A Text Mining-Based Review of Cause-Related Marketing Literature

João Guerreiro (), Paulo Rita () and Duarte Trigueiros ()
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João Guerreiro: Business Research Unit (BRU-IUL), ISCTE- University Institute of Lisbon
Paulo Rita: Business Research Unit (BRU-IUL), ISCTE- University Institute of Lisbon
Duarte Trigueiros: University of Macau

Journal of Business Ethics, 2016, vol. 139, issue 1, No 8, 128 pages

Abstract: 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.

Keywords: Cause-related marketing; Text mining; Topic models (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (27)

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DOI: 10.1007/s10551-015-2622-4

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