There have been different approaches to examine the potential determinants of movie box office success. Most of the studies conclude that movie critics play a significant role for the success or failure of a film (Terry & Butler & De’Armond, 2005). Eliashberg and Shugan (1997) distinguish two possible perspectives on the role of critics: The influencer and the predictor perspective. From the first perspective critics are opinion leaders who influence their audience and, consequently, the box office performance of movies. The predictor perspective suggests that critics might be predictors of performance but not necessarily causing it. Dodds and Holbrook (1988) conducted an analysis where they compared influence and the effect of an Oscar nomination and movie critics on the success of a movie at the box office. Pardoe (2005) focused on models predicting nominees or winners at the Academy Awards.

Awad, Dellarocas and Zhang (2004) analyzed the influence of online movie ratings on box office success and developed statistical models based on these ratings to forecast movie revenues. Furthermore, they examined the relationship of traditional consumer communication, such as infomediary (professional critics), and online word-of-mouth versus offline word-of-mouth. They used the Internet Movie Database (IMDb, http://www.imdb.com) as their main source for the online data and determined the correlation between infomediaries and online word-of-mouth as well as infomediaries and offline word-of-mouth. Eventually, they came to the conclusion that online word- of-mouth has great potential for growth and an increasing number of consumers will use online rating and online review sites as the Internet becomes more pervasive. Surveying current critical issues in the motion picture industry, Eliashberg, Elberse and Leenders (2006) suggest further research relating Internet resources and movie consumption as well as box office sales.

Research regarding trendsetters (Clark & Zboja & Goldsmith, 2007, Valente, 1996) is often associated with the concept of social network analysis (Wasserman & Faust, 1994). One prominent concept is the one of information cascades (Bikhchandani & Hirshleifer & Welch, 1992, Anderson & Holt, 1996, Anderson & Holt, 1997, Bikhchandani & Hirshleifer & Welch, 1998) which explains convergent behaviour patterns and therefore holds potential to identify trends and trendsetters. However, other experiments showed only limited validity of the concept being applied to different laboratory setups (Huck & Oechssler, 2000, Hung & Plott, 2001). Trendsetters have also been of great interest for quite some time in the field of marketing where Myers and Robertson (1972) discuss the importance of “opinion leadership”. Connected to opinion leaders is the concept of social contagion which describes the spreading of behavior patterns in a community (Burt, 1987, Crandall, 1988, Rodgers & Rowe, 1993, Kretschmer & Klimis & Choi, 1999). Yet, contagion of opinion does not necessarily result from social influencers, also marketing actions can induce the spread of a certain opinion (Bulte & Lilien, 2001). While IMDb has been frequently used as a basis to predict movie success by other researchers (Eliashberg & Sawhney, 1996, Jensen & Neville, 2002, Pardoe, 2005, Simonoff & Sparrow, 2000, Dellarocas & Awad & Zhang, 2007, Kaplan, 2006), little research has been done so far in using communication behavior and social network structure of an online community as a determinant of movie success at the box office and as a predictor for Oscar nominations.

Although Awad, Dellarocas and Zhang (2004) base their model on movie ratings of an online community, they do not make use of further information which could be retrieved through an analysis of the patterns of communication in that community. Our approach enhances prior research by taking into account social network structures in an online community and by measuring discussion content rather than movie ratings.