Start Date
12-13-2015
Description
Facebook has become a leading avenue for online brand communities (i.e., fan pages) that are used for marketing purposes. Despite the popularity on Facebook, there has been limited empirical research regarding the dynamics of firm-generated content (FGC) and user-generated content (UGC) over a long period of time at the industry level. The objective of this paper is to examine the dynamics of the interactions between FGC, UGC, and offline sales (light vehicles) in the U.S. automobile industry. We collected detailed data including the official Facebook pages of 31 car makes in the U.S., and supplemented the data from these firms’ traditional media efforts, and offline car sales from 2009 to 2014. We employ a panel vector autoregressive (PVAR) model that allows us to investigate the dynamic relationships among multiple time series variables. The preliminary analysis results, expected contributions, and future plans are discussed.
Recommended Citation
Wang, Yen-Yao; Susarla, Anjana; and Sambamurthy, Vallabh, "The Untold Story of Social Media on Offline Sales: The Impact of Facebook in the U.S. Automobile Industry" (2015). ICIS 2015 Proceedings. 3.
https://aisel.aisnet.org/icis2015/proceedings/SocialMedia/3
The Untold Story of Social Media on Offline Sales: The Impact of Facebook in the U.S. Automobile Industry
Facebook has become a leading avenue for online brand communities (i.e., fan pages) that are used for marketing purposes. Despite the popularity on Facebook, there has been limited empirical research regarding the dynamics of firm-generated content (FGC) and user-generated content (UGC) over a long period of time at the industry level. The objective of this paper is to examine the dynamics of the interactions between FGC, UGC, and offline sales (light vehicles) in the U.S. automobile industry. We collected detailed data including the official Facebook pages of 31 car makes in the U.S., and supplemented the data from these firms’ traditional media efforts, and offline car sales from 2009 to 2014. We employ a panel vector autoregressive (PVAR) model that allows us to investigate the dynamic relationships among multiple time series variables. The preliminary analysis results, expected contributions, and future plans are discussed.