Dreading and Ranting: The Distinct Effects of Anxiety and Anger in Online Seller Reviews

Dezhi Yin, Samuel Bond, Han Zhang

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

8 Citations (Scopus)

Abstract

This paper explores effects of the emotions embedded in a seller review on its perceived helpfulness. Drawing on frameworks from the emotion and cognitive processing literatures, the authors propose that although emotional review content is subject to a well-known negativity bias, the effects of discrete emotions will vary, and that one source of this variance is perceptions of reviewers' cognitive effort. We focused on the roles of two distinct, negative emotions common to seller reviews: anxiety and anger. In Study 1, actual seller reviews from Yahoo Shopping websites were collected to determine the effects of anxiety and anger on review helpfulness. In Study 2, an experiment was utilized to identify and explain the differential impact of anxiety and anger in terms of perceived reviewer effort. Our findings demonstrate the importance of examining discrete emotions in online word-of-mouth, and they also carry important practical implications for consumers and online retailers.

Original languageEnglish
Title of host publicationICIS 2011 Proceedings
PublisherAssociation for Information Systems
Pages3753-3772
Number of pages20
ISBN (Print)9781618394729
Publication statusPublished - Dec 2011
Event32nd International Conference on Information Systems, ICIS 2011 - Shanghai, China
Duration: 4 Dec 20117 Dec 2011
https://aisel.aisnet.org/icis2011 (Conference proceedings)

Publication series

NameInternational Conference on Information Systems, ICIS

Conference

Conference32nd International Conference on Information Systems, ICIS 2011
Country/TerritoryChina
CityShanghai
Period4/12/117/12/11
Internet address

Scopus Subject Areas

  • Information Systems

User-Defined Keywords

  • Consumer decision-making
  • Discrete emotions
  • Electronic commerce
  • Online word-of-mouth
  • Review helpfulness
  • Seller reviews
  • User-generated content

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