Peer learning is not fully developed or researched in online and hybrid higher education. This research analyses a peer learning experience in the asynchronous part of hybrid teaching, in one of the largest blended universities in Europe, promoting students to act as teachers of their peers, by preparing digital content (videos) for the course. This article studies whether there are behaviour patterns and different perceptions associated between students who act as teachers, and those who only act as students. The results indicate, among other findings, that students demand this type of activities, and value them very positively. Specifically, the “teachers” consider that this activity increases their motivation for the subject and their performance; they also consider that it significantly improves their creativity and communication skills, and they would definitely participate in the project again. The assessment of the students who merely view the materials is also very positive, and they prefer a learning method through classmate videos than the traditional learning method with printed materials. The research is also a boost to finding ways to promote learning among equals in non-classroom teaching in digital environments.

Purpose Drones have become an important element within hospitality and tourism. The purpose of this study is to identify the corpus of knowledge and create a research agenda that establishes appropriate guidelines for future study of drone application in hospitality and tourism. Design/methodology/approach This work has been undertaken using a mixed-methods approach that combines quantitative and qualitative research and includes a review of the literature related to the study of drone use in hospitality and tourism. Findings The mixed-methods review identified gaps in the research, potential areas of study to enhance the scientific literature and potential uses of drones in tourism and hospitality for researchers, consumers and industry professionals. Originality/value This study makes an original contribution by establishing an integrated framework, which led to a synthesis of the research corpus and provided a holistic conceptualisation of the relationship between tourism and drones. In addition, the research agenda proposed will help boost and consolidate this emerging field of research.

Digital markets have altered how economic agents interact and have changed the behaviour of tourists. In addition, the COVID-19 pandemic has shown that it is necessary to constantly monitor the evolution of digital consumer behaviour and the factors that influence it, as they are dynamic elements that evolve over time. This paper analyses digital inequalities and validates the main factors influencing tourists to book online tourism services. This research uses a set of microdata with 69,752 and 23,779 observations to analyse the booking mode of accommodation and transportation services, respectively, obtained from the Resident Travel Survey of the National Statistics Institute of Spain during the period 2016-2021. The article confirms variations in the online consumer profile and in the trip's characteristics. One of the most relevant findings is the narrowing of the generational gap in the online contracting of tourist services. However, there are remaining digital inequalities, such as regional inequalities and others based on the education level and income of tourists. It is also highlighted that different types of trips, depending on the destination, the type of accommodation or transport have a different propensity to be booked through digital purchase channels. The accessibility to big data sources and recent advances in machine learning models have also made the methodologies for analysing digital consumer behaviour evolve and must be incorporated into tourism studies. This study compares the predictive performance of different methodologies in the context of e Tourism. In particular, we evaluate the potential predictive power that could be obtained using machine learning techniques to explain consumer behaviour in e-Tourism and use it as a benchmark to compare it with the results obtained using traditional statistical methods. The selected predictive evaluation metrics show that the logistic regression statistical model outperforms the predictive power of the Multilayer Perceptron neural network and presents values very close to the maximum predictive power achieved by the Random Forest algorithm.

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Recibido (26/07/2022) Revisado (30/09/2022) Aceptado (30/11/2022) RESUMEN: El efecto disruptivo de la COVID-19 en la industria turística ha generado nuevas necesidades y motivaciones en los viajes turísticos. Este estudio evalúa los efectos de la pandemia en el perfil y características de los viajes de ocio realizados por los residentes en España. A partir de los microdatos de la Encuesta de Turismo de Residentes del INE, se utiliza el modelo de regresión logística para examinar la relación entre el perfil socioeconómico y demográfico del viajero y las características del viaje con el tipo de destino (internacional o doméstico) para los años 2019-2021. La comparación de los resultados estimados en cada año revela que la motivación del viaje al extranjero difiere significativamente entre los diferentes perfiles de turistas, excepto por razón de género. Los resultados también constatan que las características del viaje fueron significativamente diferentes antes y después de la pandemia. Además, se confirma un cambio significativo en las preferencias del tipo de alojamiento y transporte, junto con una reducción de las diferencias de duración de los viajes a destinos nacionales e internacionales.