A Text Mining-based Review of Cause-related Marketing Literature

Abstract

Cause-related marketing (C-RM) has risen to become a popular strategy to increment business value through turn a profit-motivated giving. Despite the growing number of articles published in the last decade, no comprehensive analysis of the well-nigh discussed constructs of crusade-related marketing is bachelor. This newspaper uses an advanced Text Mining methodology (a Bayesian contextual assay algorithm known as Correlated Topic Model, CTM) to deport a comprehensive analysis of 246 articles published in xl unlike 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 example, it is shown that the nearly prominent long-term topics discussed since 1988 on the subject are "brand-cause fit", "law and Ideals", 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: commencement, information technology introduces the technique of CTM to the Marketing area, illustrating how Text Mining may guide, simplify, and heighten review processes while providing objective edifice 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, nonetheless, is not aimed at replacing all subjective decisions that must be taken as part of literature review methodologies.

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Affiliations

1. Business Inquiry Unit (BRU-IUL), ISCTE- University Institute of Lisbon, Lisbon, Portugal

   João Guerreiro & Paulo Rita

2. University of Macau, Macau, Communist china

   Duarte Trigueiros

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Correspondence to João Guerreiro.

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Guerreiro, J., Rita, P. & Trigueiros, D. A Text Mining-Based Review of Cause-Related Marketing Literature. J Jitney Ideals 139, 111–128 (2016). https://doi.org/10.1007/s10551-015-2622-4

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• Received: 22 September 2014

• Accepted: thirteen March 2015

• Published: 25 March 2015

• Issue Date: November 2016

• DOI : https://doi.org/10.1007/s10551-015-2622-4

Keywords

• Cause-related marketing
• Text mining
• Topic models

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