User experiences in fishing tourism

This study aims to address the limited academic exploration of fishing tourism by means of Pine and Gilmore’s established experiential marketing model by analysing online reviews of fishing tourism activities on TripAdvisor. To do so, we use machine learning techniques combining supervised and unsup...

Descripción completa

Detalles Bibliográficos
Autores: Toro Sánchez, Fernando, Martín Fuentes, Eva, Perdomo Verdicia, Vladimir
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2025
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10459.1/469171
Acceso en línea:https://doi.org/10.1080/02508281.2025.2569872
https://hdl.handle.net/10459.1/469171
http://hdl.handle.net/10459.1/469171
Access Level:acceso embargado
Palabra clave:Fishing tourism
User experience
User-generated content
id ES_f3ecedcbb96c68add8d09d2ccd150e25
oai_identifier_str oai:recercat.cat:10459.1/469171
network_acronym_str ES
network_name_str España
repository_id_str
spelling User experiences in fishing tourismToro Sánchez, FernandoMartín Fuentes, EvaPerdomo Verdicia, VladimirFishing tourismUser experienceUser-generated contentThis study aims to address the limited academic exploration of fishing tourism by means of Pine and Gilmore’s established experiential marketing model by analysing online reviews of fishing tourism activities on TripAdvisor. To do so, we use machine learning techniques combining supervised and unsupervised analysis and with graphic value, which favours decision-making for tourism operators. Our results highlight a clear emphasis on user satisfaction in the overall model, with a significant connection to the escapist sense of the experience. Unsupervised association analysis suggests that user enjoyment across experiential components positively influences satisfaction. It also allows to visualise the Fishing Tourism user's behaviour according to two types of activity: Charter Fishing Tourism, with an active presence of the escapist component, and Combined Fishing Tourism, characterised by a more passive integration – entertainment and aesthetic – of the user in the activity. The study concludes that improvements in one experiential realm can impact others. Methodologically, the use of AI for sentence-level analysis enhances the understanding of relationships between expressions and variables. The expanded model, incorporating new variables like tourism satisfaction and loyalty, reflects the complexity of the contemporary tourism industry.his study has been funded by the Spanish Ministry of Science and Innovation within the RevTour project [Ref: PID2022-138564OA-I00] “Use of online reviews for tourism intelligence and the establishment of transparent and reliable assessment standards", by the Institute of Social and Territorial Development within the ResTur project for the 2023CRINDESTABC call, and OPP78 has received aid from the European Union under the Production and Marketing Plans Program, year 2023 with FEMPA Andalusia 2021-2027 funds for Action 10: Tourist reservations websites.Taylor and Francis Group2025info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionhttps://doi.org/10.1080/02508281.2025.2569872https://hdl.handle.net/10459.1/469171http://hdl.handle.net/10459.1/469171reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)Inglésinfo:eu-repo/grantAgreement/AEI//PID2022-138564OA-I00Versió postprint del document publicat a https://doi.org/10.1080/02508281.2025.2569872Tourism Recreation Research, 2025, In presscc-by-nc (c) Taylor and Francis Group, 2025Attribution-NonCommercial 4.0 Internationalinfo:eu-repo/semantics/embargoedAccesshttp://creativecommons.org/licenses/by-nc/4.0/oai:recercat.cat:10459.1/4691712026-05-29T05:05:01Z
dc.title.none.fl_str_mv User experiences in fishing tourism
title User experiences in fishing tourism
spellingShingle User experiences in fishing tourism
Toro Sánchez, Fernando
Fishing tourism
User experience
User-generated content
title_short User experiences in fishing tourism
title_full User experiences in fishing tourism
title_fullStr User experiences in fishing tourism
title_full_unstemmed User experiences in fishing tourism
title_sort User experiences in fishing tourism
dc.creator.none.fl_str_mv Toro Sánchez, Fernando
Martín Fuentes, Eva
Perdomo Verdicia, Vladimir
author Toro Sánchez, Fernando
author_facet Toro Sánchez, Fernando
Martín Fuentes, Eva
Perdomo Verdicia, Vladimir
author_role author
author2 Martín Fuentes, Eva
Perdomo Verdicia, Vladimir
author2_role author
author
dc.subject.none.fl_str_mv Fishing tourism
User experience
User-generated content
topic Fishing tourism
User experience
User-generated content
description This study aims to address the limited academic exploration of fishing tourism by means of Pine and Gilmore’s established experiential marketing model by analysing online reviews of fishing tourism activities on TripAdvisor. To do so, we use machine learning techniques combining supervised and unsupervised analysis and with graphic value, which favours decision-making for tourism operators. Our results highlight a clear emphasis on user satisfaction in the overall model, with a significant connection to the escapist sense of the experience. Unsupervised association analysis suggests that user enjoyment across experiential components positively influences satisfaction. It also allows to visualise the Fishing Tourism user's behaviour according to two types of activity: Charter Fishing Tourism, with an active presence of the escapist component, and Combined Fishing Tourism, characterised by a more passive integration – entertainment and aesthetic – of the user in the activity. The study concludes that improvements in one experiential realm can impact others. Methodologically, the use of AI for sentence-level analysis enhances the understanding of relationships between expressions and variables. The expanded model, incorporating new variables like tourism satisfaction and loyalty, reflects the complexity of the contemporary tourism industry.
publishDate 2025
dc.date.none.fl_str_mv 2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv https://doi.org/10.1080/02508281.2025.2569872
https://hdl.handle.net/10459.1/469171
http://hdl.handle.net/10459.1/469171
url https://doi.org/10.1080/02508281.2025.2569872
https://hdl.handle.net/10459.1/469171
http://hdl.handle.net/10459.1/469171
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/grantAgreement/AEI//PID2022-138564OA-I00
Versió postprint del document publicat a https://doi.org/10.1080/02508281.2025.2569872
Tourism Recreation Research, 2025, In press
dc.rights.none.fl_str_mv cc-by-nc (c) Taylor and Francis Group, 2025
Attribution-NonCommercial 4.0 International
info:eu-repo/semantics/embargoedAccess
http://creativecommons.org/licenses/by-nc/4.0/
rights_invalid_str_mv cc-by-nc (c) Taylor and Francis Group, 2025
Attribution-NonCommercial 4.0 International
http://creativecommons.org/licenses/by-nc/4.0/
eu_rights_str_mv embargoedAccess
dc.publisher.none.fl_str_mv Taylor and Francis Group
publisher.none.fl_str_mv Taylor and Francis Group
dc.source.none.fl_str_mv reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869424423307575296
score 15.812455

Similares en LA Referencia