Enlightening tourism -- V. 15, n. 1 (2025)

Permanent URI for this collectionhttps://hdl.handle.net/10272/25310

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    Uncovering the predictive power of neural networks in the adoption of beacon technology in the tourism sector: Adoption of beacon technology in the tourism sector
    (Universidad de Huelva, 2025) Liébana Cabanillas, Francisco; Lara Rubio, Juan; García Carrión, Beatriz; Hernández Garrido, Rocío
    This study examines the main factors influencing the adoption of location-based mobile services (LBS) powered by beacon technology in the tourism sector. Using logistic regression models and neural networks, specifically the multilayer perceptron (MLP), this research identifies eleven significant variables driving the adoption process. Among these, system quality, trust, perceived ease of use, perceived usefulness, and service quality stand out as the most influential factors. The MLP model demonstrated superior performance with a classification accuracy of 99.14% and an area under the curve (AUC) of 0.947, highlighting the exceptional predictive capability of non-parametric models over traditional logistic regression. These findings underscore the importance of system trust and reliability in driving users' adoption of beacon-based applications. Additionally, this study provides valuable insights for marketing professionals and tourism stakeholders, suggesting that enhancing user trust, improving system quality, and simplifying the user experience can positively impact LBS figures in the tourism sector. The results provide a solid foundation for leveraging advanced predictive models to improve the operational efficiency of digital solutions in tourism. ------------------------------------------------------------------------------------------------------
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    Evaluation of Vegan and Vegetarian Customers’ Reviews Within the Scope of Expectancy-Disconfirmation Model
    (Universidad de Huelva, 2025) Cetin Gürkan, Güney; Özyurt, Basak; Kübra, CelÕloglu Aylan
    The increase in the number of vegans/vegetarians in recent years has led to their evaluation as potential tourists in the tourism sector. This study investigates whether Antalya, a well-known tourist destination in Turkey, meets the needs of vegan/vegetarian tourists. Using social media comments as data, the study applied the Expectancy-Disconfirmation Model (EDM) to evaluate tourist experiences. The results show that 87.5% of vegan visitors had their expectations met, while 12.5% reported dissatisfaction. Of the satisfied tourists, 46% indicated an intention to return and recommend the destination. Positive feedback highlighted friendly staff, quality of service, pleasant atmosphere, convenient location, and reasonable prices. In contrast, negative feedback, although less frequent, mentioned inadequate labeling of vegan food, language barriers with staff, and inconsistent quality of service. This study highlights the importance of food quality, effective labeling, and service standards in ensuring the satisfaction of vegan travelers. To increase Antalya's appeal to vegan tourists, it is recommended that businesses train staff on vegan dietary needs, improve English language skills, and offer diverse, clearly labeled vegan menu options. These measures can help position Antalya as a more attractive destination for vegan travelers while increasing overall customer satisfaction