The DOI matches its Crossref record; 48 of the 48 references it cites were found in a registry or an index.
Research Note—When Do Consumers Value Positive vs. Negative Reviews? An Empirical Investigation of Confirmation Bias in Online Word of Mouth
Abstract
In the online word-of-mouth literature, research has consistently shown that negative reviews have a greater impact on product sales than positive reviews. Although this negativity effect is well documented at the product level, there is less consensus on whether negative or positive reviews are perceived to be more helpful by consumers. A limited number of studies document a higher perceived helpfulness for negative reviews under certain conditions, but accumulating empirical evidence suggests the opposite. To reconcile these contradictory findings, we propose that consumers can form initial beliefs about a product on the basis of the product’s summary rating statistics (such as the average and dispersion of the product’s ratings) and that these initial beliefs play a vital role in their subsequent evaluation of individual reviews. Using a unique panel data set collected from Apple’s App Store, we empirically demonstrate confirmation bias—that consumers have a tendency to perceive reviews that confirm (versus disconfirm) their initial beliefs as more helpful, and that this tendency is moderated by their confidence in their initial beliefs. Furthermore, we show that confirmation bias can lead to greater perceived helpfulness for positive reviews (positivity effect) when the average product rating is high, and for negative reviews (negativity effect) when the average product rating is low. Thus, the mixed findings in the literature can be a consequence of confirmation bias. This paper is among the first to incorporate the important role of consumers’ initial beliefs and confidence in such beliefs (a fundamental dimension of metacognition) into their evaluation of online reviews, and our findings have significant implications for researchers, retailers, and review websites.
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(2001) · RePEc: Research Papers in Economics · Cited by 27,577
A Theory of Cognitive Dissonance
Festinger, L. (1957) · doi:10.1515/9781503620766 · Cited by 24,241
A Theory of Cognitive Dissonance
(2017) · Macat Library eBooks · doi:10.4324/9781912282432 · Cited by 18,122
Baumeister, R. F., Bratslavsky, E., Finkenauer, C., & Vohs, K. D. (2001) · Review of General Psychology · doi:10.1037/1089-2680.5.4.323 · Cited by 7,678
Confirmation Bias: A Ubiquitous Phenomenon in Many Guises
Nickerson, R. S. (1998) · Review of General Psychology · doi:10.1037/1089-2680.2.2.175 · Cited by 7,267
The Effect of Word of Mouth on Sales: Online Book Reviews
Chevalier, J. A., & Mayzlin, D. (2006) · Journal of Marketing Research · doi:10.1509/jmkr.43.3.345 · Cited by 6,003
Social psychology: handbook of basic principles
(2008) · Choice Reviews Online · doi:10.5860/choice.45-3496 · Cited by 5,473
Causal Inference without Balance Checking: Coarsened Exact Matching
Iacus, S. M., King, G., & Porro, G. (2012) · Political Analysis · doi:10.1093/pan/mpr013 · Cited by 3,563
What Makes a Helpful Online Review? A Study of Customer Reviews on Amazon.Com1
Mudambi, S. M., & Schuff, D. (2010) · MIS Quarterly · doi:10.2307/20721420 · Cited by 2,490
Parducci, A., & Helson, H. (1965) · The American Journal of Psychology · doi:10.2307/1421107 · Cited by 2,475
Cited by
Ho, H., & Ito, K. (2019) · European Journal of Marketing · doi:10.1108/ejm-11-2017-0809 · Cited by 59
Ketron, S. (2017) · Journal of Business Research · doi:10.1016/j.jbusres.2017.08.008 · Cited by 37
Metadata
- Authors
- Yin, D., Mitra, S., & Zhang, H.
- Venue
- Information Systems Research
- Published
- 2016-03
- Volume
- 27
- Issue
- 1
- Pages
- 131-144
- ISSN
- 1047-7047 (ISSN-L)
- 1526-5536
- OpenAlex
- W2278526461
- Cited by
- 365
- References
- 48
- FWCI
- 67.64
- Type
- journal article
- Language
- en
- Open access
- closed
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- Yin, D., Mitra, S., & Zhang, H. (2016). Research Note—When Do Consumers Value Positive vs. Negative Reviews? An Empirical Investigation of Confirmation Bias in Online Word of Mouth. Information Systems Research. https://doi.org/10.1287/isre.2015.0617
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