related to service personnel are the priority item to be improved, which could eventually enhance tourist satisfaction. In addition, brand differentiation could be achieved by improving the attractive quality items identified in this study to enhance tourist loyalty.

This study focuses firstly on the importance for forecasting accuracy of allowing for intervention events in the modeling process. Seasonal autoregressive integrated moving average (SARIMA) models are therefore estimated both with and without intervention effects (the September 11, 2001 events) using data for the period 1990–2001. These models are used to generate forecasts for 2002 and the first part of 2003, and forecast accuracy is assessed using mean absolute percentage error and root mean square percentage error. The second focus of the study is to examine the impacts on tourism demand of the major crises that occurred during the period 2001–2003. The chosen US metropolitan destination is New York City, which was severely affected by the September 11 events, and within New York the US Metropolitan Museum of Art is selected, as this is a very well-known and visited destination for which seasonal data are available over the period 1990–2003. The artificial neural networks (ANNs) and SARIMA forecasts are compared with forecasts generated by the much simpler automatic Holt-Winter's seasonal double exponential smoothing model as well as two naïve forecasting models to ensure that minimum performance standards are being met.