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

机译：基于文本挖掘的因果相关营销文献综述

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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.
机译：与因果相关的营销（C-RM）已成为一种流行的策略，可以通过以利润为导向的捐赠来增加业务价值。尽管在过去十年中发表的文章数量不断增加，但仍无法对讨论最多的与因果相关的营销结构进行全面分析。本文使用一种先进的文本挖掘方法（一种称为相关主题模型，CTM的贝叶斯语境分析算法）对1988年至2013年间40种不同期刊上发表的246篇文章进行了全面分析，涉及因果营销。文本挖掘还允许对文献进行定量分析。例如，可以看出，自1988年以来，关于该主题的最突出的长期话题是“品牌因果关系”，“法律与道德”以及“企业和社会认同”，而目前最活跃的话题是“引起社会禁忌和道德辩论的部门”。本文有两个目标：首先，它将CTM技术引入市场营销领域，说明文本挖掘如何指导，简化和增强评论过程，同时提供要在评论中使用的客观组成部分（主题）；其次，它将CTM应用于C-RM领域，发现并总结了讨论最多的主题。但是，挖掘文本并非旨在取代所有必须作为文献综述方法的一部分而做出的主观决定。

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