In economics and business, a network effect (also called network externality or demand-side economies of scale) is the effect that one user of a good or service has on the value of that product to other people. When network effect is present, the value of a product or service is dependent on the number of others using it.
The classic example is the telephone. The more people own telephones, the more valuable the telephone is to each owner. This creates a positive externality because a user may purchase a telephone without intending to create value for other users, but does so in any case. Online social networks work in the same way, with sites like Twitter and Facebook being more useful the more users join.
The expression "network effect" is applied most commonly to positive network externalities as in the case of the telephone. Negative network externalities can also occur, where more users make a product less valuable, but are more commonly referred to as "congestion" (as in traffic congestion or network congestion).
- 1 Origins
- 2 Benefits
- 3 Technology lifecycle
- 4 Lock-in
- 5 Types of network effects
- 6 Negative network effects
- 7 Interoperability
- 8 Open versus closed standards
- 9 Examples
- 10 See also
- 11 References
- 12 External links
Network effects were a central theme in the arguments of Theodore Vail, the first post patent president of Bell Telephone, in gaining a monopoly on US telephone services. In 1908, when he presented the concept in Bell's annual report, there were over 4000 local and regional telephone exchanges, most of which were eventually merged into the Bell System. The economics of network effects were presented in a paper by Bell employee N. Lytkins in 1917.
The economic theory of the network effect was advanced significantly between 1985 and 1995 by researchers Michael L. Katz, Carl Shapiro, Joseph Farrell and Garth Saloner.
Network effects were popularized by Robert Metcalfe, stated as the Metcalfe's law. Metcalfe was one of the co-inventors of Ethernet and a co-founder of the company 3Com. 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.
According to Metcalfe, the rationale behind the sale of networking cards was that (1) the cost of cards was directly 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 N². 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.
Rod Beckstrom presented a mathematical model for describing networks that are in a state of positive network effect at BlackHat and Defcon in 2009 and also presented the "inverse network effect" with an economic model for defining it as well.
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 value exceeding the price.
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.
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 (though market saturation will still occur).
Network effects are commonly mistaken for economies of scale, which result from business size rather than interoperability. 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 described in system dynamics. System dynamics could be used as a modelling method to describe phenomena such as word of mouth and Bass model of marketing.
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.
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 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 standards are open, enabling competitive implementation by different vendors, there is no vendor lock-in. One example of this would be email, which has a considerable network effect but there is interoperability between different email providers.
Lock-in can also result because users perceive that their switching costs do not cover the value of switching to the new service. For example, relationships developed in one service do not transfer to the new service. In other words, you are not willing to use a possibly better service because you have vested data in the service you are locked into.
Types of network effects
There are many ways to classify networks effects. One popular segmentation views network effects as being of four kinds
- Two-sided network effects: An increase in usage by one set of users increases the value to and participation of a complementary and distinct set of users, and vice versa. An example is developers choosing to code for an operating system with many users, with users choosing to adopt an operating system with many developers. This is a special case of a two-sided market.
- Direct network effects: An increase in usage leads to a direct increase in value for other users. For example, telephone systems, fax machines, and social networks all imply direct contact among users. In two-sided networks, a direct network effect is called a same-side network effect. An example is online gamers who benefit from participation of other gamers as distinct from how they benefit from game developers.
- Indirect network effects: Increases in usage of one product or network spawn increases in the value of a complementary product or network, which can in turn increase the value of the original. Examples of complementary goods include software (such as an Office suite for operating systems) and DVDs (for DVD players). This is why Windows and Linux might compete not just for users, but for software developers. This is more accurately called a cross-side network effect in order to distinguish network benefits that cross distinct markets.
- Local network effects: The structure of an underlying social network affects who benefits from whom. For example, a good displays local network effects when rather than being influenced by an increase in the size of a product's user base in general, each consumer is influenced directly by the decisions of only a typically small subset of other consumers, for instance those he or she is "connected" to via an underlying social or business network. Instant messaging is an example of a product that displays local network effects.
Additionally, there are two sources of economic value that are relevant when analyzing products that display network effects:
- Inherent value: I derive value from my use of the product
- Network value: I derive value from other people's use of the product
Negative network effects
There are negative network effects beyond lock-in.
- Congestion occurs when the efficiency of a network decreases as more people use it, and this reduces the value to people already using it. Traffic congestion that overloads the freeway and network congestion over limited bandwidth both display negative network externalities.
Open versus closed standards
In communication and information technologies, open standards and interfaces are often developed through the participation of multiple companies and are usually perceived to provide mutual benefit. But, 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, 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.
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. See, for example, the work of Steve Wunsch (1999).
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.
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 is justified, it is 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. Also, it is true for the development of enterprise software applications where the Internet (HTTP), databases (SQL), and to a moderate degree, service-oriented message buses (SOA) have become common interfaces. Further up the development chain there are network effects as well in language back-end base platforms (JVM, CLR, LLVM), programming models (FP, OOP) and languages themselves.
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.
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. 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.
There are strong network effects in the initial choice of rail gauge, and in gauge conversion decisions. Even when placing isolated rails not connected to any other lines, track layers usually choose a standard rail gauge so they can use off-the-shelf rolling stock. Although a few manufacturers make rolling stock that can adjust to different rail gauges, most manufacturers make rolling stock that only works with one of the standard rail gauges.
- Anti-rival good
- Beckstrom's law
- Cluster effect
- Open format
- Open system (computing)
- Reed's law
- Returns to scale (increasing returns)
- Semantic Web
- Two-sided market
- ^ Knut Blind (2004). The economics of standards: theory, evidence, policy. Edward Elgar Publishing. ISBN 978-1843767930.
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- ^ Buley, Taylor (2009-07-31). "How To Value Your Networks". Forbes. http://www.forbes.com/2009/07/31/facebook-bill-gates-technology-security-defcon.html. Retrieved 2010-12-10.
- ^ Robert M. Grant (2009). Contemporary Strategy Analysis. John Wiley & Sons. ISBN 0470747102.
- ^ Network Effects
- ^ Nicholas Economides and Evangelos Katsamakas (2008-05). "Two-sided competition of proprietary vs. open source technology platforms and the implications for the software industry". http://web.si.umich.edu/tprc/papers/2005/487/Two-sided.pdf. Retrieved 2010-12-10.
- ^ Thomas Eisenmann and Geoffrey Parker and Marshall Van Alstyne (2006-10). "Strategies for Two Sided Markets". http://hbr.org/2006/10/strategies-for-two-sided-markets/ar/1. Retrieved 2011-06-21.
- ^ Sundararajan, Arun (2007). "Local network effects and complex network structure". The B.E. Journal of Theoretical Economics 7 (1). doi:10.2202/1935-1704.1319. http://www.bepress.com/bejte/vol7/iss1/art46.
- ^ Wunsch, Steve (1999). "Mayday, Mayday"
- ^ The Economics of Programming Languages, David N. Welton : 2005-07-18
- Coordination and Lock-In: Competition with Switching Costs and Network Effects, Joseph Farrell and Paul Klemperer.
- Network Externalities (Effects), S. J. Liebowitz, Stephen E. Margolis.
- An Overview of Network Effects, Arun Sundararajan.
- The Economics of Networks, Nicholas Economides.
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