The network effect arises when users gain value from others using the same network. The more people that join the more value for everyone else, this dynamic is driven by positive externalities and feedback loops. For example, when a person chooses to learn a particular language they are not just generating value for themselves but also some of the value is being externalized to everyone else who is using that network, as they now have more communications options available to them due to this positive externality. The network effect gives us what is called Metcalfe’s law which suggests that the value of a network is proportional to the square of the number of users of that network. because of all of these positive externalities the system as a whole now has a value greater than it individuals. With the network effect, people will not only adopt a phenomenon based upon its value in isolation but also on an assessment of how many others will also adopt the phenomenon; we choose to go to a party or some gathering only if we think others will also go. Thus expectation becomes very important, people not only have to value something but they have to expect that others will also adopt it. In this way, expectation can be a very high leverage point with respect to diffusion on social networks. The network effect is also notorious for creating lock-in because there is so much value created by everyone simply using the same network, this creates a strong force towards convergence – everyone using the one network at the expense of all others. We can see this with the dominance of English as a global language and the decline of many other smaller languages.
A good example of the network effect would be a language, the value of some language is relative to the number of other users of that language, the more people that adopt that language the more valuable it will be. People learn English, Spanish and Chinese as a second language not because those languages are in anyway better than others, but simply because billions of people speak these languages giving them a powerful network effect and lots of value. The network effect may be seen in the formation and spreading of many phenomena within social networks, such as the spreading of some fashion. As is invariably the case with positive feedback, the network effect results in exponential growth, tipping points, and cascades. Network effects may also be seen in stock markets and derivatives exchanges. Market liquidity is a major determinant of transaction cost in the sale or purchase of a security. As the number of buyers and sellers on an exchange increases, liquidity increases, and transaction costs decrease. This then attracts a larger number of buyers and sellers to the exchange. Thus we can see the positive feedback loop that is behind the network effect.
This network effect may give the diffusion process a strong tipping point, because below a certain level of people adopting that phenomenon the value is very low, we might say sublinear. Adopting some radical new fashion when no one else has will come at a great social cost, but doing it when everyone else has will come at a much greater value. In the initial phase of a network’s formation, due to the limited number of nodes and connections in the network, the value of joining that network may, in fact, be negative because of the opportunity cost. Joining this network may well exclude you from joining another more mature network that already has a lot of network value. For example, if you choose to adopt a Linux operating system, you will be limiting your capacity to interoperate with over one billion users of Windows. Thus, in terms of opportunity cost, you are actually having to pay to be part of this burgeoning Linux network, and the same would be true for a social network, digital currencies and many other types of networks that have not reached a critical mass. These early adopters are typically special interest users that particularly care about this service and are prepared to pay the opportunity cost. Thus the pioneers of some new phenomena – whether we are talking about a new political opinion, a new social movement or a new style – these first adopters will have to be very committed putting in a lot of resources and getting little out, but if the phenomenon does spread then the network effect will take hold, there will be a snowball effect due to the positive externalities, there will be some tipping point or phase transition where it rapidly goes from a fringe activity to a mainstream phenomenon and the course of least resistance.
1. Chapter 17 Network Effects. (2010). [online] Available at: https://www.cs.cornell.edu/home/kleinber/networks-book/networks-book-ch17.pdf [Accessed 4 Sep. 2020].
2. Aija Leiponen (2014). Network effects. YouTube. Available at: https://www.youtube.com/watch?v=gbd-93yi9vQ&ab_channel=AijaLeiponen [Accessed 4 Sep. 2020].
3. Aija Leiponen (2014). Network effects. YouTube. Available at: https://www.youtube.com/watch?v=gbd-93yi9vQ&ab_channel=AijaLeiponen [Accessed 4 Sep. 2020].