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The advent of electronic word-of-mouth (eWOM) communities and review sites has strongly affected the tourism industry, changing the way hotels and accommodations build credibility and a good image. Few studies have tested, among eWOM communities, the predictors and factors that directly affect positive eWOM reviews. Using a directory of Spanish hotels in TripAdvisor, the purpose of this paper is to critically discuss and examine the predictors of positive eWOM, from the hoteliers’ perspective.

After the literature review on eWOM, hypotheses regarding predictors of positive eWOM were developed. Correlation and regression analyses were conducted to empirically validate the relation between hotel and personal characteristics and positive eWOM among a population of 335 hotels.

Results suggest that commitment and competence are factors affecting positive eWOM. The study found that hoteliers do not perceive hotel characteristics (category, size, and type of ownership) or the use of the review site as predictors of positive valence. Instead, they perceive commitment and competence as the main predictors.

The identification of the predictors of positive valence is important to get better eWOM valence. It would allow hotels to improve some factors that affect positive eWOM. In this way, the hotel resources and efforts would be better targeted.

The identification of the predictors of positive valence is important to get better eWOM valence. It would enable hotels to improve some factors and characteristics that affect positive eWOM. In this way, the hotel resources and efforts would be better targeted.

This paper investigates the factors affecting positive reviews on hotels, from the hoteliers’ perception. The study will help researchers to understand positive eWOM formation. Moreover, this study will provide marketers with information on how to improve efforts to obtain positive reviews.

This study aims to explore the relationship between Generation Z (or Gen Z) consumers’ decision-making styles and electronic word-of-mouth (eWOM) use in the tourism sector. Drawing on the consumer style inventory (CSI) model and the theory of reasoned action (TRA), the research examines how specific decision-making styles influence Gen Z’s propensity to use eWOM recommendations for accommodation choices.

The study uses structural equation modelling to analyse data collected from 296 Gen Z users of Booking.com. The CSI model is adapted to the analysed context and attributes – impulsive, recreational, sustainable, fashion-conscious and perfectionist attitudes – are examined to determine their impact on eWOM use intention and actual eWOM use.

Three of the hypothesised relationships in the model were validated. Specifically, the results suggest that the attitudes of sustainable and perfectionist consumers influence the intention to use eWOM. Furthermore, use intention is positively associated with the actual use of eWOM.

For marketers and tourism businesses, understanding the decision-making styles of Gen Z can inform the development of targeted marketing strategies that emphasise quality and sustainability. Highlighting these aspects in online reviews and eWOM platforms can enhance engagement with Gen Z consumers.

This research advances the understanding of eWOM behaviour by integrating CSI and TRA theories in the context of Gen Z’s tourism decision-making. It provides empirical evidence on the significant role of perfectionist and sustainable attitudes in shaping eWOM intentions, contributing to the literature on consumer behaviour and digital marketing in tourism.

This study aims to develop a measurement model to help hotels manage their reputation within the context of online reviews and ratings platforms and evaluate the impact of this reputation management on the benefits derived by the hotels, as perceived by their managers.

Partial least squares was used to assess the model and make a causal predictive analysis, using data from a survey of a random sample of 335 Spanish hotel managers and personnel involved in reputation management.

This study shows the operationalization of hotel reputation management as a superordinate second-order construct affecting six individual first-order dimensions, strongly impacting on three key benefits as perceived by hotel managers (i.e. financial benefits, customer relationship benefits and customer-based brand benefits), within the context of online review platforms.

Based on the results of this study, hotel managers can improve the effectiveness of their management of ratings and reviews. They can also learn which aspects they should focus on when managing ratings and reviews.

Based on the opinions of hotel managers, a causal model for managing online reviews was developed and validated. This study shows how reputation management affects the benefits derived by hotels as perceived by their managers.

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.