Explaination of the concept of network effects


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In the course Information Strategy we often hear and read the word “Network Effects”. However, in my opinion it is often not obvious what kind of network effects exist, what other similar effects occur, and especially how to differen
tiate “Network Effects” from these other effects. In this block post I will therefore explain two kinds of network effects and will differentiate them from “Economies of Scale”, “Learning Effects” and “Bandwagon Effects”.

Network effects describe the benefit that accrues to the user of a product or service because he or she is one of many who use it (Swann, 2002, p. 417). One of the first and most influential statements on network effects has been those by Katz and Shapiro (1985, p. 424). They define their concept following: There are many products for which the utility that a user derives from consumption of the good increases with the number of other agents consuming the good. In this early paper, Katz & Shapiro (1985, p. 424) have already illustrated that network effects can be divided into direct and indirect effects.

A direct network effect is generated through a direct physical interaction of users. Therefore, the number of purchasers affects the quality of the product. The utility that a consumer of a communication technology derives, depends on the interaction opportunities. For example, the consumer’s utility of a telefax depends on the number of other users that are joining the network. Thus, a user who is joining the network in the early stage can use it only restricted, however, with every new user, the own utility is further increasing. Summarized, direct network effects are caused by interaction opportunities of users and therefore its benefit is higher in bigger networks than in smaller ones (Fleisch, 2001, pp. 86f.).

In contrast to direct effects, indirect network effects are independent from user interactions. Economies of scale cause a utility increase for the provider and can occur due to dissemination of complementary goods. Sterman (2000, p. 12) clarifies this with the following example: The larger the installed base of Microsoft software and Intel machines, the more attractive the “Wintel” architecture became for developers. And furthermore, the more Wintel computers were sold, the stronger the installed base grows and consequently the more compatible software could be sold. In this way, network effects can be expanded from actual network effect goods to compatible groups of goods (Frels, Shervani & Srivastava, 2003, p. 40).

At the same time, limitations have to be presented in order to boarder them from other consumer and producer effects. Wiese (1993, p. 6) named bandwagon effects, economies of scale, and learning effects as economic effects that seem to be closely related to network effects, but are actually caused by other reasons. These effects are grouped depending on if their effects are related to the presence (static) or the past (dynamic) and if they affect the demand or costs.

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Bandwagon effects appear on demand side and occur when customer increase their demand since they expect an increased overall demand of this product (Leibenstein, 1950, p.189). When considering network effects, the increasing number of users leads to an increased demand as well. However, in contrast to bandwagon effects this is not affected by imitations, but by the increased value of the network effect good, which is triggered by an increased transaction frequency.

Economies of scale arise when unit costs decrease because fixed cost can be distributed over an increased output. According to Wiese (1993, p. 6) bandwagon effects and economies of scale are static since they occur in the presence and are not caused by past events.

In contrast, learning effects occur when unit costs decrease in dependency of the overall output over time. Kloster (2002, p. 27) states that actual users are gaining experience over time and when a product or services is improved due to past experiences these are called a learning effects. They are beneficial for all future users. Accordingly, the utility increase is a derivative one since the increase appeared due to past dynamic experiences and not by the current number of users or their interaction frequency. Thus, learning effects stay within the network, even if the creator leaves it, whereas network effects are getting lost or rather weakened with network exits (Schräder, 2000, pp. 98ff.).

Base of this block post is my bachelor thesis: Network Effects in Transportation Networks on Basis of Supply Chain Economics. In there I deepened in the theory of network effect. When I heard this term in the lecture again I had the feeling that the concept is not totally clear to everyone. For this reason I decided to sum up the theory of network effects.


References:

Fleisch, E. (2001). Das Netzwerkunternehmen: Strategien und Prozesse zur Steigerung der Wettbewerbsfähigkeit in der “Networked economy”. Berlin: Springer.

Frels, J. K., Shervani, T., & Srivastava, R. K. (2003). The integrated networks model: Explaining resource allocations in network markets. Journal of Marketing, 67(1), 29-45.

Katz, M. L., & Shapiro, C. (1985). Network externalities, competition, and compatibility. The American economic review, 75(3), 424-440.

Kleb, H. (2013). Network Effects in Transportation Networks on Basis of Supply Chain Economics: Application on the Southern Link Transport Hub Strategic Vision in Cranbrook, Western Australia (unpublished bachelor thesis). University of St. Gallen.

Kloster, T. (2002). Gestaltung von Logistiksystemen auf Basis von Netzeffekten. Frankfurt/M (et.al.): Peter Lang.

Leibenstein, H. (1950). Bandwagon, snob, and Veblen effects in the theory of consumers’ demand. The Quarterly Journal of Economics64(2), 183-207.

Schräder, A. (2000). Netzeffekte in Transport und Tourismus. Bern et.al.: Haupt.

Sterman, J. (2000). Business dynamics. Boston: Irwin-McGraw-Hill. Available at: http://users.cecs.anu.edu.au/~u3951377/ENGN2225/course-files/wk02-Sterman_Feedback.pdf (visited on 25/08/2013).

Swann, G. M. (2002). The functional form of network effects. Information Economics and Policy, 14(3), 417-429.

Wiese, H. (1993). Lern-und Netzeffekte im asymmetrischen Duopol. Heidelberg: Physica.

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