Mechanisms of negativity bias: An empirical exploration of App reviews in Apple'S App Store

Dezhi Yin, Sabyasachi Mitra, Han Zhang

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

10 Citations (Scopus)

Abstract

Researchers in many diverse areas have consistently found that we are unduly influenced by negative information. In electronic commerce, this negativity bias is evident in the effect of product reviews on consumer behavior in the information systems literature. While the negativity bias is well documented, there has been little systematic and empirical research on its underlying causes. Utilizing a novel data set collected from Apple's App Store, we examine three probable causes of the negativity bias: that negative reviews are more specific, that they have higher surprise value, and that they increase our ability to avoid losses. The empirical analysis revealed that while all three mechanisms contribute to the negativity bias, the 'surprise' factor and the ability to avoid losses play a more prominent role when consumers process and integrate positive and negative review information. Our findings also carry important practical implications for review platforms and online companies.

Original languageEnglish
Title of host publicationICIS 2012 Proceedings
PublisherAssociation for Information Systems
Pages3057-3069
Number of pages13
ISBN (Print)9781627486040, 9780615718439
Publication statusPublished - Dec 2012
Event33rd International Conference on Information Systems, ICIS 2012 - Orlando, FL, United States
Duration: 16 Dec 201219 Dec 2012
https://aisel.aisnet.org/icis2012 (Conference proceedings)

Publication series

NameInternational Conference on Information Systems, ICIS

Conference

Conference33rd International Conference on Information Systems, ICIS 2012
Country/TerritoryUnited States
CityOrlando, FL
Period16/12/1219/12/12
Internet address

Scopus Subject Areas

  • Computer Science Applications
  • Statistics, Probability and Uncertainty
  • Applied Mathematics
  • Library and Information Sciences

User-Defined Keywords

  • Negativity bias
  • Online word-of-mouth
  • Product review
  • Review helpfulness
  • Review rating

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