Pricing new goods in the presence of multichannel social learning and online fake reviews in social networks


Launching a new product usually involves uncertainty. The rapid development of e-commerce stimulates social learning in social networks, profoundly changing consumers' behaviors. Online product reviews can be classified into first-party and third-party ones regarding publishing channels, but different manipulations of first-party reviews could affect their credibility. Moreover, third-party reviews may provide solutions to above mentioned problems to some extent. However, many prior studies have been conducted on how single-channel social learning affects pricing schemes, ignoring effects of multichannel social learning and fake reviews. This study aims to fill up the gaps by constructing stylized mathematical and empirical models to investigate consumers' decision rules and monopolist's two dynamic pricing policies given dual-channel social learning and different reviews manipulations. The ultimate purpose of this research is to provide useful guidelines for pricing new products and the governance of E-commerce market, which may be relevant to future research of big data in finance.

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AMCIS 2016: Surfing the IT Innovation Wave - 22nd Americas Conference on Information Systems

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