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Twitter's capacity to forecast tourism demand: the case of way of Saint James

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dc.contributor.authorMendieta Aragón, Adrián
dc.contributor.authorNavío Marco, Julio
dc.contributor.authorGarín Muñoz, María Teresa
dc.date.accessioned2024-08-29T16:19:15Z
dc.date.available2024-08-29T16:19:15Z
dc.date.issued2024-04-25
dc.descriptionThe registered version of this article, first published in European Journal of Management and Business Economics, is available online at the publisher's website: Emerald Publishing, https://doi.org/10.1108/EJMBE-09-2023-0295
dc.descriptionLa versión registrada de este artículo, publicado por primera vez en European Journal of Management and Business Economics, está disponible en línea en el sitio web del editor: Emerald Publishing, https://doi.org/10.1108/EJMBE-09-2023-0295
dc.description.abstractPurpose – Radical changes in consumer habits induced by the coronavirus disease (COVID-19) pandemic suggest that the usual demand forecasting techniques based on historical series are questionable. This is particularly true for hospitality demand, which has been dramatically affected by the pandemic. Accordingly, we investigate the suitability of tourists’ activity on Twitter as a predictor of hospitality demand in the Way of Saint James – an important pilgrimage tourism destination. Design/methodology/approach – This study compares the predictive performance of the seasonal autoregressive integrated moving average (SARIMA) time-series model with that of the SARIMA with an exogenous variables (SARIMAX) model to forecast hotel tourism demand. For this, 110,456 tweets posted on Twitter between January 2018 and September 2022 are used as exogenous variables. Findings – The results confirm that the predictions of traditional time-series models for tourist demand can be significantly improved by including tourist activity on Twitter. Twitter data could be an effective tool for improving the forecasting accuracy of tourism demand in real-time, which has relevant implications for tourism management. This study also provides a better understanding of tourists’ digital footprints in pilgrimage tourism. Originality/value – This study contributes to the scarce literature on the digitalisation of pilgrimage tourism and forecasting hotel demand using a new methodological framework based on Twitter user-generated content. This can enable hospitality industry practitioners to convert social media data into relevant information for hospitality management.en
dc.description.versionversión publicada
dc.identifier.citationMendieta-Aragón, A., Navío-Marco, J. and Garín-Muñoz, T. (2024), ""Twitter's capacity to forecast tourism demand: the case of way of Saint James"", European Journal of Management and Business Economics. https://doi.org/10.1108/EJMBE-09-2023-0295
dc.identifier.doihttps://doi.org/10.1108/EJMBE-09-2023-0295
dc.identifier.issn2444-8494
dc.identifier.urihttps://hdl.handle.net/20.500.14468/23583
dc.journal.titleEuropean Journal of Management and Business Economics
dc.language.isoen
dc.publisherEmerald Publishing
dc.relation.centerFacultades y escuelas::Facultad de Ciencias Económicas y Empresariales
dc.relation.departmentAnálisis Económico
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/deed.es
dc.subject53 Ciencias Económicas
dc.subject.keywordshotel demand forecastingen
dc.subject.keywordssocial mediaen
dc.subject.keywordsbig dataen
dc.subject.keywordstwitteren
dc.subject.keywordspilgrimage tourismen
dc.titleTwitter's capacity to forecast tourism demand: the case of way of Saint Jamesen
dc.typeartículoes
dc.typejournal articleen
dspace.entity.typePublication
relation.isAuthorOfPublicationed719559-27dc-4062-82fd-ada454f60b39
relation.isAuthorOfPublication83ee7c49-5731-4c68-92b3-7e2abeb75dc7
relation.isAuthorOfPublicationf748dddb-6fe0-4e69-a775-0ab94eaf053c
relation.isAuthorOfPublication.latestForDiscoveryed719559-27dc-4062-82fd-ada454f60b39
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