Clements, Matthew T. “Direct and indirect network effects: are they equivalent?." International Journal of Industrial Organization 22.5 (2004): 633-645.
Network effects may be either direct or indirect. While many analyses conflate the two, I show that the ways in which direct and indirect effects influence technological standardization are quite different. Some parameter changes have opposite effects in the two models, and some factors which are irrelevant under direct effects are central under indirect effects. Compatibility in particular has a different interpretation and more subtle implications for standardization in the indirect model.
Network effects, Network externalities, Standards, Compatibility
Andrea Pontiggia, Francesco Virili (2010) “Network effects in technology acceptance: Laboratory experimental evidence." International Journal of Information Management, Volume 30, Issue 1, February 2010, Pages 68–77. link thruDOI
This research analyzes network effects in technology acceptance. The hypothesis is that the size of the user network affects technology acceptance. Even today, empirical measurement of network effects is challenging and there is a lack of experimental evidence. In order to investigate and measure the relationship between network size (number of adopters) and user acceptance, technology acceptance research needs to broaden its scope and approaches. To overcome this limitation we reproduce a particular type of technology acceptance process in a laboratory experiment, controlling for user network size and testing its influence on user perceptions and, ultimately, on acceptance decisions. We measured user perceptions and analyzed the data using consolidated and tested technology acceptance models. The results confirm our hypothesis, showing a significant effect of user network size on user perceptions. Finally, we discuss the theoretical and managerial implications of our approach and findings.
- Technology acceptance;
- Network effects;
- Network externalities;
- Laboratory experiment
Joseph Farrell and Paul Klemperer (2007) “Coordination and Lock-In: Competition with Switching Costs and Network Effects," Chapter 31 in Handbook of Industrial Organization, Volume 3, 2007, Pages 1967–2072.
source: http://dx.doi.org/10.1016/S1573-448X(06)03031-7; see also http://escholarship.org/uc/item/9n26k7v1 for PDF
Switching costs and network effects bind customers to vendors if products are incompatible, locking customers or even markets in to early choices. Lock-in hinders customers from changing suppliers in response to (predictable or unpredictable) changes in efficiency, and gives vendors lucrative ex post market power – over the same buyer in the case of switching costs (or brand loyalty), or over others with network effects. Firms compete ex ante for this ex post power, using penetration pricing, introductory offers, and price wars. Such “competition for the market” or “life-cycle competition” can adequately replace ordinary compatible competition, and can even be fiercer than compatible competition by weakening differentiation. More often, however, incompatible competition not only involves direct efficiency losses but also softens competition and magnifies incumbency advantages. With network effects, established firms have little incentive to offer better deals when buyers’ and complementors’ expectations hinge on non-efficiency factors (especially history such as past market shares), and although competition between incompatible networks is initially unstable and sensitive to competitive offers and random events, it later “tips” to monopoly, after which entry is hard, often even too hard given incompatibility. And while switching costs can encourage small-scale entry, they discourage sellers from raiding one another’s existing customers, and so also discourage more aggressive entry. Because of these competitive effects, even inefficient incompatible competition is often more profitable than compatible competition, especially for dominant firms with installed-base or expectational advantages. Thus firms probably seek incompatibility too often. We therefore favor thoughtfully pro-compatibility public policy.
* Switching costs;
* Network effects;
* Network externalities;
* Indirect network effects
Sujoy Chakravarty (2003) “Experimental Evidence on Product Adoption in the Presence of Network Externalities." Review of Industrial Organization, Volume 23, Numbers 3-4, 233-254, DOI: 10.1023/B:REIO.0000031367.79009.9b.
The benefits accruing to a purchaser of a product due to the existing base of consumers of the same or compatible products are known as network externalities. This paper studies Katz and Shapiro’s (1986) model of network externalities in an experimental setting. Two sellers choose prices for competing technologies sold to two groups of four buyers purchasing sequentially in two stages. The results are qualitatively consistent with Katz and Shapiro’s equilibrium predictions. In certain sessions over three-quarters of first stage buyers purchase the more expensive technology anticipating that later arriving buyers will also buy this technology. In periods where a strong network has been established for a technology in the first stage, over 80 percent of second stage buyers buy that technology, even though in most cases it is priced higher. The data, however, differ from the point predictions of the model.
Catherine Tucker (2008) “Identifying Formal and Informal Influence in Technology Adoption with Network Externalities." MANAGEMENT SCIENCE, Vol. 54, No. 12, December 2008, pp. 2024-2038
Notes by Yi-Nung
Firms introducing network technologies (whose benefits depend on who installs the technology) need to understand which user characteristics confer the greatest network benefits on other potential adopters. To examine which adopter characteristics matter, I use the introduction of a video-messaging technology in an investment bank. I use data on its 2,118 employees, their adoption decisions, and their 2.4 million subsequent calls.The video-messaging technology can also be used to watch TV.Exogenous shocks to the benefits of watching TV are used to identify the causal (network) externality of one individual user’s adoption on others’ adoption decisions. I allow this network externality to vary in size with a variety of measures of informal and formal influence. I find that adoption by either managers or workers in “boundary spanner" positions has a large impact on the adoption decisions of employees who wish to communicate with them. Adoption by ordinary workers has a negligible impact.This suggests that firms should target those who derive their informal influence from occupying key boundary-spanning positions in communication networks, in addition to those with sources of formal influence, when launching a new network technology.
R. Iyengar, C. Van den Bulte, and T. W. Valente “Opinion Leadership and Social Contagion in New Product Diffusion." Marketing Science, March 1, 2011; 30(2): 195 – 212. [Abstract] [PDF]
S. Aral, L. Muchnik, and A. Sundararajan
Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks PNAS, December 22, 2009; 106(51): 21544 – 21549.
[Abstract] [Full Text] [PDF]