The network effect causes a good or service to have a value to a potential customer dependent on the number of customers already owning that good or using that service. Ergo, it means that the total value of a good or service that possesses a network effect is roughly proportional to the square of the number of customers already owning that good or using that service.
One consequence of a network effect is that the purchase of a good by one individual indirectly benefits others who own the good - for example by purchasing a telephone a person makes other telephones more useful. This type of side-effect in a transaction is known as an externality in economics, and externalities arising from network effects are known as network externalities. This is also an example of a positive feedback loop.
Network effects were used as justification for some of the business models for dot-coms in the late 1990s. These firms operated under the belief that when a new market comes into being which contains strong network effects, firms should care more about growing their market share . This was believed because market share will determine which firm can set technical and marketing standards and thus determine the basis of future competition.
A good example of this strategy was that deployed by Mirabilis, the Israeli start-up which pioneered instant messaging ("IM") and was bought-out by America Online. By giving away their ICQ product for free and preventing interoperability between their client software and other products, they were able to corner the market for instant messaging. Because of the network effect, new IM users gained much more value by choosing to use the Mirabilis system (and join its large network of users) than they would using a competing system. As was typical for that era, the company never made any attempt to generate profits from their dominant position before selling out.
Network effects become significant after a certain subscription percentage has been achieved, called critical mass. At the critical mass point, the value obtained from the good or service is greater than or equal to the price paid for the good or service. As the value of the good is determined by the user base, this implies that after a certain number of people have subscribed to the service or purchased the good, additional people will subscribe to the service or purchase the good due to the positive utility : price ratio. Until this point has been achieved, however, only early adopters will subscribe.
The increasing number of subscribers cannot continue indefinitely. After a certain point, most networks become either congested or saturated, stopping future uptake. Congestion occurs due to overuse. The applicable analogy is that of a telephone network. While the number of users is below the congestion point, each additional user adds additional value to every other customer. However, at some point the addition of an extra user exceeds the capacity of the existing system. After this point, each additional user decreases the value obtained by every other user. In practical terms, each additional user increases the total system load, leading to busy signals, the inability to get a dial tone, and poor customer support. The next critical point is where the value obtained equals the price paid again. The network will cease to grow at this point, and the system must be enlarged. The congestion point may be larger than the market size . New Peer-To-Peer technological models may always defy congestion. Peer-to-Peer systems, or "P2P," are networks designed to distribute load among their user pool. This theoretically allows true P2P networks to scale indefinitely. But market saturation will still occur.
Network effects are commonly mistaken for economies of scale, which result from business size rather than interoperability (see also natural monopoly). To help clarify the distinction people speak of demand side vs. supply side economies of scale. Classical economies of scale are on the production side, while network effects arise on the demand side. Network effects are also mistaken for economies of scope.
There are very strong network effects operating in the market for widely-used computer software. Take for example Microsoft Office. For many people choosing an office suite, prime considerations include how valuable having learned that office suite will prove to potential employers, and how well the software interoperates with other users. That is, since learning to use an office suite takes many hours, they want to invest that time learning the office suite that will make them most attractive to potential employers (or consulting clients, etc), and they also want to be able to share documents with potential co-workers.
Similarly, finding already-trained employees is a big concern for employers when deciding which office suite to purchase or standardize on. The lack of cross-platform standards results in a situation in which one firm is in control of almost 100% of the market.
However, network effects need not lead to market dominance by one firm, when there are standards which allow multiple firms to interoperate, thus allowing the network externalities to benefit the entire market. This is true for the case of x86-based personal computer hardware, in which there are extremely strong market pressures to interoperate with pre-existing standards, but in which no one firm dominates in the market. The same holds true for the market for long-distance telephone service within the United States. In fact, the existence of these types of networks discourages dominance of the market by one company, as it creates pressures which work against one company attempting to establish a proprietary protocol or to even distinguish itself by means of product differentiation.
In cases in which the relevant communication protocols are under the control of a single company, however, the network effect can give the company monopoly power. The Microsoft corporation is widely seen by computer professionals as maintaining its monopoly through these means. One observed method Microsoft uses to put the network effect to its advantage is called embrace and extend (derisively called embrace, extend, and extinguish).
A limited handful of websites also feature a network effect.
Generally, web marketplaces and exchanges feature a network effect, in that the value of the marketplace to a new user is proportional to the number of other users in the market. For example, eBay would not be a particularly useful site if auctions were not competitive. However, as the numbers of users grew on eBay, auctions grew more competitive, pushing up the prices of bids on items. This made it more worthwhile to sell on eBay and brought more sellers onto eBay, which drove prices down again as this increased supply and also brought more people onto eBay because there were things being sold that people wanted. Essentially, as the number of users of eBay grew, the prices better reflected supply-and-demand and more and more people found the site to be useful.
The collaborative encyclopedia Wikipedia also benefits from a network effect. As the number of editors grows, the quality of information on the website improves, encouraging more users to turn to it as a source of information; many of the new users in turn became editors, continuing the process.
Social networking websites are also good examples. The more people registered onto a social networking website, the more useful the website is to its registrants.
By contrast, the value of a news site is primarily proportional to the quality of the articles, not to the number of other people using the site. Similarly, the first generation of search sites experienced little network effect, as the value of the site was based on the value of the search results. This allowed Google to win users away from Yahoo! without much trouble, once users believed that Google's search results were superior. Some commentators mistook the value of the Yahoo! brand (which does increase as more people know of it) for a network effect protecting its advertising business.
Interestingly, Alexa Internet uses a technology that tracks users' surfing patterns; thus Alexa's Related Sites results improve as more users use the technology. As theory would predict, no competing technology has emerged to compete successfully with Alexa, but this may be because of other factors. Alexa's network relies heavily on a small number of browser software relationships, which makes the network more vulnerable to competition.
Google has also attempted to create a network effect in its advertising business with its Google AdSense service. Google AdSense places ads on many small sites, such as blogs, using Google technology to determine which ads are relevant to which blogs. Thus, the service appears to aim to serve as an exchange (or ad network) for matching many advertisers with many small sites (such as blogs). In general, the more blogs GoogleAdSense can reach, the more advertisers it will attract, making it the most attractive option for more blogs, and so on, making the network more valuable for all participants.
Network effects and technology lifecycle
If some existing technology or company whose benefits are largely based on network effects starts to lose market share against a challenger such as a disruptive technology or open standards based competition, the benefits of network effects will reduce for the incumbent, and increase for the challenger.
In this model, a tipping point is eventually reached at which the network effects of the challenger dominate those of the former incumbent, and the incumbent is forced into an accelerating decline, whilst the challenger takes over the incumbent's former position.