Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

6-2021

Abstract

Strategic sellers on some online selling platforms have recently been using a conditional-rebate strategy to manipulate product reviews under which only purchasing consumers who post positive reviews online are eligible to redeem the rebate. A key concern for the conditional rebate is that it can easily induce fake reviews, which might be harmful to consumers and society. We develop a microbehavioral model capturing consumers’ review-sharing benefit, review-posting cost, and moral cost of lying to examine the seller’s optimal pricing and rebate decisions. We derive three equilibria: the no-rebate, organic-review equilibrium; the low-rebate, boosted-authentic-review equilibrium; and the high-rebate, partially-fake-review equilibrium. We find that the seller’s optimal price and rebate decisions critically depend on both the review-posting and moral costs. The seller adopts the no-rebate strategy when the review-posting cost is low but the moral cost is high, the low-rebate strategy when the review-posting cost is high or when the review-posting cost is intermediate and the moral cost is high, and the high-rebate strategy when the review-posting cost is not too high and the moral cost is low. Our results suggest that it is not always profitable for strategic sellers to adopt the conditional-rebate strategy. Even if the conditional-rebate strategy is adopted, it does not always result in fake reviews. Furthermore, we find that, compared with the benchmark of no rebate, conditional rebates do not always hurt consumer surplus or social welfare. When a low (high) rebate is offered, if the review-posting cost is not too low (not very high), the conditional-rebate strategy can even lead to higher consumer surplus and social welfare. Our findings shed new light on the platform-policy debate about the fake-review phenomenon induced by conditional rebates.

Keywords

Online reviews, Fake reviews, Review manipulation

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

Proceedings of China Summer Workshop on Information Management 14th CSWM 2021, June 26-28

First Page

312

Last Page

317

Identifier

10.1287/isre.2021.1048

City or Country

Online

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