Network effect

“In the case of a social network, the interaction may occur between any pair of nodes, and the probability of a person joining a social network is highly dependent upon the probability of his friends being a part of the social network as well.” (Aggarwal, Charu and Philip, 2012:147).

The probability of a user using an application depends on the probability of their friends using it too. Further, whilst many users may belong to several social networks at the same time, they are only active in one at a time, and this depends on whether their friends are active in it. Furthermore, if they and their friends register on one social network, they may only stay active for a short period of time until a better or more attractive social network appears. This has an exponential element: as more people join the network, more of their friends will join the network which thus encourages more people to join, and as that single network grows, other networks shrink (Facebook vs MySpace, Bebo, etc).

To be successful in a social media network already dominated by one app (eg Foursquare), the new entrant needs to include an as different as possible interaction model. For example, Facebook is the dominant social network within the field, but Twitter offers succinct posts and LinkedIn offers employer references that are not offered or trusted by the other networks. This allows for the peaceful co-existence of the networks without stimulating direct competition, and often results in interoperability between the networks to further negate risks associated with direct competition.

Application to Scene

The option to tweet or post a Facebook status directly from the app will encourage interoperability, boosting the potential adoption of the app and simultaneously promoting it across the different networks, recursively driving interest in the app. Perhaps uploading pictures to Instagram too.

The cascading effect (Fowler and Christakis, 2010) of key users within dense clusters adopting the application is also a key factor. If the benefits for the others in the network, such as connecting with their other friends in the network, outweigh the effort required of familiarising and using the service then the effect will cascade through the cluster and provide us with more users.

References

Aggarwal, C. C., Charu, C. and Philip, S. Y. 2012. On the network effect in Web 2.0 applications. Electronic Commerce Research and Applications, 11(2), pp.142-151.
Chicago

Fowler, J. H. and Christakis, N. A., 2010. Cooperative behaviour cascades in human social networks. PNAS, 107(12), pp.5334-5338.