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Whose and What Chatter Matters? The Effect of Tweets on Movie Sales

Huaxia Rui (), Yizao Liu () and Andrew Whinston ()
Additional contact information
Huaxia Rui: University of Texas
Andrew Whinston: University of Texas

No 8, Working Papers from University of Connecticut, Department of Agricultural and Resource Economics, Charles J. Zwick Center for Food and Resource Policy

Abstract: Social broadcasting networks such as Twitter in the U.S. and "Weibo" in China are transforming the way online word of mouth (WOM) is disseminated and consumed in the digital age. In the present study, we investigated whether and how Twitter WOM affects movie sales by estimating a dynamic panel data model using publicly available data and well-known machine learning algorithms. We found that chatter on Twitter does matter; however, the magnitude and direction of the effect depends on whom the WOM is from and what the WOM is about. Measuring Twitter users' in uence by how many followers they had, we found that the effect of WOM from more in fluential users is signifcantly larger than that from less in uential users. In support of some recent findings about the importance of WOM valence on product sales, we also found that positive Twitter WOM increases movie sales, whereas negative WOM decreases them. Interestingly, we found that the strongest effect on movie sales comes from those tweets in which the authors expressed their intention to watch a certain movie. We attribute this finding to the dual effects of such intention tweets on movie sales: the direct effect through the WOM author's own purchase behavior, and the indirect effect through either the awareness effect or the persuasive effect of the WOM on its recipients. Our findings provide new perspectives to understand the effect of WOM on product sales and have important managerial implications. For example, our study reveals the potential values of monitoring people's intentions and sentiments on Twitter and identifying in uential users for companies wishing to harness the power of social broadcasting networks.

Keywords: Twitter; word-of-mouth; dynamic panel data (search for similar items in EconPapers)
JEL-codes: C2 M3 (search for similar items in EconPapers)
Pages: 29 pages
Date: 2012-05
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