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Exploring consumer boycott intelligence using a socio-cognitive approach

Open access
Farah, Maya F., Newman, Andrew J.·2010·Journal of Business Research

Abstract

Despite a worldwide growth in the number of boycott campaigns, the results of studies are inconclusive as the motives behind individual participation are still largely ignored. Drawing on a socio-cognitive theory, the theory of planned behavior, this research investigates whether the direct variables of attitude, subjective norm and perceived behavioral control, help predict consumers' boycott intention. Conducted in Lebanon, this work employs a survey design administered to a randomized systematic sample of 500 Muslim and Christian consumers. The sample is split into two sub-samples reflecting the main religious groups in the Middle-East. Results show that although the Muslim participants appear more prone to participate in the boycott, still attitude, subjective norms, and perceived behavioral control are all significant predictors of intentions in both communities with the attitudinal component carrying the most weight. This application of a social psychology theory to the consumers' passive resistance to purchasing yielded significant contributions at the theoretical, empirical, and managerial levels.

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Metadata

Authors
Farah, M. F., & Newman, A. J.
Venue
Journal of Business Research
Publisher
Elsevier BV
Published
2010-04
Volume
63
Issue
4
Pages
347-355
ISSN
0148-2963 (ISSN-L)
OpenAlex
W2117762169
Cited by
193
References
8
FWCI
18.72
Type
journal article
Language
en
Open access
hybrid

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CoChat. (2026). Citation check: Exploring consumer boycott intelligence using a socio-cognitive approach. https://app.cochat.ai/papers/10.1016/j.jbusres.2009.03.019
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Farah, M. F., & Newman, A. J. (2010). Exploring consumer boycott intelligence using a socio-cognitive approach. Journal of Business Research. https://doi.org/10.1016/j.jbusres.2009.03.019

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.