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dc.contributor.authorLahuerta Otero, Eva 
dc.contributor.authorCordero Gutiérrez, Rebeca 
dc.contributor.authorPrieta Pintado, Fernando de la 
dc.date.accessioned2024-01-29T09:34:07Z
dc.date.available2024-01-29T09:34:07Z
dc.date.issued2018-08-21
dc.identifier.issn1468-4527
dc.identifier.urihttp://hdl.handle.net/10366/154836
dc.description.abstract[EN]Purpose – Due to the size and importance of social media, user-generated content analysis is becoming a key factor for companies and brands across the world. By using Twitter messages’ content, the purpose of this paper is to identify which elements of the messages enable tweet diffusion and facilitate eWOM. Design/methodology/approach – In total, 30,082 tweets collected from 10,120 Twitter users were classified based on four assorted brands. By comparing with multiple regression techniques high vs low purchase involvement and hedonic vs utilitarian products and using the theory of heuristic-systematic processing of information, the authors examine the causes of tweet diffusion. Findings – The authors illustrate how the elements of a tweet (hashtags, mentions, links, sentiment or tweet length) influence its diffusion and popularity. Research limitations/implications – This study validated the use of information processing theories in the social media field. The study showed a picture on how different Twitter elements influence eWOM and message diffusion under several purchase involvement situations. Practical implications – The results of this study can help social media brand community managers of all types of companies on how to write their Twitter messages to obtain greater dissemination and popularity. Originality/value – The study offers a unique deep brand analysis which helps brands and companies to understand their social media popularity in detail. Depending on product category, companies can achieve maximum social impactes_ES
dc.language.isoenges_ES
dc.rightsAtribución-NoComercial-CompartirIgual 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/*
dc.subjectTwitteres_ES
dc.subjectDiffusiones_ES
dc.subjectHeuristic-systematic modeles_ES
dc.subjectPerceived purchased valuees_ES
dc.subjectPopularityes_ES
dc.subjectPurchase involvementes_ES
dc.titleRetweet or like? That is the questiones_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publishversionhttps://www.emerald.com/insight/content/doi/10.1108/OIR-04-2017-0135/full/htmles_ES
dc.identifier.doi10.1108/OIR-04-2017-0135
dc.relation.projectIDTSI-100104-2015-007es_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.journal.titleOnline Information Reviewes_ES
dc.volume.number42es_ES
dc.issue.number5es_ES
dc.page.initial562es_ES
dc.page.final578es_ES
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones_ES


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