Skip to main content
SpringerLink
Log in

Larger number of reviews or higher rating? The firm’s pricing and quality disclosure strategies on the online platform

  • Published:
Information Technology and Management Aims and scope Submit manuscript

Abstract

Considering two common dimensions of online reviews, number of reviews and rating, this paper investigates the impacts of online reviews on the firm’s pricing and quality disclosure strategies when selling the product through an online platform’s agency or resell scheme. By setting up a two-period model, we find that the first-period retail price decreases in the presence of online reviews and the degree of the first-period retail price cut is greater when consumers assign a higher weight to the number of reviews. However, the second-period retail price does not necessarily increase in the presence of reviews, depending on realized number of reviews and rating. The firm chooses to disclose its quality when the quality is above a threshold under two selling schemes, and the quality disclosure threshold decreases with the weight that consumers assign to the rating under the resell scheme, whereas it increases with the weight to the rating under the agency scheme. The existence of online reviews may stimulate or inhibit the firm’s incentive to disclose product quality, depending on the consumer’s weight on the two dimensions and the selling scheme. Finally, the firm and the platform come to an agreement on the agency scheme when the commission rate is relatively low, leading to a win–win scenario, and the win–win scenario is more likely associated with less quality information disclosure. We also consider the endogenous correlation between the rating and true product quality in the extension to check the robustness of the results.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Notes

  1. https://report.iresearch.cn/report/202010/3672.shtml

  2. OKEO-TEX® is the most authoritative and influential textile eco-label in the world.

  3. https://www.hbrchina.org/2015-04-07/2870.html

  4. https://www.sohu.com/a/443456019_120288442

  5. https://www.sohu.com/a/430811478_114819

  6. Empirical research [3, 4] indicates that the number of reviews and the rating are the most significant factors in the firm’s sales and consumer’s purchase decisions. Based on these empirical results, this paper considers that online review information is consisted of two dimensions of information, i.e., the number of reviews and the rating. Besides, the number of reviews is proportional to sales volume. To simplify the analysis, this paper does not distinguish between the number of reviews and sales volume.

  7. https://www.qianzhan.com/analyst/detail/329/211126-12b325a1.html

  8. This work focuses on the market that is not fully covered to guarantee higher quality leads to higher demand, given all else equal. The demand of regular consumers is \(\frac{q-ta}{2t}\). Thus, to ensure not all regular consumers will purchase the product under all possible scenarios, it needs that \(max\left\{\frac{q-ta}{2t}\right\}\le 1\), i.e., \(\frac{1}{2+a}\le t\).

  9. To focus on the impacts of online reviews on the firm’s quality disclosure strategy, we normalize the disclosure cost to \(0\) as Guan et al. [15], since the disclosure cost will not qualitatively affect our results.

  10. The “informativeness of the rating” refers to the rating that can correctly reveal the true quality of the product with a certain probability. The higher the informativeness of the rating is, the higher the probability of correctly revealing the product quality.

  11. According to Lemma 1, we have that \({\pi }_{F1}^{B*}=\frac{\left(1-\alpha \right){\left(1+2ta\right)}^{2}}{16t\left(1+bt\right)}\) and \({\pi }_{F1}^{H*}=\frac{\left(1-\alpha \right)\left[4-{m}^{2}{\left(1-\beta \right)}^{2}\right]{\left(1+2ta\right)}^{2}}{64t\left(1+bt\right)}.\)

  12. According to Lemma 1, it can be derived that \({\pi }_{F2}^{B*}=\frac{\left(1-\alpha \right)m{\left(1+2ta\right)}^{2}}{16t\left(1+bt\right)}\) and \(E\left({\pi }_{F2}^{H*}\right)=\left(1-\alpha \right)m\left[\frac{{\left(1+2ta\right)}^{2}}{16t\left(1+bt\right)}+\frac{{\beta }^{2}+4{t}^{2}{\varepsilon }^{2}{\left(1-\beta \right)}^{2}}{48t\left(1+bt\right)}\right].\)

  13. Paula’s Choice is an American skincare brand.

References

  1. Kim Y, Krishnan R (2015) On product-level uncertainty and online purchase behavior: an empirical analysis. Manag Sci 61(10):2449–2467

    Article  Google Scholar 

  2. TESTEX Official Site. These companies trust TESTEX for OEKO-TEX® certification. https://www.testex.com/en/oeko-tex. Accessed 29 June 2022

  3. Chevalier JA, Mayzlin D (2006) The effect of word of mouth on sales: online book reviews. J Mark Res 43(3):345–354

    Article  Google Scholar 

  4. Zhu F, Zhang XQ (2010) Impact of online consumer reviews on sales: the moderating role of product and consumer characteristics. J Mark 74(2):133–148

    Article  Google Scholar 

  5. Kuksov D, Xie Y (2010) Pricing, frills and customer ratings. Mark Sci 29(5):925–943

    Article  Google Scholar 

  6. Li X, Hitt LM, Zhang ZJ (2011) Product reviews and competition in markets for repeat purchase products. J Manag Inf Syst 27(4):9–41

    Article  Google Scholar 

  7. Von Helversen B, Abramczuk K, Kopec W, Nielek R (2018) Influence of consumer reviews on online purchasing decisions in older and younger adults. Decis Support Syst 113:1–10

    Article  Google Scholar 

  8. Chen N, Li A, Talluri K (2021) Reviews and self-selection bias with operational implications. Manag Sci 67(12):7472–7492

    Article  Google Scholar 

  9. Caminal R, Vives X (1996) Why market shares matter: an information-based theory. Rand J Econ 27(2):221–239

    Article  Google Scholar 

  10. Simonsohn U, Dan A (2008) When rational sellers face non-rational buyers: evidence from herding on eBay. Manag Sci 54(9):1624–1637

    Article  Google Scholar 

  11. Liu Y, Feng J, Liao X (2017) When online reviews meet sales volume information: Is more or accurate information always better? Inf Syst Res 28(4):723–743

    Article  Google Scholar 

  12. Zhen X, Cai G, Song R, Jang S (2019) The effects of herding and word of mouth in a two-period advertising signaling model. Eur J Oper Res 275(1):361–373

    Article  Google Scholar 

  13. Bai X, Marsden JR, Ross WTJ, Wang G (2017) How e-WOM and local competition drive local retailers’ decisions about daily deal offerings. Decis Support Syst 101:82–94

    Article  Google Scholar 

  14. Zhao J, Qiu J, Zhou YW, Hu XJ, Yang AF (2020) Quality disclosure in the presence of strategic consumers. J Retail Consum Serv 55:102084

    Article  Google Scholar 

  15. Guan X, Wang Y, Yi Z, Chen YJ (2020) Inducing consumer online reviews via disclosure. Prod Oper Manag 29(9):1956–1971

    Article  Google Scholar 

  16. Grossman SJ, Hart OD (1980) Disclosure laws and takeover bids. J Finance 35(2):323–334

    Article  Google Scholar 

  17. Grossman SJ (1981) The informational role of warranties and private disclosure about product quality. J Law Econ 24(3):461–483

    Article  Google Scholar 

  18. Milgrom PR (1981) Good news and bad news: representation theorems and applications. Bell J Econ 12(2):380–391

    Article  Google Scholar 

  19. Jovanovic B (1982) Truthful disclosure of information. Bell J Econ 13(1):36–44

    Article  Google Scholar 

  20. Board O (2009) Competition and disclosure. J Ind Econ 57(1):197–213

    Article  Google Scholar 

  21. Sun M (2011) Disclosing multiple product attributes. J Econ Manag Strategy 20(1):195–224

    Article  Google Scholar 

  22. Zhu H, Yu Y, Ray S (2021) Quality disclosure strategy under customer learning opportunities. Prod Oper Manag 30(4):1136–1153

    Article  Google Scholar 

  23. Kronrod A, Danziger S (2013) “Wii will rock you!” The use and effect of figurative language in consumer reviews of hedonic and utilitarian consumption. J Consum Res 40(4):726–739

    Article  Google Scholar 

  24. Chevalier JA, Dover Y, Mayzlin D (2018) Channels of impact: user reviews when quality is dynamic and managers respond. Mark Sci 37(5):688–709

    Article  Google Scholar 

  25. Kwark Y, Chen J, Raghunathan S (2018) User-generated content and competing firms’ product design. Manage Sci 64(10):4608–4628

    Article  Google Scholar 

  26. Li X, Hitt LM (2010) Price effects in online product reviews: an analytical model and empirical analysis. MIS Q 34(4):809–832

    Article  Google Scholar 

  27. Duan WJ, Gu B, Whinston AB (2008) Do online reviews matter? An empirical investigation of panel data. Decis Support Syst 45(4):1007–1016

    Article  Google Scholar 

  28. Liu Y (2006) Word-of-mouth for movies: its dynamics and impact on box office revenue. J Mark 70(3):74–89

    Article  Google Scholar 

  29. Sun M (2012) How does the variance of product ratings matter? Manag Sci 58(4):696–707

    Article  Google Scholar 

  30. Fainmesser IP, Lauga DO, Ofek E (2021) Ratings, reviews, and the marketing of new products. Manag Sci 67(11):7023–7045

    Article  Google Scholar 

  31. Abhishek V, Jerath K, Zhang ZJ (2016) Agency selling or reselling? Channel structures in electronic retailing. Manag Sci 62(8):2259–2280

    Article  Google Scholar 

  32. Tian L, Vakharia AJ, Tan YR, Xu Y (2018) Marketplace, reseller, or hybrid: strategic analysis of an emerging e-commerce model. Prod Oper Manag 27(8):1595–1610

    Article  Google Scholar 

  33. Kwark Y, Chen J, Raghunathan S (2017) Platform or wholesale? A strategic tool for online retailers to benefit from third-party information. MIS Q 41(3):763–785

    Article  Google Scholar 

  34. Yi Z, Wang Y, Liu Y, Chen YJ (2018) The impact of consumer fairness seeking on distribution channel selection: direct selling vs. agent selling. Prod Oper Manag 27(6):1148–1167

    Article  Google Scholar 

  35. Danziger C (2022) How Amazon used big data to rule E-Commerce. https://insidebigdata.com/2019/11/30/how-amazon-used-big-data-to-rule-e-commerce/. Accessed 23 May 2022

  36. Bates JM, Granger CWJ (1969) The combination of forecasts. Oper Res Q 20(4):451–468

    Article  Google Scholar 

  37. Huang L, Huang Z, Liu B (2022) Interacting with strategic waiting for store brand: online selling format selection. J Retail Consum Serv 67:102987

    Article  Google Scholar 

  38. Nestlé Official Site. Nestlé expands blockchain to Zoégas coffee brand. (2020-04-06). https://www.nestle.com/media/news/nestle-blockchain-zoegas-coffee-brand. Accessed 23 May 2022

  39. Daxue Consulting. TMall heybox app: unwrapping the secret behind successful new product launches in China. (2021-07-02). https://daxueconsulting.com/tmall-heybox/. Accessed 23 May 2022

Download references

Acknowledgements

He Huang was supported by the National Natural Science Foundation of China (NSFC) [Grants 72225008, 71871032] and the Fundamental Research Funds for the Central Universities, China (Projects No. 2021CDJSKZD06 and 2021CDJSKCG18). Hongyan Xu was supported by the National Natural Science Foundation of China (NSFC) [Grants 72272020, 71972019] and the Fundamental Research Funds for the Central Universities, China (Project No. 2021CDJSKCG35). The authors thank the editor and all anonymous reviewers for valuable and constructive suggestions, which have greatly improved the quality of the paper

Author information

Authors and Affiliations

  1. School of Economics and Business Administration, Chongqing University, Chongqing, 400044, China

    He Huang, Yufeng Yang & Hongyan Xu

Authors
  1. He Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yufeng Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hongyan Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hongyan Xu.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file 1

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Huang, H., Yang, Y. & Xu, H. Larger number of reviews or higher rating? The firm’s pricing and quality disclosure strategies on the online platform. Inf Technol Manag (2023). https://doi.org/10.1007/s10799-023-00397-9

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10799-023-00397-9

Keywords

Search

Navigation