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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2451/31443
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| Title: | Whose and What Chatter Matters? The Impact of Tweets on Movie Sales |
| Authors: | Liu, Yizao
Rui, Huaxia
Whinston, Andrew |
| Keywords: | Twitter, word-of-mouth, dynamic panel data |
| Issue Date: | 17-Jan-2012 |
| Series/Report no.: | Working Papers;11_27 |
| 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 aects movie sales by estimating a dynamic
panel data model using publicly available data and well known machine
learning algorithms. We nd that chatter on Twitter does matter, however,
the magnitude and direction of the eect depends on whom the WOM is from
and what the WOM is about. Measuring Twitter users' in uence by how many
followers they have, we nd that the eect of WOM from more in uential
users is signicantly larger than that from less in uential users. In
support of some recent ndings about the importance of WOM valence on
product sales, we also nd that positive Twitter WOM increases movie
sales while negative WOM decreases them. Interestingly, we nd that the
strongest eect on movie sales comes from those tweets where the authors
express their intention to watch a certain movie. We attribute this to
the dual eects of such intention tweets on movie sales: the direct eect
through the WOM author's own purchase behavior, and the indirect eect
through either the awareness eect or the persuasive eect of the WOM on
its recipients. Our ndings provide new perspectives to understand the
eect 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 in uential
users for companies wishing to harness the power of social broadcasting networks. |
| URI: | http://hdl.handle.net/2451/31443 |
| Appears in Collections: | NET Institute Working Papers Series
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