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When network products and services become more valuable as their userbase grows (network effects), this tendency can become a major determinant of how they compete with each other in the market and how the market is structured. Network effects are traditionally linked to high market concentration, early-mover advantages, and entry barriers, and in the market they have also been used as a valuation tool. The recent resurgence of Bitcoin has been partly attributed to network effects, too. We study the existence of network effects in six cryptocurrencies from their inception to obtain a high-level overview of the application of network effects in the cryptocurrency market. We show that, contrary to the usual implications of network effects, they do not serve to concentrate the cryptocurrency market, nor do they accord any one cryptocurrency a definitive competitive advantage, nor are they consistent enough to be reliable valuation tools. Therefore, while network effects do occur in cryptocurrency networks, they are not (yet) a defining feature of the cryptocurrency marketas a whole.

The customer base is an important value driver of software companies and a reliable prediction of its development is fundamental for investment decisions. A particularity in software markets is that an individual's purchasing decision is often influenced by other users' choices. Although such customer network effects are evident, their quantitative assessment remain elusive with conventional approaches. This book contributes to closing this gap by developing methods for measuring network effects and their implications for valuation in software markets. Based on the theory of complex networks the book reveals that such diffusion processes highly depend on structural properties of customer networks. Moreover, it depicts that such insights are contributions to improve the quality of valuations in software markets. But the implications of this research also comprise social and political aspects as they can be applied in order to prevent corporate failures in all network effect markets.

The customer base is an important value driver of software companies and a reliable prediction of its development is fundamental for investment decisions. A particularity in software markets is that an individual's purchasing decision is often influenced by other users' choices. Although such customer network effects are evident, their quantitative assessment remain elusive with conventional approaches. This book contributes to closing this gap by developing methods for measuring network effects and their implications for valuation in software markets. Based on the theory of complex networks the book reveals that such diffusion processes highly depend on structural properties of customer networks. Moreover, it depicts that such insights are contributions to improve the quality of valuations in software markets. But the implications of this research also comprise social and political aspects as they can be applied in order to prevent corporate failures in all network effect markets.

Cryptocurrencies are digital alternatives to traditional government‐issued paper monies. Given the current state of technology and skepticism regarding the future purchasing power of existing monies, why have cryptocurrencies failed to gain widespread acceptance? I offer an explanation based on network effects and switching costs. In order to articulate the problem that agents considering cryptocurrencies face, I employ a simple model developed by Dowd and Greenaway (1993) (Dowd, K., and D. Greenaway. "Currency Competition, Network Externalities, and Switching Costs: Towards an Alternative View of Optimum Currency Areas." The Economic Journal, 103(420), 1993, 1180–89). The model demonstrates that agents may fail to adopt an alternative currency when network effects and switching costs are present, even if all agents agree that the prevailing currency is inferior. The limited success of bitcoin—almost certainly the most popular cryptocurrency to date—serves to illustrate. After briefly surveying episodes of successful monetary transition, I conclude that cryptocurrencies like bitcoin are unlikely to generate widespread acceptance in the absence of either significant monetary instability or government support. (JEL E40, E41, E42, E49)

This paper presents a model of local network effects in which agents connected in a social network each value the adoption of a product by a heterogeneous subset of other agents in their neighborhood, and have incomplete information about the structure and strength of adoption complementarities between all other agents. I show that the symmetric Bayes-Nash equilibria of this network game are in monotone strategies, can be strictly Pareto-ranked based on a scalar neighbor-adoption probability value, and that the greatest such equilibrium is uniquely coalition-proof. Each Bayes-Nash equilibrium has a corresponding fulfilled-expectations equilibrium under which agents form local adoption expectations. Examples illustrate cases in which the social network is an instance of a Poisson random graph, when it is a complete graph, a standard model of network effects, and when it is a generalized random graph. A generating function describing the structure of networks of adopting agents is characterized as a function of the Bayes-Nash equilibrium they play, and empirical implications of this characterization are discussed.