The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Consumer Response to Negative Publicity: The Moderating Role of Commitment
Abstract
Even though negative information about brands and companies is widely prevalent in the marketplace, except for case studies, there has been no systematic investigation of how consumers process negative information about the brands they like and use. In the three studies in this research, the authors attempt to bridge this gap. The findings of the first and second studies provide a theoretical framework for understanding how consumers process negative information in the marketplace. Commitment of the consumer toward the brand is identified as a moderator of negative information effects. In the third study, the authors use this theoretical framework to derive and test response strategies that companies can use to counter negative publicity for consumers who are high and low in commitment toward the brand.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Baron, R. M., & Kenny, D. A. (1986) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.51.6.1173 · Cited by 91,532
The commitment-trust theory of relationship marketing
Morgan, R. M., & Hunt, S. D. (1994) · Journal of Marketing · doi:10.1177/002224299405800302 · Cited by 22,355
The case for motivated reasoning.
Kunda, Z. (1990) · Psychological Bulletin · doi:10.1037/0033-2909.108.3.480 · Cited by 9,022
Consumers and Their Brands: Developing Relationship Theory in Consumer Research
Fournier, S. (1998) · Journal of Consumer Research · doi:10.1086/209515 · Cited by 7,542
Negativity and extremity biases in impression formation: A review of explanations.
Skowronski, J. J., & Carlston, D. E. (1989) · Psychological Bulletin · doi:10.1037/0033-2909.105.1.131 · Cited by 1,550 · cited 3×
“…about consumer reaction to negative publicity about a known brand, the issue of how consumers process and integrate negative information with positive information has been studied in the impression formation literature in psychology (e.g., Fiske 1980; Klein 1996; Skowronski and Carlston 1989).”
Background
Cited by
Tried and tested: The impact of online hotel reviews on consumer consideration
Vermeulen, I. E., & Seegers, D. (2009) · Tourism Management · doi:10.1016/j.tourman.2008.04.008 · Cited by 1,311
Measuring the impact of positive and negative word of mouth on brand purchase probability
East, R., Hammond, K., & Lomax, W. (2008) · International Journal of Research in Marketing · doi:10.1016/j.ijresmar.2008.04.001 · Cited by 629
When Will a Brand Scandal Spill Over, and how Should Competitors Respond?
Roehm, M. L., & Tybout, A. M. (2006) · Journal of Marketing Research · doi:10.1509/jmkr.43.3.366 · Cited by 380
Hewett, K., Rand, W., Rust, R. T., & van Heerde, H. J. (2016) · Journal of Marketing · doi:10.1509/jm.15.0033 · Cited by 366
A study of factors that contribute to online review helpfulness
Huang, A. H., Chen, K., Yen, D. C., & Tran, T. P. (2015) · Computers in Human Behavior · doi:10.1016/j.chb.2015.01.010 · Cited by 364
Brand Misconduct: Consequences on Consumer–Brand Relationships
Huber, F., Vollhardt, K., Matthes, I., & Vogel, J. (2010) · Journal of Business Research · doi:10.1016/j.jbusres.2009.10.006 · Cited by 198
Eng, T.-Y., & Quaia, G. (2009) · Industrial Marketing Management · doi:10.1016/j.indmarman.2008.01.003 · Cited by 82
Bailey, A. A. (2005) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2004.10.003 · Cited by 42
Moral credentials versus moral credits: Two paths to consumers’ licensing of brand transgressions
Ryoo, Y. (2022) · Journal of Business Research · doi:10.1016/j.jbusres.2022.03.048 · Cited by 38
How to regain green consumer trust after greenwashing: experimental evidence from China
Wang, D., & Walker, T. (2023) · Sustainability · doi:10.3390/su151914436 · Cited by 33
Ambivalent Attitudes in a Communication Process: An Integrated Model
Chang, C. (2012) · Human Communication Research · doi:10.1111/j.1468-2958.2012.01429.x · Cited by 24
Feng, N., Su, Z., Li, D., Zheng, C., & Li, M. (2018) · Information & Management · doi:10.1016/j.im.2018.05.012 · Cited by 22
Metadata
- Authors
- Ahluwalia, R., Burnkrant, R. E., & Unnava, H. R.
- Venue
- Journal of Marketing Research
- Publisher
- SAGE Publications
- Published
- 2000-05
- Volume
- 37
- Issue
- 2
- Pages
- 203-214
- ISSN
- 0022-2437 (ISSN-L)
- 1547-7193
- OpenAlex
- W2039200841
- Cited by
- 1,053
- References
- 5
- FWCI
- 20.04
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
