This study explores optimal pricing strategies and online review provision policies for firms selling substitutable products in markets with social learning. Using a two-period model, we compare two common review policies: displaying reviews separately for each version (the separated policy) and aggregating reviews across all versions (the aggregated policy). We find that online reviews generally induce firms to adopt penetration pricing strategies. The optimal review policy depends crucially on two factors: product display quality and consumer learning speed. The aggregated policy is more profitable for low-quality products with slow learning, as it mitigates quality assessment noise through information aggregation. Conversely, the separated policy outperforms for high-quality products with rapid learning, as it provides precise quality signals. Within optimized thresholds, each policy achieves a triple-win: increasing firm profits, consumer welfare, and social surplus.
HUANG Wei
. Online review influence on multi-product pricing and review provision policies[J]. Journal of University of Chinese Academy of Sciences, 0
: 54
-54
.
DOI: 10.7523/j.ucas. 2025.056
[1] Paget S. Local consumer review survey2024: Trends, behaviors, and platforms explored[EB/OL]. BrightLocal,(2024)[2025-01-15]. https://www.brightlocal.com/research/local-consumer-review-survey.
[2] Varga M, Albuquerque P.The impact of negative reviews on online search and purchase decisions[J]. Journal of Marketing Research, 2024, 61(5): 803-820. DOI: 10.1177/00222437231190874.
[3] Luca M.Reviews, reputation, and revenue: The case of Yelp.Com[J]. SSRN Electronic Journal, 2011. DOI: 10.2139/ssrn.1928601.
[4] Zhu F, Zhang X M.Impact of online consumer reviews on sales: The moderating role of product and consumer characteristics[J]. Journal of Marketing, 2010, 74(2): 133-148. DOI: 10.1509/jm.74.2.133.
[5] Chevalier J A, Mayzlin D.The effect of word of mouth on sales: Online book reviews[J]. Journal of Marketing Research, 2006, 43(3):345-354. DOI: 10.1509/jmkr.43.3.345.
[6] Sun M.How does the variance of product ratings matter?[J]. Management Science, 2011, 58(4):696-707. DOI: 10.1287/mnsc.1110.1458.
[7] Crapis D, Ifrach B, Maglaras C, et al.Monopoly pricing in the presence of social learning[J]. Management Science, 2016, 63(11): 3586-3608. DOI: 10.1287/mnsc.2016.2526.
[8] Ifrach B, Maglaras C, Scarsini M, et al.Bayesian social learning from consumer reviews[J]. Operations Research, 2019, 67(5): 1209-1221. DOI: 10.1287/opre.2019.1861.
[9] Yu M, Debo L, Kapuscinski R.Strategic waiting for consumer-generated quality information: Dynamic pricing of new experience goods[J]. Management Science, 2015, 62(2): 410-435. DOI: 10.1287/mnsc.2014.2134.
[10] Papanastasiou Y, Savva N.Dynamic pricing in the presence of social learning and strategic consumers[J]. Management Science, 2016, 63(4): 919-939. DOI: 10.1287/mnsc.2015.2378.
[11] Yan X, Han X H.Optimal pricing and remanufacturing entry strategies of manufacturers in the presence of online reviews[J]. Annals of Operations Research, 2022, 316(1): 59-92. DOI: 10.1007/s10479-021-04030-2.
[12] Shin D, Vaccari S, Zeevi A.Dynamic pricing with online reviews[J]. Management Science, 2022, 69(2): 824-845. DOI: 10.1287/mnsc.2022.4387.
[13] Shin D, Zeevi A.Product quality and information sharing in the presence of reviews[J]. Management Science, 2023, 70(3): 1428-1447. DOI: 10.1287/mnsc.2023.4746.
[14] Zhao Y, Yang S, Narayan V, et al.Modeling consumer learning from online product reviews[J]. Marketing Science, 2012, 32(1): 153-169. DOI: 10.1287/mksc.1120.0755.
[15] 冯娇, 姚忠. 基于社会学习理论的在线评论信息对购买决策的影响研究[J]. 中国管理科学, 2016, 24(9): 106-114. DOI: 10.16381/j.cnki.issn1003-207x.2016.09.013.
[16] Ke W, Ryzin G V. Coordinating vertical partnerships for horizontally differentiated products[J]. Columbia Business School Working Papers, (2011)[2025-01-15]. https://business.columbia.edu/faculty/research/coordinating-vertical-partnerships-horizontally-differentiated-products.
[17] Kwark Y, Chen J Q, Raghunathan S.Online product reviews: Implications for retailers and competing manufacturers[J]. Information Systems Research, 2014, 25(1): 93-110. DOI: 10.1287/isre.2013.0511.
[18] Hu M, Milner J, Wu J H.Liking and following and the newsvendor: Operations and marketing policies under social influence[J]. Management Science, 2015, 62(3): 867-879. DOI: 10.1287/mnsc.2015.2160.
[19] Shang W X, Ha A Y, Tong S L.Information sharing in a supply chain with a common retailer[J]. Management Science, 2015, 62(1): 245-263. DOI: 10.1287/mnsc.2014.2127.
[20] Liu Y, Feng J, Liao X W.When online reviews meet sales volume information: Is more or accurate information always better?[J]. Information Systems Research, 2017, 28(4): 723-743. DOI: 10.1287/isre.2017.0715.
[21] Jiang Y B, Guo H.Design of consumer review systems and product pricing[J]. Information Systems Research, 2015, 26(4): 714-730. DOI: 10.1287/isre.2015.0594.
[22] Besbes O, Scarsini M.On information distortions in online ratings[J]. Operations Research, 2018, 66(3): 597-610. DOI: 10.1287/opre.2017.1676.
[23] Xiao S H, Chen Y J, Tang C S.Customer review provision policies with heterogeneous cluster preferences[J]. Management Science, 2021, 68(7): 5025-5048. DOI: 10.1287/mnsc.2021.4138.
[24] Lembregts C, Schepers J, De Keyser A.Is it as bad as it looks? judgments of quantitative scores depend on their presentation format[J]. Journal of Marketing Research, 2024, 61(5): 937-954. DOI: 10.1177/00222437231193343.
