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dc.contributor.authorDe Sancha Navarro, Jesús Manuel
dc.contributor.authorLara Rubio, Juan 
dc.date.accessioned2021-03-09T07:59:57Z
dc.date.available2021-03-09T07:59:57Z
dc.date.issued2021
dc.identifier.citationSancha-Navarro, J.M.D.; Lara-Rubio, J.; Oliver-Alfonso, M.D.; Palma-Martos, L. Cultural Sustainability in University Students’ Flamenco Music Event Attendance: A Neural Networks Approach. Sustainability 2021, 13, 2911. https://doi.org/10.3390/ su13052911es_ES
dc.identifier.urihttp://hdl.handle.net/10481/66991
dc.description.abstractUniversity students consume live music; however, almost 40% declare that they have never attended a flamenco show, an intangible heritage of humankind. Numerous studies have shown that cultural capital and socioeconomic profile, among other factors, are variables that influence cultural consumption, and therefore, cultural sustainability. Considering the relationship between several variables, this paper pursues a double objective. On the one hand, identifying the factors that influence attendance at flamenco shows, and on the other, proposing a predictive model that quantifies the likelihood of an individual attending a flamenco show. To this end, we analyse flamenco consumption by means of a survey conducted on 452 university students, using Multilayer Perceptrom (a non-parametric model), a methodology based on an artificial neural network. Our results confirm the importance of cultural capital, as well as personal and external factors, among other. The findings of this research work are of potential interest for management and planning of cultural events, as well as to promote cultural sustainability.es_ES
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.rightsAtribución 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectCulture es_ES
dc.subjectSustainabilityes_ES
dc.subjectAttendancees_ES
dc.subjectFlamenco music es_ES
dc.subjectEventes_ES
dc.subjectConsumptiones_ES
dc.titleCultural Sustainability in University Students’ Flamenco Music Event Attendance: A Neural Networks Approaches_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.identifier.doi10.3390/ su13052911


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Atribución 3.0 España
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