This presentation is part of: L10-1 Market Structure and Performance

Killer Applications and Network Effects: The Case of the U.S. Home Video Game Industry

Richard T. Gretz, Ph.D. and Jannett Highfill, Ph.D., M.A. Economics, Bradley University, 1501 W Bradley Ave, Peoria, IL 61625

The hardware/software relationship is characterized by indirect network effects.  The usual story is as follows:  hardware becomes more attractive to consumers as they are able to access greater amounts of compatible software; more software is likely to be developed for a hardware platform with a larger installed base of consumers.  There is ample theoretical and empirical work to support this story.  (Church & Gandal, 1992, 1993; Clements & Ohashi, 2005; Nair et al., 2004) However, the literature has relied on one important assumption:  software is symmetric.  That is, each unit of software provides the same benefit to the consumer as another unit of software.  We relax this assumption by considering ‘killer applications,’ software desirable enough to entice consumers to purchase the relevant hardware regardless of other compatible software titles.          

We examine the role of killer applications in the home video game industry using a unique data set (1995 – 2007).  The home video game industry is especially suited for this analysis since few game titles ever become hits.  Our results suggest video game console market share is more sensitive to console quality and killer application provision than network size.  These results are robust to several measures of software quality.          

Further, we examine the introduction of killer applications over the lifecycle of a console.  Preliminary evidence suggests network size is not a major determinant of killer application provision.  Our results suggest that policy implications may differ substantially when heterogeneous software is included in the analysis. 

References

Church, J. & Gandal, N. (1992).  Network effects, software provision, and standardization.  The Journal of Industrial Economics, 40(1), 85 – 104.

Church, J. & Gandal, N. (1993).  Complementary network externalities and technological adoption.  International Journal of Industrial Organization, 11(2), 239 – 260.

Clements, M. T. & Ohashi, H. (2005).  Indirect network effects and the product cycle:  video games in the U.S., 1994 – 2002.  The Journal of Industrial Economics, 53(4), 515 – 542.

Nair, H., Chintagunta, P. & Dube, J. (2004).  Empirical analysis of indirect network effects in the market for personal digital assistants.  Quantitative Marketing and Economics, 2, 23 – 58.