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

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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. We investigate 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 find 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' influence by how many followers they have, we find that the effect of WOM from more influential users is significantly larger than that from less influential users. In support of some recent findings about the importance of WOM valence on product sales, we also find that positive Twitter WOM increases movie sales while negative WOM decreases them. Interestingly, we find that the strongest effect on movie sales comes from those tweets where the authors express their intention to watch a certain movie. We attribute this 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 intention and sentiment on Twitter and identifying influential users for companies wishing to harness the power of social broadcasting networks.

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File URL: http://www.netinst.org/Liu_Rui_11_27.pdf
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Bibliographic Info

Paper provided by NET Institute in its series Working Papers with number 11-27.

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Length: 29 pages
Date of creation: Sep 2011
Date of revision: Nov 2011
Handle: RePEc:net:wpaper:1127

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Web page: http://www.NETinst.org/

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Keywords: Twitter; word-of-mouth; dynamic panel data;

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