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

• Published in: Journal of business ethics Vol. 139; no. 1; pp. 111 - 128
• Main Authors: Guerreiro, João, Rita, Paulo, Trigueiros, Duarte
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer 01.11.2016; Springer Netherlands; Springer Nature B.V
• Subjects: Advertising campaigns; Algorithms; Altruism; Anniversaries; Automation; Bayesian analysis; Brands; Business and Management; Business entities; Business Ethics; Charities; Charity; Clustering; Consumer goods industries; Contextual analysis; Corporate image; Corporate responsibility; Corporate social responsibility; Corporate sponsorship; Data mining; Education; Ethics; Identification; Keywords; Law; Legal documents; Literature reviews; Management; Market strategy; Marketing; Paper profit; Philosophy; Purchase intention; Quality of Life Research; Quantitative analysis; Social identification; Social responsibility; Strip mining; Studies; Taboos; Topics; Trends; Macao; Text mining; Cause-related marketing; Topic models
• ISSN: 0167-4544; 1573-0697
• DOI: 10.1007/s10551-015-2622-4

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.