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Short-term rentals (STRs) (like Airbnb) are reshaping social behaviour, notably in gastronomy, altering how people dine while travelling. This study delves into revenue management, examining the impact of seasonality and dining options near guests’ Airbnb. Machine Learning analysis of Airbnb data suggests owners enhance revenue strategies by adjusting prices seasonally, taking nearby food amenities into account.

This study analysed 220 Airbnb establishments from Madrid, Spain, using consistent monthly price data from Seetransparent and environment variables from MapInfo GIS. The Machine Learning algorithm calculated average prices, determined seasonal prices, applied factor analysis to categorise months and used cluster analysis to identify tourism-dwelling typologies with similar seasonal behaviour, considering nearby supermarkets/restaurants by factors such as proximity and availability of food options.

The findings reveal seasonal variations in three groups, using Machine Learning to improve revenue management: Group 1 has strong autumn-winter patterns and fewer restaurants; Group 2 shows higher spring seasonality, likely catering to tourists, and has more restaurants, while Group 3 has year-round stability, fewer supermarkets and active shops, potentially affecting local restaurant dynamics. Food establishments in these groups may need to adapt their strategies accordingly to capitalise on these seasonal trends.

Current literature lacks information on how seasonality, rental housing and proximity to amenities are interconnected. The originality of this study is to fill this gap by enhancing the STR price predictive model through a Machine Learning study. By examining seasonal trends, rental housing dynamics, and the proximity of supermarkets and restaurants to STR properties, the research enhances our understanding and predictions of STR price fluctuations, particularly in relation to the availability and demand for food options.

This study aims to evaluate the usability and effectiveness of a mobile application, ValTO, designed to enhance communication and professional reasoning skills in occupational therapy students through a case-based learning approach.

A descriptive exploratory study was conducted with 32 second-year occupational therapy students. The usability of the app was assessed using the Mobile Application Rating Scale and the System Usability Scale, complemented by the University of Oviedo’s Learning Satisfaction Survey.

The majority of students (77.8%) rated the app above average on the System Usability Scale, with 50% scoring it as excellent. Mobile Application Rating Scale scores revealed high ratings across functionality, aesthetics and information quality, with a significant correlation between app usability and user satisfaction. Increased student satisfaction was also observed in the Learning Satisfaction Survey compared to previous years.

ValTO integrates modern mobile health tools into occupational therapy education, enhancing students’ decision-making skills in an innovative, real-world context. This study contributes to the growing body of research on mobile health applications in educational settings, demonstrating their potential to improve both student engagement and learning outcomes.

High-end gastronomic tourism is currently trending and significantly impacts a destination’s image. This research aims to demonstrate the importance of high-level local gastronomy in forming the destination image from a semiotic perspective.

To achieve this objective, the three phases of the upscale dining experience – pre-visit, in situ and post-visit – are examined from a descriptive (informative use), appraisive (valuative use) and prescriptive (incitive use) semiotic perspective. This conceptual model includes six constructs: restaurant image, consumer need for status, consumer expectations, consumer satisfaction, restaurant loyalty and destination loyalty. The study is based on a survey of high-end restaurant customers (N = 421).

The research findings highlight that the characteristics of upscale dining establishments influence customer expectations, while customer satisfaction plays a crucial role in fostering loyalty towards both the restaurant and the destination. Additionally, the study reveals that individuals' social status or reputation moderates their expectations and satisfaction levels.

Although studies relate gastronomic image to global destination image, this relationship from a semiotic perspective has not been demonstrated through surveys. This proposed three-phase model based on the Peircean semiotic triad and Morris semiotic trichotomies not only addresses a gap in the existing literature but also offers valuable insights for destination managers and restaurant owners.