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Welfare economics of review information: Implications for the online selling platform owner

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  • Zhang, Tao
  • Li, Gang
  • Cheng, T.C.E.
  • Lai, Kin Keung

Abstract

The review system is a core component of the online market as it provides user-generated content to support consumer purchase decisions. We investigate the welfare-based effects of review information features, i.e., the amounts of quality information and match information, and the accuracy of quality information, on the sellers and consumers, and discuss their implications for the online selling platform owner. We find that the amount of review information positively influences social welfare, but quality information and match information play different roles in the process of welfare enhancement. Quality information reduces the sellers’ profits but significantly increases consumer welfare, while match information benefits the sellers more than it hurts the consumers. The inaccuracy of quality information negatively affects the welfare enhancement function of review information. Considering the sellers’ quality information manipulation, we derive the conditions of inaccuracy information controlling and find that a higher manipulation cost coefficient eases the prisoner's dilemma for the sellers and increases consumer welfare. We discuss the implications and also note some counterintuitive insights for review system management.

Suggested Citation

  • Zhang, Tao & Li, Gang & Cheng, T.C.E. & Lai, Kin Keung, 2017. "Welfare economics of review information: Implications for the online selling platform owner," International Journal of Production Economics, Elsevier, vol. 184(C), pages 69-79.
  • Handle: RePEc:eee:proeco:v:184:y:2017:i:c:p:69-79
    DOI: 10.1016/j.ijpe.2016.10.017
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    1. Chunhua Wu & Hai Che & Tat Y. Chan & Xianghua Lu, 2015. "The Economic Value of Online Reviews," Marketing Science, INFORMS, vol. 34(5), pages 739-754, September.
    2. Young Kwark & Jianqing Chen & Srinivasan Raghunathan, 2014. "Online Product Reviews: Implications for Retailers and Competing Manufacturers," Information Systems Research, INFORMS, vol. 25(1), pages 93-110, March.
    3. Chrysanthos Dellarocas, 2006. "Strategic Manipulation of Internet Opinion Forums: Implications for Consumers and Firms," Management Science, INFORMS, vol. 52(10), pages 1577-1593, October.
    4. Monic Sun, 2012. "How Does the Variance of Product Ratings Matter?," Management Science, INFORMS, vol. 58(4), pages 696-707, April.
    5. Duan, Wenjing & Gu, Bin & Whinston, Andrew B., 2008. "The dynamics of online word-of-mouth and product sales—An empirical investigation of the movie industry," Journal of Retailing, Elsevier, vol. 84(2), pages 233-242.
    6. David Godes & Dina Mayzlin & Yubo Chen & Sanjiv Das & Chrysanthos Dellarocas & Bruce Pfeiffer & Barak Libai & Subrata Sen & Mengze Shi & Peeter Verlegh, 2005. "The Firm's Management of Social Interactions," Marketing Letters, Springer, vol. 16(3), pages 415-428, December.
    7. Jonah Berger & Alan T. Sorensen & Scott J. Rasmussen, 2010. "Positive Effects of Negative Publicity: When Negative Reviews Increase Sales," Marketing Science, INFORMS, vol. 29(5), pages 815-827, 09-10.
    8. Yabing Jiang & Hong Guo, 2015. "Design of Consumer Review Systems and Product Pricing," Information Systems Research, INFORMS, vol. 26(4), pages 714-730, December.
    9. Jiang, Yuanchun & Shang, Jennifer & Liu, Yezheng & May, Jerrold, 2015. "Redesigning promotion strategy for e-commerce competitiveness through pricing and recommendation," International Journal of Production Economics, Elsevier, vol. 167(C), pages 257-270.
    10. Floyd, Kristopher & Freling, Ryan & Alhoqail, Saad & Cho, Hyun Young & Freling, Traci, 2014. "How Online Product Reviews Affect Retail Sales: A Meta-analysis," Journal of Retailing, Elsevier, vol. 90(2), pages 217-232.
    11. van Donselaar, K.H. & Peters, J. & de Jong, A. & Broekmeulen, R.A.C.M., 2016. "Analysis and forecasting of demand during promotions for perishable items," International Journal of Production Economics, Elsevier, vol. 172(C), pages 65-75.
    12. Eric T. Anderson & Karsten Hansen & Duncan Simester, 2009. "The Option Value of Returns: Theory and Empirical Evidence," Marketing Science, INFORMS, vol. 28(3), pages 405-423, 05-06.
    13. Xinxin Li & Lorin M. Hitt, 2008. "Self-Selection and Information Role of Online Product Reviews," Information Systems Research, INFORMS, vol. 19(4), pages 456-474, December.
    14. Ramanathan, Ramakrishnan, 2011. "An empirical analysis on the influence of risk on relationships between handling of product returns and customer loyalty in E-commerce," International Journal of Production Economics, Elsevier, vol. 130(2), pages 255-261, April.
    15. Yi Zhao & Sha Yang & Vishal Narayan & Ying Zhao, 2013. "Modeling Consumer Learning from Online Product Reviews," Marketing Science, INFORMS, vol. 32(1), pages 153-169, May.
    16. David Godes & Dina Mayzlin, 2004. "Using Online Conversations to Study Word-of-Mouth Communication," Marketing Science, INFORMS, vol. 23(4), pages 545-560, June.
    17. Yan, Xiaoming & Liu, Ke, 2009. "Optimal control problems for a new product with word-of-mouth," International Journal of Production Economics, Elsevier, vol. 119(2), pages 402-414, June.
    18. Dina Mayzlin, 2006. "Promotional Chat on the Internet," Marketing Science, INFORMS, vol. 25(2), pages 155-163, 03-04.
    19. Dmitri Kuksov & Ying Xie, 2010. "Pricing, Frills, and Customer Ratings," Marketing Science, INFORMS, vol. 29(5), pages 925-943, 09-10.
    20. Gunasekaran, A. & Marri, H. B. & McGaughey, R. E. & Nebhwani, M. D., 2002. "E-commerce and its impact on operations management," International Journal of Production Economics, Elsevier, vol. 75(1-2), pages 185-197, January.
    21. Shyam Gopinath & Jacquelyn S. Thomas & Lakshman Krishnamurthi, 2014. "Investigating the Relationship Between the Content of Online Word of Mouth, Advertising, and Brand Performance," Marketing Science, INFORMS, vol. 33(2), pages 241-258, March.
    22. Man Yu & Laurens Debo & Roman Kapuscinski, 2016. "Strategic Waiting for Consumer-Generated Quality Information: Dynamic Pricing of New Experience Goods," Management Science, INFORMS, vol. 62(2), pages 410-435, February.
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    5. Zheng, Lili, 2021. "The classification of online consumer reviews: A systematic literature review and integrative framework," Journal of Business Research, Elsevier, vol. 135(C), pages 226-251.
    6. Shao, Xiao-Feng, 2017. "Free or calculated shipping: Impact of delivery cost on supply chains moving to online retailing," International Journal of Production Economics, Elsevier, vol. 191(C), pages 267-277.
    7. Tran, Lobel Trong Thuy, 2021. "Managing the effectiveness of e-commerce platforms in a pandemic," Journal of Retailing and Consumer Services, Elsevier, vol. 58(C).
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    9. Qiao, Haike & Su, Qin, 2021. "Distribution channel and licensing strategy choice considering consumer online reviews in a closed-loop supply chain," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 151(C).
    10. Yong Liu & Wen‐xue Gan & Wen‐wen Ren, 2021. "Influence mechanism of online consumer comments on e‐retailer," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 42(5), pages 1132-1145, July.
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    12. Zhang, Tao & Li, Gang & Lai, Kin Keung & Leung, John W.K., 2018. "Information disclosure strategies for the intermediary and competitive sellers," European Journal of Operational Research, Elsevier, vol. 271(3), pages 1156-1173.
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