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

Yizao Liu (), Huaxia Rui () and Andrew Whinston ()
Additional contact information
Huaxia Rui: McCombs School of Business, The University of Texas at Austin, http://www.mccombs.utexas.edu/departments/irom/
Andrew Whinston: McCombs School of Business, The University of Texas at Austin, http://www.mccombs.utexas.edu/departments/irom/

No 11-27, Working Papers from NET Institute

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.

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: 2011-09, Revised 2011-11
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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