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Títol: Inside Airbnb’s performance and adaptive strategies in Barcelona using artificial neural networks: A longitudinal, spatial, and multi-host perspective
Autoria: Morales-Pérez, Soledad  
Meseguer-Artola, Antoni  
Garay, Lluis  
Llados-Masllorens, Josep  
Citació: Morales Pérez, S. [Soledad], Meseguer-Artola, A. [Antoni] Garay-Tamajón, L. [Lluís] & Lladós Masllorens, J. [Josep] (2024). Inside Airbnb's performance and adaptive strategies in Barcelona using artificial neural networks: A longitudinal, spatial, and multi-host perspective. Journal of Hospitality and Tourism Management, 59, 238-250. doi: 10.1016/j.jhtm.2024.04.010
Resum: This research explores the Airbnb platform's performance and adaptive strategies by analysing its spatial, temporal, and multi-host patterns. A three-layer model based on machine learning and neural networks, compared with a multiple linear regression, Random Forest Regression (RFR), and Support Vector Regression (SVR) methods, is used to conduct a longitudinal analysis of three representative months for tourism each year from 2016 to 2022. The study reveals the importance of “minimum nights”, active price management and professionalization, coupled with the potential transfer of accommodations in the medium- and long-term residential markets, as the platform's adaptive strategies. The findings also suggest a shift towards more professional host profiles and the consolidation of new tourist hubs in the city in post-Covid period. The study contributes to the understanding of Airbnb's performance and impact on global urban dynamics and demonstrates an application of machine learning to tourism and hospitality research. Theoretical and practical implications are discussed.
Paraules clau: Airbnb
DOI: http://doi.org/10.1016/j.jhtm.2024.04.010
Tipus de document: info:eu-repo/semantics/article
Versió del document: info:eu-repo/semantics/submittedVersion
Data de publicació: 3-mai-2024
Llicència de publicació: http://creativecommons.org/licenses/by-nc-nd/3.0/es/  
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