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http://hdl.handle.net/10071/12127
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Title: A text mining-based review of cause-related marketing literature
Authors: Guerreiro, J.
Rita, P.
Trigueiros, D.
Keywords: Cause-related marketing
Text mining
Topic models
Issue Date: 2016
Publisher: Springer Verlag
Abstract: Cause-related marketing (C-RM) has risen to become a popular strategy to increase business value through profit-motivated giving. Despite the growing number of articles published in the last decade, no comprehensive analysis of the most discussed constructs of cause-related marketing is available. This paper uses an advanced Text Mining methodology (a Bayesian contextual analysis algorithm known as Correlated Topic Model, CTM) to conduct a comprehensive analysis of 246 articles published in 40 different journals between 1988 and 2013 on the subject of cause-related marketing. Text Mining also allows quantitative analyses to be performed on the literature. For instance, it is shown that the most prominent long-term topics discussed since 1988 on the subject are “brand-cause fit”, “law and Ethics”, and “corporate and social identification”, while the most actively discussed topic presently is “sectors raising social taboos and moral debates”. The paper has two goals: first, it introduces the technique of CTM to the Marketing area, illustrating how Text Mining may guide, simplify, and enhance review processes while providing objective building blocks (topics) to be used in a review; second, it applies CTM to the C-RM field, uncovering and summarizing the most discussed topics. Mining text, however, is not aimed at replacing all subjective decisions that must be taken as part of literature review methodologies.
Peer reviewed: yes
URI: http://hdl.handle.net/10071/12127
DOI: 10.1007/s10551-015-2622-4
ISSN: 0167-4544
Ciência-IUL: https://ciencia.iscte-iul.pt/id/ci-pub-23341
Accession number: WOS:000387284300008
Appears in Collections:BRU-RI - Artigos em revistas científicas internacionais com arbitragem científica
DMOG-RI - Artigos em revistas internacionais com arbitragem científica

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