ssive information contagion behind an innovation's adoption and diffusion have been proposed. In the epidemic diffusion model a potential adopter would adopt a new innovation immediately upon receiving information about it. As the information about the innovation spreads by contagion, the diffusion is bound to take time. Other theories assume, for example, that for some reasons the benefit or utility of potential adopters vary, and thus they adopt at different points of time. One of the diffusion Fig. 1 The S-shaped innovation diffusion curve (see, for example, Rogers 2003) Theories Related to Vertical Software Industry Evolution proposed reasons for the varying benefits derived is the presence of network externalities or effects. Economides (1991) states that network effects occur when "The buyer of the last unit of a good has a higher benefit than the buyer of the first because the sale of the earlier units has created some benefits in a related dimension" - i.e., the number of existing users (the installed base) affects the benefit of a new adopter. Positive network effects have been noted to influence the adoption and diffusion of many technological innovations (e.g., Church and Gandal 1993; Katz and Shapiro 1985; Witt 1997). Network externalities have also been seen as complicating the fore- casting of the adoption success in telecommunications services (Schoder 2000). Network externalities can be seen to lead to the above mentioned logistic diffusion model, where the diffusion is also related to the number of existing users. Network effects cause the presence of a critical mass point in the diffusion of an innovation: In the early phase of the diffusion the innovation does not seem attractive to potential adopters, because there are only few other users. If the critical mass is not reached, the diffusion will fail. On the other hand, if the critical mass point is reached, the innovation diffuses successfully. The critical mass point in the diffusion process is generally expected to occur approximately betwe