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
Keywords:
Aplicaciones móviles, Beacon, Intención de uso, Redes Neuronales
Abstract
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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Published
2025-07-01
How to Cite
Liébana-Cabanillas, F., Lara-Rubio, J., García-Carrión, B., & Hernandez-Garrido, R. (2025). 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. ENLIGHTENING TOURISM. A PATHMAKING JOURNAL, 15(1), 45-92. https://doi.org/10.33776/et.v15i1.8548
Section
RESEARCH PAPERS
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