Abstract
Online reviews as a major form of user-generated contents play a strategic role in eliminating information asymmetry and facilitating transactions in electronic markets. To improve market efficiency, many online retailers use helpfulness voting as a crowdsourcing strategy to identify and rank online reviews. In this study, we analysed a data set of 2187 reviews as well as more than two months’ review ranking data collected from six best-selling products on amazon.com. We found that a disproportionately higher percentage of votes went to early-posted lengthy reviews due to the Matthew effect. We also found that early reviews, once identified as most helpful, could maintain their top ranking status throughout the product life cycle because of the Ratchet effect. The implications of these findings were discussed and strategies for how to mitigate negative impacts of such effects by online retailers were suggested.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
