Information about Network Effect

A network effect is a characteristic that causes a good or service to have a value to a potential customer which depends on the number of other customers who own the good or are users of the service. In other words, the number of prior adopters is a term in the value available to the next adopter.

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. The resulting bandwagon effect is an example of a positive feedback loop.

Origins of the network effect

The term "network effect" was coined by Robert Metcalfe, the founder of Ethernet. In selling the product, Metcalfe argued that customers needed Ethernet cards to grow above a certain critical mass if they were to reap the benefits of their network. ^[1]

According to Metcalfe, the rationale behind the sale of networking cards was that (1) the cost of cards was proportional to the number of cards installed, but (2) the value of the network was proportional to the square of the number of users. This was expressed algebraically as having a cost of "N", and a value of "N^2". While the actual numbers behind this definition were never firm, the concept allowed customers to share access to expensive resources like disk drives and printers, send e-mail, and access the internet.

Benefits of the network effect

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.

A key business concern must then be how to attract users prior to reaching critical mass. One way is to rely on extrinsic motivation, such as a payment, a fee waiver, or a request for friends to sign up. A more natural strategy is to build a system that has enough value without network effects, at least to early adopters. Then, as the number of users increases, the system becomes even more valuable and is able to attract a wider user base. Joshua Schachter has explained that he built Del.icio.us along these lines - he built an online system where he could keep bookmarks for himself, such that even if no other user joined, it would still be valuable to him. It was relatively easy to build up a user base from zero because early adopters found enough value in the system outside of the network aspects. The same could be said for many other successful websites which derive value from network effects, e.g. Flickr, MySpace.

Beyond critical mass, the increasing number of subscribers generally 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 again equals the price paid. 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. The P2P based telephony service Skype benefits greatly from this effect. 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.

The network effect has a lot of similarities with the description of phenomenon in reinforcing positive feedback loops description of system dynamics (Sterman 2000). System dynamics could be used as a modeling method to describe such phenomenon such as word of mouth and Bass model of marketing.

Network effect business examples

Financial exchanges

Stock exchanges and derivatives exchanges feature a network effect. Market liquidity is a major determinant of transaction cost in the sale or purchase of a security, as a bid-ask spread exists between the price at which a purchase can be done versus the price at which the sale of the same security can be done. 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.

The network advantage of financial exchanges is apparent in the difficulty that startup exchanges have in dislodging a dominant exchange. For example, the Chicago Board of Trade has retained overwhelming dominance of trading in US Treasury Bond futures despite the startup of Eurex US trading of identical futures contracts. Similarly, the Chicago Mercantile Exchange has maintained a dominance in trading of Eurobond interest rate futures despite a challenge from Euronext.Liffe.

Software

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. (Additionally, an example of an indirect network effect in this case is the notable similarity in user-interfaces and operability menus of most new software - since that similarity directly translates into less time spent learning new environments, therefore potentially greater acceptance and adoption of those products.)

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 user-interface standards results in a situation in which one firm is in control of almost 100% of the market.

Microsoft Windows is a further example of network effect. The most-vaunted advantage of Windows, and that most publicised by Microsoft, is that Windows is compatible with the widest range of hardware and software. Although this claim was justified at some point of time, it was in reality the result of network effect: hardware and software manufacturers ensure that their products are compatible with Windows in order to have access to the large market of Windows users. Thus, Windows is popular because it is well supported, but is well supported because it is popular. 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 or interfaces are closed standards the network effect can give the company controlling those standards 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).

Mirabilis is an Israeli start-up which pioneered instant messaging (IM) and was bought 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 temporarily dominate 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 the company.

Web sites

Many web sites also feature a network effect. One example is web marketplaces and exchanges, 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 number of users grows on eBay, auctions grow more competitive, pushing up the prices of bids on items. This makes it more worthwhile to sell on eBay and brings more sellers onto eBay, which drives prices down again as this increases supply, while bringing more people onto eBay because there are more things being sold that people want. Essentially, as the number of users of eBay grows, prices fall and supply increases, and more and more people find the site to be useful.

The collaborative encyclopedia Wikipedia also benefits from a network effect. The theory goes that 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; some of the new users in turn become editors, continuing the process.

Social networking websites are also good examples. The more people register 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.

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 Google AdSense 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 were used as justification for some of the dot-com business models 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 than about becoming profitable. This was believed because market share will determine which firm can set technical and marketing standards and thus determine the basis of future competition.

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.

Network effects and lock-in

Not surprisingly network economics became a hot topic after the diffusion of the Internet across academia. Most people know only of Metcalfe's law as part of network effects. Network effects are notorious for causing vendor lock-in with the most-cited examples being Microsoft products and the qwerty keyboard.

Network effects are a source of, but distinct from, lock-in. Lock-in can result from network effects, and network effects generate increasing returns that are associated with lock-in. However, the presence of a network effect does not guarantee that lock-in will result. For example, if the network is open there is no issue of lock-in.

Types of network effects

There are two kinds of economic value to be concerned about when thinking of network effects:

Inherent — my value from my using the product
Network — my value from your using the product

Network value itself can be direct or indirect.

Direct network value is an immediate result of other users adopting the same system. Some examples of this are fax machines and email.

Indirect is a secondary result of many people using the same system. For example, complementary goods are cheaper or more available when many people adopt a standard. Toner may be cheaper for widely used printers. An example of this is that Windows and Linux can be seen as competing not for users, but for software developers, as shown by Nicholas Economides and Evangelos Katsamakas.

Negative and positive network effects

Positive network effects are obvious. More people means more interaction. Wikipedia itself depends on positive network effects. Negative network effects beyond lock-in also exist.

Negative network effects result from resource limits. Consider the connection that overloads the freeway — or the competition for bandwidth. In fact, the automobile and ethernet congestion examples illustrate that there can be threshold limits. In this case, the n+1 person begins to decrease the value of a network if additional resources are not provided.

The result is that in some networks there is an exclusion value. This is clear to anyone who has considered problems of authentication or trust on the modern internet.

Another negative network effect is provider complacency. The absence of viable competitors in a successful network can cause a provider to restrict resources, consider fee increases, or otherwise create an environment contrary to the users' benefit. These situations are typically accompanied by vocal complaints from the users. (In a competitive environment the users would simply change vendors rather than complain.)

Classic examples are the United States Postal Service or telephone companies during the 1960s and 1970s. More recent examples include Microsoft's operating system and Ebay's auction site.

See also

• Open system (computing)
• Open standard
• Open format
• Vendor lock-in
• Path dependency
• Metcalfe's law
• Reed's law
• Betamax
• Technology lifecycle
• Business model
• Bandwagon effect
• Cluster effect
• The Long Tail
• List of management topics
• Graph Theory
• Semantic Web

References

External links

• Coordination and Lock-In: Competition with Switching Costs and Network Effects, Joseph Farrell and Paul Klemperer.
• Network Effect — The Best Barrier-to-Entry? — An article that discusses how powerful Network Effect can be in the high-tech industry.
• Network Externalities (Effects), S. J. Liebowitz, Stephen E. Margolis.
• An Overview of Network Effects, Arun Sundararajan.
• The Economics of Networks, Nicholas Economides.
• Social Science Research Network and NBER — research on the economics of networks and network effects.