Online product reviews-triggered dynamic pricing: Theory and evidence

  • Juan Feng
  • , Xin Li
  • , Xiaoquan Michael Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

Prior works offer compelling evidence that, on the demand side of the market, user-generated online product reviews play a very important role in informing consumers’ purchase decisions. On the supply side, however, the interplay between online product reviews and firm strategies is less understood. We build an analytical model that differentiates products based on consumers’ preference for tastes (horizontal differentiation) or quality (vertical differentiation) and show that a firm is able to not only manipulate its pricing to influence online product reviews (thus influencing sales) but also, adjust pricing dynamically in response to online word of mouth. Our model derives rich and testable results on possible price trajectories. To offer empirical support for the analytical predictions, we conduct a panel data study of prices and reviews. We adopt a difference-in-differences framework to address endogeneity challenges.

Original languageEnglish
Pages (from-to)1107-1123
Number of pages17
JournalInformation Systems Research
Volume30
Issue number4
DOIs
Publication statusPublished - 2019
Externally publishedYes

Keywords

  • Analytical model
  • Empirical study
  • Online product reviews
  • Pricing

ASJC Scopus subject areas

  • Management Information Systems
  • Information Systems
  • Computer Networks and Communications
  • Information Systems and Management
  • Library and Information Sciences

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