The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Why are you telling me this? An examination into negative consumer reviews on the Web
Abstract
Although word-of-mouth (WOM) is recognized as a powerful force in persuasion, we know little about the new communication phenomenon known as e-WOM. One of the main forms of e-WOM is the product reviews consumers post on different Web sites, and how this form of e-WOM stands up to this claim is yet unknown. For example, do consumers trust the accuracy of these reviews posted by anonymous reviewers, and, do readers trust negative and positive reviews equally? Past research has shown that people tend to weight negative information more than positive information during evaluation. Through an observation study and two laboratory experiments, we investigate the existence of this negativity effect in e-WOM consumer reviews for utilitarian versus hedonic products, and investigate the influence of the reader's attributions regarding the reviewer's motivations on this. Both types of studies show that product type moderates the effect of review valence, and readers exhibit a negativity bias for utilitarian product reviews only. Furthermore, the lab studies show that the reader's attributions about the motivations of the reviewer mediate the effect of this moderation on their attitude about the review. We find that compared with the utilitarian case, readers of negative hedonic product reviews are more likely to attribute the negative opinions expressed, to the reviewer's internal (or non-product related) reasons; and therefore are less likely to find the negative reviews useful. However, in the utilitarian case, readers’ are more likely to attribute the reviewer's negative opinions to external (or product related) motivations, and therefore find negative reviews more useful than positive reviews on average.
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
Asymptotic Confidence Intervals for Indirect Effects in Structural Equation Models
Sobel, M. E. (1982) · Sociological Methodology · doi:10.2307/270723 · Cited by 12,810
The case for motivated reasoning.
Kunda, Z. (1990) · Psychological Bulletin · doi:10.1037/0033-2909.108.3.480 · Cited by 9,022
Attitudinal Effects of Mere Exposure
Zajonc, R. B. (1968) · Journal of Personality and Social Psychology · doi:10.1037/h0025848 · Cited by 6,745
Hennig-Thurau, T., Gwinner, K. P., Walsh, G., & Gremler, D. D. (2004) · Journal of Interactive Marketing · doi:10.1002/dir.10073 · Cited by 5,916
The processes of causal attribution.
Kelley, H. H. (1973) · American Psychologist · doi:10.1037/h0034225 · Cited by 4,025 · cited 2×
“Heider (1958) and subsequently others (Feldman, 1966; Jones & David, 1965; Jones & Nisbett, 1972; Kanouse & Hanson, 1972; Kelley, 1973; Zajonc, 1968) point out a number of distortions that affect this attribution process.”
BackgroundFrom acts to dispositions the attribution process in person perception
Jones, E. E., & Davis, K. E. (1965) · Advances in Experimental Social Psychology · doi:10.1016/s0065-2601(08)60107-0 · Cited by 3,424
Adaptive strategy selection in decision making.
Payne, J. W., Bettman, J. R., & Johnson, E. J. (1988) · Journal of Experimental Psychology: Learning, Memory, and Cognition · doi:10.1037/0278-7393.14.3.534 · Cited by 1,955
Information Load and Consumer Decision Making
Malhotra, N. K. (1982) · Journal of Consumer Research · doi:10.1086/208882 · Cited by 778
Cited by
The impact of electronic word-of-mouth communication: A literature analysis and integrative model
Cheung, C. M. K., & Thadani, D. R. (2012) · Decision Support Systems · doi:10.1016/j.dss.2012.06.008 · Cited by 2,005
The impact of online reviews on hotel booking intentions and perception of trust
Sparks, B. A., & Browning, V. (2011) · Tourism Management · doi:10.1016/j.tourman.2010.12.011 · Cited by 1,688
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
Lamberton, C., & Stephen, A. T. (2016) · Journal of Marketing · doi:10.1509/jm.15.0415 · Cited by 1,127
How Does Brand-related User-generated Content Differ across YouTube, Facebook, and Twitter?
Smith, A. N., Fischer, E., & Yongjian, C. (2012) · Journal of Interactive Marketing · doi:10.1016/j.intmar.2012.01.002 · Cited by 1,044
Metadata
- Authors
- Sen, S., & Lerman, D.
- Venue
- Journal of Interactive Marketing
- Publisher
- SAGE Publications
- Published
- 2007-11
- Volume
- 21
- Issue
- 4
- Pages
- 76-94
- ISSN
- 1094-9968 (ISSN-L)
- 1520-6653
- OpenAlex
- W2069927273
- Cited by
- 1,245
- References
- 9
- FWCI
- 54.19
- 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.