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This study proposes an extension of the theory of planned behaviour (TPB) model to understand international travellers' intentions to visit Spain. This study aims to compare whether the predictive variables of the intention to travel differ depending on nationality. The extension includes: perceived risk, loyalty to the destination, past travel experience, public opinion climate and electronic word-of-mouth (eWOM).

A multiple-indicator, multiple-cause (MIMIC) model was developed as a structural equational model to predict the 1,978 participants' intention to travel. The structural model was used to determine the theoretical model for the total sample and by nationality (Germans, Britons and those from other European countries).

The extended models fitted the data well, explaining 64%–68% of the total variance, while differences depending on tourist nationality were also found. The MIMIC model indicated that German people's intention to travel to a holiday destination was influenced by their perception of risk, eWOM and loyalty to the country. In the British group, only the TPB variables were relevant. For those of other European nationalities, loyalty and eWOM were also significant. Travel experience, used as a variable in previous studies, was shown not to be significant. Overall, these results offer insights into how people from diverse countries and cultures embrace the aforementioned constructs when making travel decisions.

This study also has practical implications for policymakers in holiday tourism destinations, such as Spain. In particular, this study provides a better understanding of Britons' and Germans' travel intentions and could be beneficial for guiding policies for the recovery of the tourism industry in major tourism destinations.

Previous studies have applied various extended TPBs to one specific country or made comparisons between Asian countries. This study’s proposal makes a comparison of the variables used to predict the intention to visit a holiday destination among the European countries.

本研究提出了计划行为理论 (TPB) 模型的扩展, 以了解国际旅行者访问西班牙的意图。目的是比较旅行意图的预测变量是否因国籍而异。扩展包括：感知风险、对目的地的忠诚度、过去的旅行经历、舆论氛围和电子口碑（eWOM）。

开发了一个多指标、多原因 (MIMIC) 模型作为结构方程模型来预测 1,978 名参与者的旅行意图。结构模型用于确定总样本和国籍（德国人、英国人和来自其他欧洲国家的人）的理论模型。

扩展模型很好地拟合了数据, 解释了总方差的 64%–68%, 同时还发现了取决于旅游国籍的差异。 MIMIC 模型表明, 德国人前往度假目的地的意愿受到他们对风险、eWOM 和对国家忠诚度的认知的影响。在英国组中, 只有 TPB 变量是相关的。对于其他欧洲国家的人来说, 忠诚度和 eWOM 也很重要。旅行经验, 在以前的研究中用作变量, 被证明并不重要。总体而言, 这些结果提供了有关来自不同国家和文化的人们在做出旅行决定时如何接受上述结构的见解。

以前的研究已经将各种扩展的 TPB 应用于一个特定的国家或在亚洲国家之间进行了比较。我们的建议对用于预测欧洲国家旅游目的地意图的变量进行了比较。

Este estudio propone una extensión del modelo de la teoría del comportamiento planificado (TPB) para comprender las intenciones de visitar España de los viajeros internacionales. El objetivo es comparar si las variables que predicen la intención de viajar difieren según la nacionalidad. Esta extensión del modelo incluye variables como: riesgo percibido, lealtad al destino, experiencia de viaje anterior, clima de la opinión pública y el boca a boca electrónico (eWOM).

Se desarrolló un modelo de indicadores y causas múltiples (MIMIC) como modelo de ecuaciones estructurales para predecir la intención de viajar de los 1978 participantes. El modelo estructural se utilizó para comprobar el modelo teórico para la muestra total y por nacionalidades (alemanes, británicos y otros países europeos).

Los modelos ampliados propuestos se ajustaron bien a los datos, explicando entre el 64% y el 68% de la varianza total, si bien se encontraron diferencias en función de la nacionalidad del turista. El modelo MIMIC indicó que la intención de los alemanes de viajar a un destino de vacaciones estaba influenciada por su percepción de riesgo, el eWOM y la lealtad a España. En el grupo británico, solo las variables TPB resultaron relevantes. Para el grupo de otras nacionalidades europeas, la lealtad y el eWOM también fueron significativas. Sin embargo, la experiencia de viaje, utilizada en estudios previos, se mostró no significativa en todos los grupos. En general, estos resultados ofrecen información sobre cómo las personas de diversos países y culturas adoptan los constructos antes mencionados cuando toman decisiones de viaje.

Estudios previos han aplicado varios TPB extendidos a un país específico o han comparado los resultados entre países asiáticos. Nuestra propuesta hace una comparación de las variables utilizadas para predecir la intención de visitar un destino vacacional entre países europeos.

Recommendation systems are a fundamental tool for hotels to adopt a differentiating competitive strategy. The main purpose of this work is to use machine learning techniques to treat imbalanced data sets, not applied until now in the tourism field. These techniques have allowed the authors to analyse the influence of imbalance data on hotel recommendation models and how this phenomenon affects client dissatisfaction.

An opinion survey was conducted among hotel customers of different categories in 120 different countries. A total of 135.102 surveys were collected over eleven quarters. A longitudinal design was conducted during this period. A binary logistic model was applied using the function generalized lineal model (GLM).

Through the analysis of a representative amount of data, the authors empirically demonstrate that the imbalance phenomenon is systematically present in hotel recommendation surveys. In addition, the authors show that the imbalance exists independently of the period in which the survey is done, which means that it is intrinsic to recommendation surveys on this topic. The authors demonstrate the improvement of recommendation systems highlighting the presence of imbalance data and consequences for marketing strategies.

The main contribution of the current work is to apply to the tourism sector the framework for imbalanced data, typically used in the machine learning, improving predictive models.

The purpose of this paper is to contribute to the understanding of the tourism service experience of consumers with vulnerabilities. Moreover, this research analyses the pre-core service encounter in the tourism services sector, which is one of the most important phases in the service experience. The objective is to understand how vulnerability might influence risk perceptions when people travel. To this end, this study focusses on individuals with coeliac disease (CD) and non-coeliac gluten sensitivity (NCGS) as a specific group to test the hypotheses. For the millions of individuals with CD or NCGS, food is one of the most critical elements of a trip and the reason for vulnerability perception. The research also proposes measures suggested by survey respondents to improve the information search process of vulnerable travellers before a trip.

A mixed-method was used based on a survey of 813 responses from people diagnosed with CD and NCGS. The individuals were placed in three groups according to their perception of how strongly their disease impacts their trips: low impact, medium impact and high impact.

The results confirm that people with a high-risk perception due to their vulnerability spend more time searching for information prior to the trip than people without this perception. In this sense, individuals that feel more vulnerable, tend to use more personal information sources and also make greater use of online information sources. The participants affected by CD and NCGS proposed measures to reduce their perceived vulnerability. These proposals are based on information about the disease, specific information from the tourist industry at the destination and various online, as well as offline information channels.

The novelty of this research is mainly found in the study in the study of how consumers with vulnerabilities behave during the information process before travelling. From a holistic approach and based on both, marketing service theory and the risk perception perspectives, this research is focussed on vulnerable individuals affected by CD and NCGS to find answers to the problems they face during the pre-core service encounter.

This paper aims to study the effect of imbalanced data in tourism quality models. It is demonstrated that this imbalance strongly affects the accuracy of tourism prediction models for hotel recommendation.

A questionnaire was used to survey 83,740 clients from hotels between five and two or less stars using a binary logistic model. The data correspond to a sample of 87 hotels from all around the world (120 countries from America, Africa, Asia, Europe and Australia).

The results of the study suggest that the imbalance in the data affects the prediction accuracy of the models used, especially to the prediction provided by unsatisfied clients, tending to consider them as satisfied customers.

In this sense, special attention should be given to unsatisfied clients or, at least, some safeguards to prevent the effect of the imbalance of data should be included in the models.

In the tourism industry, the strong imbalance between satisfied and unsatisfied customers produces misleading prediction results. This fact could have effects on the quality policy of hoteliers.

In this work, focusing on tourism data, it is shown that this imbalance strongly affects the prediction accuracy of the models used, especially to the prediction of the recommendation provided by unsatisfied customers, tending to consider them as satisfied customers; a methodological approach based on the balance of the data set used to build the models is proposed to improve the accuracy of the prediction for unsatisfied customers provided by traditional services quality models.