Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-02574-7_59
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dc.titleAre we trapped by majority influences in electronic word-of-mouth?
dc.contributor.authorTong, Y.
dc.contributor.authorZhong, Y.
dc.date.accessioned2013-07-11T10:19:26Z
dc.date.available2013-07-11T10:19:26Z
dc.date.issued2009
dc.identifier.citationTong, Y.,Zhong, Y. (2009). Are we trapped by majority influences in electronic word-of-mouth?. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 5610 LNCS (PART 1) : 520-529. ScholarBank@NUS Repository. <a href="https://doi.org/10.1007/978-3-642-02574-7_59" target="_blank">https://doi.org/10.1007/978-3-642-02574-7_59</a>
dc.identifier.isbn3642025730
dc.identifier.issn03029743
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/42843
dc.description.abstractBeing an effective online mechanism to generate large-scale electronic Word-of-Mouth (EWOM), online feedback systems (OFS) offers a variety of system design cues to facilitate consumers' decision making. However, such cues may lead consumers to make inferences based on an overall picture of the majority opinion without scrutinizing the content of reviews. This study draws on theories of majority/minority influence and dual-process to explore the influences of OFS design cues on consumers' learning outcomes (i.e., awareness of product/service, confidence in judgment, intention to searching for additional information and intention to conform to majority). Numerical and power majority influences are examined through two design cues: review clustering format (list-clustering vs. pair-clustering) and source credibility (available vs. unavailable). © 2009 Springer Berlin Heidelberg.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1007/978-3-642-02574-7_59
dc.sourceScopus
dc.subjectMajority influence
dc.subjectOnline feedback system
dc.subjectSystem design
dc.subjectWord-of-mouth
dc.typeConference Paper
dc.contributor.departmentINFORMATION SYSTEMS
dc.description.doi10.1007/978-3-642-02574-7_59
dc.description.sourcetitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.description.volume5610 LNCS
dc.description.issuePART 1
dc.description.page520-529
dc.identifier.isiutNOT_IN_WOS
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