Impact of prior reviews on the subsequent review process in reputation systems

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)62_Review of books or of software (or similar publications/items)peer-review

112 Scopus Citations
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Author(s)

  • Xiao Ma
  • Lara Khansa
  • Yun Deng
  • Sung S. Kim

Detail(s)

Original languageEnglish
Pages (from-to)279-310
Journal / PublicationJournal of Management Information Systems
Volume30
Issue number3
Publication statusPublished - 1 Jan 2013
Externally publishedYes

Abstract

Reputation systems have been recognized as successful online review communities and word-of-mouth channels. Our study draws upon the elaboration likelihood model to analyze the extent that the characteristics of reviewers and their early reviews reduce or worsen the bias of subsequent online reviews. Investigating the sources of this bias and ways to mitigate it is of considerable importance given the previously established significant impact of online reviews on consumers' purchasing decisions and on businesses' profitability. Based on a panel data set of 744 individual consumers collected from Yelp, we used the Markov chain Monte Carlo simulation method to develop and empirically test a system of simultaneous models of consumer review behavior. Our results reveal that male reviewers or those who lack experience, geographic mobility, or social connectedness are more prone to being influenced by prior reviews. We also found that longer and more frequent reviews can reduce online reviews' biases. This paper is among the first to examine the moderating effects of reviewer and review characteristics on the relationship between prior reviews and subsequent reviews. Practically, this study offers businesses effective customer relationship management strategies to improve their reputations and expand their clientele. © 2014 M.E. Sharpe, Inc. All rights reserved.

Research Area(s)

  • Consumer review, Elaboration likelihood model, Hierarchical modeling, MCMC simulation, Reputation systems, Simultaneous equations model

Citation Format(s)

Impact of prior reviews on the subsequent review process in reputation systems. / Ma, Xiao; Khansa, Lara; Deng, Yun et al.

In: Journal of Management Information Systems, Vol. 30, No. 3, 01.01.2013, p. 279-310.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)62_Review of books or of software (or similar publications/items)peer-review