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dc.contributor.authorTang, V. W.-
dc.date.accessioned2020-08-20T03:30:40Z-
dc.date.available2020-08-20T03:30:40Z-
dc.date.issued2017-
dc.identifier.issn1475-679X-
dc.identifier.otherBBKH1924-
dc.identifier.urihttp://thuvienso.vanlanguni.edu.vn/handle/Vanlang_TV/21290-
dc.description46 tr. ; 317 kb; Journal of Accounting Research Vol. No. xxxx 2017vi
dc.description.abstract"This paper examines whether third-party-generated product information on Twitter, once aggregated at the firm level, is predictive of firm-level sales, and if so, what factors determine the cross-sectional variation in the predictive power. First, the predictive power of Twitter comments increases with the extent to which they fairly represent the broad customer response to products and brands. The predictive power is greater for firms whose major customers are consumers rather than businesses. Second, the word-of-mouth effect of Twitter comments is greater when advertising is limited. Third, a detailed analysis of the identity of the tweet handles provides the additional insights that the predictive power of the volume of Twitter comments is dominated by “the wisdom of crowds,” whereas the predictive power of the valence of Twitter comments is largely attributable to expert comments. Furthermore,Twitter comments not only reflect upcoming sales, but also capture an unexpected component of sales growth."vi
dc.language.isoenvi
dc.publisherUniversity of Chicagovi
dc.subjectWisdom of Crowdsvi
dc.subjectSocial Mediavi
dc.subjectProduct Informationvi
dc.subjectWord of Mouthvi
dc.subjectTwittervi
dc.subjectFundamental Analysisvi
dc.titleWisdom of Crowds: Cross-Sectional Variation in the Informativeness of Third-Party Generated Product Information on Twittervi
dc.typeOthervi
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