The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
Online Product Reviews: Implications for Retailers and Competing Manufacturers
Open accessAbstract
This paper studies the effect of online product reviews on different players in a channel structure. We consider a retailer selling two substitutable products produced by different manufacturers, and the products differ in both their qualities and fits to consumers' needs. Online product reviews provide additional information for consumers to mitigate the uncertainty about the quality of a product and about its fit to consumers' needs. We show that the effect of reviews on the upstream competition between the manufacturers is critical in understanding which firms gain and which firms lose. The upstream competition is affected in fundamentally different ways by quality information and fit information, and each information type has different implications for the retailer and manufacturers. Quality information homogenizes consumers' perceived utility differences between the two products and increases the upstream competition, which benefits the retailer but hurts the manufacturers. Fit information heterogenizes consumers' estimated fits to the products and softens the upstream competition, which hurts the retailer but benefits the manufacturers. Furthermore, reviews may also alter the nature of upstream competition from one in which consumers' own assessment on the quality dimension plays a dominant role in consumers' comparative evaluation of products to one in which fit dimension plays a dominant role. If manufacturers do not respond strategically to reviews and keep the same wholesale prices regardless of reviews (i.e., the upstream competition is assumed to be unaffected by reviews), then, we show that reviews never hurt the retailer and the manufacturer with favorable reviews, and never benefit the manufacturer with unfavorable reviews, a finding that demonstrates why reviews' effect on upstream competition is critical for firms in online marketplaces.
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Cites
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
Information and Consumer Behavior
Nelson, P. (1970) · Journal of Political Economy · doi:10.1086/259630 · Cited by 5,758
Bates, J. M., & Granger, C. W. J. (1969) · Journal of the Operational Research Society · doi:10.1057/jors.1969.103 · Cited by 3,201
Zhu, F., & Zhang, X. (. (2010) · Journal of Marketing · doi:10.1509/jm.74.2.133 · Cited by 2,363
Online Consumer Review: Word-of-Mouth as a New Element of Marketing Communication Mix
Chen, Y., & Xie, J. (2008) · Management Science · doi:10.1287/mnsc.1070.0810 · Cited by 1,810
Forman, C., Ghose, A., & Wiesenfeld, B. (2008) · Information Systems Research · doi:10.1287/isre.1080.0193 · Cited by 1,695
Word of Mouth for Movies: Its Dynamics and Impact on Box Office Revenue
Liu, Y. (2006) · Journal of Marketing · doi:10.1509/jmkg.70.3.74 · Cited by 1,588
Word of Mouth for Movies: Its Dynamics and Impact on Box Office Revenue
Liu, Y. (2006) · Journal of Marketing · doi:10.1509/jmkg.70.3.074 · Cited by 1,329
DUAN, W., GU, B., & WHINSTON, A. (2008) · Journal of Retailing · doi:10.1016/j.jretai.2008.04.005 · Cited by 1,223
Self-Selection and Information Role of Online Product Reviews
Li, X., & Hitt, L. M. (2008) · Information Systems Research · doi:10.1287/isre.1070.0154 · Cited by 1,125
Research works printed but not linked: 6
- Chen P-Y, 2004, Proc. 25th Internat. Conf. Inform. Systems (ICIS)
- Garvin DA, 1984, Sloan Management Rev., 26, S25
- Hong Y, 2012, Proc. 33th Internat. Conf. Inform. Systems (ICIS)
- Jiang Y, Guo H (2013) Design of consumer review systems and product pricing. Working paper, Florida Gulf Coast University. · doi:10.2139/ssrn.2164368
- Jiang B-J, 2008, Proc. 29th Internat. Conf. Inform. Systems (ICIS)
- Sutton J, 1986, Amer. Econom. Rev., 76, 393
Cited by
Product fit uncertainty in online markets: nature, effects, and antecedents
Hong, Y. (., & Pavlou, P. A. (2014) · Information Systems Research · doi:10.1287/isre.2014.0520 · Cited by 453
Kwark, Y., Chen, J., & Raghunathan, S. (2017) · MIS Quarterly · doi:10.25300/misq/2017/41.3.05 · Cited by 350
Self-citationOnline Product Reviews-Triggered Dynamic Pricing: Theory and Evidence
Feng, J., Li, X., & Zhang, X. (. (2019) · Information Systems Research · doi:10.1287/isre.2019.0852 · Cited by 117
Liu, Y., & Tang, X. (2018) · Information Technology & People · doi:10.1108/itp-10-2016-0242 · Cited by 111
Do fit opinions matter? The impact of fit context on online product returns
Wang, Y., Ramachandran, V., & Liu Sheng, O. R. (2021) · Information Systems Research · doi:10.1287/isre.2020.0965 · Cited by 72
Metadata
- Authors
- Kwark, Y., Chen, J., & Raghunathan, S.
- Venue
- Information Systems Research
- Published
- 2014-03
- Volume
- 25
- Issue
- 1
- Pages
- 93-110
- ISSN
- 1047-7047 (ISSN-L)
- 1526-5536
- OpenAlex
- W2167181457
- Cited by
- 398
- References
- 35
- FWCI
- 11.52
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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