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Mining Delivery Customer Claims in Social Media in Colombia, an Exploratory Analysis Applying Machine Learning Algorithms

dc.contributor.authorEscobar-Sierra, M.
dc.contributor.authorGiraldo, E.Y.G.
dc.date.accessioned2024-12-27T20:52:12Z
dc.date.available2024-12-27T20:52:12Z
dc.date.created2024
dc.identifier.issn16465954
dc.identifier.urihttp://hdl.handle.net/11407/8723
dc.descriptionThe vast amount of available data, computational advances, and increasing social demands from customers present enormous challenges for delivery companies. Recognizing this need, we aim to investigate how such companies manage customer claims through X®. Our research, employing a sequential mixed-methods approach, began with a literature review through bibliometric analysis and subsequent interpretation via content analysis, concluding with the triangulation of theoretical findings with empirical evidence obtained from social media. For the final analysis phase, we collected and analyzed user mentions of delivery brands on X®, creating a data corpus-i.e., a sample collected through techniques-essential for our exploratory analysis. In this big data sample, we applied various natural language processing and machine learning algorithms, uncovering how users of these delivery companies tend to compare brands when making complaints. Furthermore, our research highlighted the psychological bias of users, who tend to polarize between love and hate for brands, respond to other user's posts, and engage in significant interactions with likes. Consequently, factors such as brand comparison, polarization between love and hate, and user interaction emerged as the main predictors of claims to these companies, underscoring the practical implications of our findings for the delivery industry. © 2024 (Escobar-Sierra, Guisao Giraldo).
dc.language.isoeng
dc.publisherObercom
dc.relation.isversionofhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85205490914&doi=10.15847%2fobsOBS18320242382&partnerID=40&md5=e3c1c16a0638605a5cc870a53d107b27
dc.sourceObservatorio
dc.sourceObservatorio
dc.sourceScopus
dc.subjectBig dataeng
dc.subjectDelivery customer claimseng
dc.subjectMachine learning algorithmseng
dc.subjectPolarizationeng
dc.subjectSocial mediaeng
dc.titleMining Delivery Customer Claims in Social Media in Colombia, an Exploratory Analysis Applying Machine Learning Algorithmseng
dc.typeArticle
dc.rights.accessrightsinfo:eu-repo/semantics/restrictedAccess
dc.publisher.programAdministración de Empresas
dc.publisher.programNegocios Internacionales
dc.type.spaArtículo de revista
dc.identifier.doi10.15847/obsOBS18320242382
dc.relation.citationvolume18
dc.relation.citationissue3
dc.relation.citationstartpage54
dc.relation.citationendpage74
dc.publisher.facultyFacultad de Ciencias Económicas y Administrativas
dc.publisher.facultyFacultad de Ingenierías
dc.affiliationEscobar-Sierra, M., School of Economic and Administrative Sciences, University of Medellin, Colombia
dc.affiliationGiraldo, E.Y.G., School of Economic and Administrative Sciences, University of Medellin, Colombia
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dc.type.versioninfo:eu-repo/semantics/publishedVersion
dc.identifier.reponamereponame:Repositorio Institucional Universidad de Medellín
dc.identifier.repourlrepourl:https://repository.udem.edu.co/
dc.identifier.instnameinstname:Universidad de Medellín


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