Research trends on Big Data in Marketing: A text mining and topic modeling based literature analysis

Detalhes bibliográficos
Autor(a) principal: Amado, Alexandra
Data de Publicação: 2018
Outros Autores: Cortez, Paulo, Rita, Paulo, Moro, Sergio
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/1822/62743
Resumo: Given the research interest on Big Data in Marketing, we present a research literature analysis based on a text mining semi-automated approach with the goal of identifying the main trends in this domain. In particular, the analysis focuses on relevant terms and topics related with five dimensions: Big Data, Marketing, Geographic location of authors' affiliation (countries and continents), Products, and Sectors. A total of 1560 articles published from 2010 to 2015 were scrutinized. The findings revealed that research is bipartite between technological and research domains, with Big Data publications not clearly aligning cutting edge techniques toward Marketing benefits. Also, few inter-continental co-authored publications were found. Moreover, findings show that research in Big Data applications to Marketing is still in an embryonic stage, thus making it essential to develop more direct efforts toward business for Big Data to thrive in the Marketing arena.
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spelling Research trends on Big Data in Marketing: A text mining and topic modeling based literature analysisBig dataMarketingLiterature analysisResearch trendsText miningSocial SciencesM15M31Given the research interest on Big Data in Marketing, we present a research literature analysis based on a text mining semi-automated approach with the goal of identifying the main trends in this domain. In particular, the analysis focuses on relevant terms and topics related with five dimensions: Big Data, Marketing, Geographic location of authors' affiliation (countries and continents), Products, and Sectors. A total of 1560 articles published from 2010 to 2015 were scrutinized. The findings revealed that research is bipartite between technological and research domains, with Big Data publications not clearly aligning cutting edge techniques toward Marketing benefits. Also, few inter-continental co-authored publications were found. Moreover, findings show that research in Big Data applications to Marketing is still in an embryonic stage, thus making it essential to develop more direct efforts toward business for Big Data to thrive in the Marketing arena.Elsevier Science LtdUniversidade do MinhoAmado, AlexandraCortez, PauloRita, PauloMoro, Sergio20182018-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/1822/62743eng2444-883410.1016/j.iedeen.2017.06.002https://www.sciencedirect.com/science/article/pii/S2444883417300268info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-07-21T12:22:05Zoai:repositorium.sdum.uminho.pt:1822/62743Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T19:15:31.677418Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Research trends on Big Data in Marketing: A text mining and topic modeling based literature analysis
title Research trends on Big Data in Marketing: A text mining and topic modeling based literature analysis
spellingShingle Research trends on Big Data in Marketing: A text mining and topic modeling based literature analysis
Amado, Alexandra
Big data
Marketing
Literature analysis
Research trends
Text mining
Social Sciences
M15
M31
title_short Research trends on Big Data in Marketing: A text mining and topic modeling based literature analysis
title_full Research trends on Big Data in Marketing: A text mining and topic modeling based literature analysis
title_fullStr Research trends on Big Data in Marketing: A text mining and topic modeling based literature analysis
title_full_unstemmed Research trends on Big Data in Marketing: A text mining and topic modeling based literature analysis
title_sort Research trends on Big Data in Marketing: A text mining and topic modeling based literature analysis
author Amado, Alexandra
author_facet Amado, Alexandra
Cortez, Paulo
Rita, Paulo
Moro, Sergio
author_role author
author2 Cortez, Paulo
Rita, Paulo
Moro, Sergio
author2_role author
author
author
dc.contributor.none.fl_str_mv Universidade do Minho
dc.contributor.author.fl_str_mv Amado, Alexandra
Cortez, Paulo
Rita, Paulo
Moro, Sergio
dc.subject.por.fl_str_mv Big data
Marketing
Literature analysis
Research trends
Text mining
Social Sciences
M15
M31
topic Big data
Marketing
Literature analysis
Research trends
Text mining
Social Sciences
M15
M31
description Given the research interest on Big Data in Marketing, we present a research literature analysis based on a text mining semi-automated approach with the goal of identifying the main trends in this domain. In particular, the analysis focuses on relevant terms and topics related with five dimensions: Big Data, Marketing, Geographic location of authors' affiliation (countries and continents), Products, and Sectors. A total of 1560 articles published from 2010 to 2015 were scrutinized. The findings revealed that research is bipartite between technological and research domains, with Big Data publications not clearly aligning cutting edge techniques toward Marketing benefits. Also, few inter-continental co-authored publications were found. Moreover, findings show that research in Big Data applications to Marketing is still in an embryonic stage, thus making it essential to develop more direct efforts toward business for Big Data to thrive in the Marketing arena.
publishDate 2018
dc.date.none.fl_str_mv 2018
2018-01-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/1822/62743
url http://hdl.handle.net/1822/62743
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2444-8834
10.1016/j.iedeen.2017.06.002
https://www.sciencedirect.com/science/article/pii/S2444883417300268
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier Science Ltd
publisher.none.fl_str_mv Elsevier Science Ltd
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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