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A reputation management mechanism that incorporates accountability in online ratings

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  • Subhasis Thakur

    (National University of Ireland)

Abstract

Online reputation has a strong impact on the success of a seller in an e-marketplace. Also, buyers use it to choose an appropriate seller among a set of alternatives. The standard practice of determining the reputation of a seller is the aggregation of the feedbacks or the ratings reported by its buyers. Such a model of reputation formulation is vulnerable to misleading and unfair feedbacks. A seller may collude with a set of buyers to report good feedbacks while the quality of its product is poor. Also the buyers can report unfair feedbacks being irrational, malicious or competitors. A robust reputation management mechanism is the one which can not be manipulated by these unfair feedbacks. The existing reputation management models are either reactive or proactive. The reactive solutions intend to identify the unfair feedbacks and the proactive solutions propose incentive to the buyers to encourage them to report fair feedbacks. In this paper, we propose an incentive system that encourages the buyers to report fair feedbacks. We associate a buyer’s reputation with a seller’s reputation if the buyer has expressed its feedback about the seller. If the reputation of the seller decreases then the reputation of all buyers who had endorsed it (provided positive feedbacks) also decreases and vice versa. This means a buyer risks its own reputation by providing the feedback about a seller. In this paper, we show that such a mechanism is incentive compatible, i.e., it encourages the buyers to provide fair feedbacks. Using analytical and experimental analysis, we show the correctness of this reputation management system.

Suggested Citation

  • Subhasis Thakur, 2019. "A reputation management mechanism that incorporates accountability in online ratings," Electronic Commerce Research, Springer, vol. 19(1), pages 23-57, March.
    • Handle: RePEc:spr:elcore:v:19:y:2019:i:1:d:10.1007_s10660-017-9280-9
    DOI: 10.1007/s10660-017-9280-9
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    References listed on IDEAS

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    1. Dellarocas, Chrysanthos, 2003. "The Digitization of Word-of-mouth: Promise and Challenges of Online Feedback Mechanisms," Working papers 4296-03, Massachusetts Institute of Technology (MIT), Sloan School of Management.
    2. Chrysanthos Dellarocas, 2003. "The Digitization of Word of Mouth: Promise and Challenges of Online Feedback Mechanisms," Management Science, INFORMS, vol. 49(10), pages 1407-1424, October.
    3. Daniel Houser & John Wooders, 2006. "Reputation in Auctions: Theory, and Evidence from eBay," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 15(2), pages 353-369, June.
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    Cited by:

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    2. Xu, Man & Tang, Wansheng & Zhao, Ruiqing, 2023. "Should reputable e-retailers undertake service activities along with sales?," Journal of Retailing and Consumer Services, Elsevier, vol. 74(C).
    3. Guo Li & Hong Zheng & Mengqi Liu, 2020. "Reselling or drop shipping: Strategic analysis of E-commerce dual-channel structures," Electronic Commerce Research, Springer, vol. 20(3), pages 475-508, September.
    4. Cuixia Jiang & Jun Zhu & Qifa Xu, 2022. "Dissecting click farming on the Taobao platform in China via PU learning and weighted logistic regression," Electronic Commerce Research, Springer, vol. 22(1), pages 157-176, March.
    5. Xueke Du & Rui Dong & Wenli Li & Yibo Jia & Lirong Chen, 2019. "Online Reviews Matter: How Can Platforms Benefit from Online Reviews?," Sustainability, MDPI, vol. 11(22), pages 1-20, November.
    6. Eugenia Y. Huang & Shu-Chiung Lin & I-Ting Hsieh, 2023. "Online marketplace sellers’ influence on rating scores and comment orientation," Electronic Commerce Research, Springer, vol. 23(2), pages 1241-1270, June.

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    Keywords

    Reputation; e-marketplace; Trust;
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