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A reputation management mechanism that incorporates accountability in online ratings

Author:

Subhasis ThakurAuthors Info & Claims

Electronic Commerce Research, Volume 19, Issue 1

Pages 23 - 57

https://doi.org/10.1007/s10660-017-9280-9

Published: 01 March 2019 Publication History

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.

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Cited By

Jiang CZhu JXu Q(2022)Dissecting click farming on the Taobao platform in China via PU learning and weighted logistic regressionElectronic Commerce Research10.1007/s10660-020-09418-z22:1(157-176)Online publication date: 1-Mar-2022
https://dl.acm.org/doi/10.1007/s10660-020-09418-z
Huang ELin SHsieh I(2021)Online marketplace sellers’ influence on rating scores and comment orientationElectronic Commerce Research10.1007/s10660-021-09511-x23:2(1241-1270)Online publication date: 5-Oct-2021
https://dl.acm.org/doi/10.1007/s10660-021-09511-x
Li GZheng HLiu M(2020)Reselling or drop shipping: Strategic analysis of E-commerce dual-channel structuresElectronic Commerce Research10.1007/s10660-019-09382-320:3(475-508)Online publication date: 1-Sep-2020
https://dl.acm.org/doi/10.1007/s10660-019-09382-3

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Published In

cover image Electronic Commerce Research

Electronic Commerce Research Volume 19, Issue 1

March 2019

248 pages

ISSN:1389-5753

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Copyright © Copyright © 2019 Springer Science+Business Media, LLC, part of Springer Nature.

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Kluwer Academic Publishers

United States

Publication History

Published: 01 March 2019

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Cited By

Jiang CZhu JXu Q(2022)Dissecting click farming on the Taobao platform in China via PU learning and weighted logistic regressionElectronic Commerce Research10.1007/s10660-020-09418-z22:1(157-176)Online publication date: 1-Mar-2022
https://dl.acm.org/doi/10.1007/s10660-020-09418-z
Huang ELin SHsieh I(2021)Online marketplace sellers’ influence on rating scores and comment orientationElectronic Commerce Research10.1007/s10660-021-09511-x23:2(1241-1270)Online publication date: 5-Oct-2021
https://dl.acm.org/doi/10.1007/s10660-021-09511-x
Li GZheng HLiu M(2020)Reselling or drop shipping: Strategic analysis of E-commerce dual-channel structuresElectronic Commerce Research10.1007/s10660-019-09382-320:3(475-508)Online publication date: 1-Sep-2020
https://dl.acm.org/doi/10.1007/s10660-019-09382-3

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