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Cited by 227

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Emotional Compatibility and the Effectiveness of Antidrinking Messages: A Defensive Processing Perspective on Shame and Guilt

Agrawal, Nidhi, Duhachek, Adam·2010·Journal of Marketing Research

Abstract

Five studies examine how the two distinct emotional states of shame and guilt influence the effectiveness of messages that highlight socially undesirable consequences of alcohol consumption. Appeals that frame others as observing versus suffering the negative consequences of binge drinking differentially activate shame and guilt. Given these emotional consequences of message framing, the authors examine the interaction between incidental shame or guilt and message framing on drinking intentions and behavior. Compatible appeals (i.e., appeals that elicit the same emotion as being incidentally experienced by the consumer) are less effective in influencing behavioral intentions and beverage consumption because of a process in which consumers discount the notion that they may cause the negative consequences outlined in the message. Such defensive processing of compatible messages is driven by a desire to reduce the existing negative emotion.

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Metadata

Authors
Agrawal, N., & Duhachek, A.
Venue
Journal of Marketing Research
Published
2010-04
Volume
47
Issue
2
Pages
263-273
ISSN
  • 0022-2437 (ISSN-L)
  • 1547-7193
OpenAlex
W1987593115
Cited by
227
References
2
FWCI
8.84
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Emotional Compatibility and the Effectiveness of Antidrinking Messages: A Defensive Processing Perspective on Shame and Guilt. https://app.cochat.ai/papers/10.1509/jmkr.47.2.263
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Agrawal, N., & Duhachek, A. (2010). Emotional Compatibility and the Effectiveness of Antidrinking Messages: A Defensive Processing Perspective on Shame and Guilt. Journal of Marketing Research. https://doi.org/10.1509/jmkr.47.2.263

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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.