Interactive , Option-Value and Domino Network Effects in Technology Adoption February 2

The network benefits of new technologies are often modeled as depending on the total number of users in a network. However, it is common in networks for only small subsets of network users to interact. Therefore for network benefits to depend on the total network size requires network benefits which are derived outside of actual interactions. One possibility is that network users place an option value on calling those they do not ultimately interact with, creating an“option network effect”. Alternatively, a “domino network effect” may occur if network users anticipate that having more users in the network increases the likelihood of those they want to talk with adopting. Therefore, the extent to which potential adopters value adoption by users they do not interact with is an empirical question. Studying network effects at the aggregate level does not permit a distinction between interactive and non-interactive (option and domino) network effects. Therefore I use extensive micro-data on all potential adopters of a firm’s internal video-messaging system and their subsequent video-messaging patterns to examine the role of different kinds of network effects in technology adoption. The technology can also be used to watch TV. Exogenous shocks to the benefits of watching TV are used to identify the causal (network) effect of changes in the installed base on adoption decisions. I find evidence that network effects are based on interactions, and that potential adopters only react to adoption by people they wish to communicate with. This implies that the network benefits to adding a user for a new technology could be more restricted in scope than previously supposed.

[1]  Takeshi Amemiya,et al.  The nonlinear two-stage least-squares estimator , 1974 .

[2]  Jeffrey H. Rohlfs A theory of interdependent demand for a communications service , 1974 .

[3]  C. Shapiro,et al.  Network Externalities, Competition, and Compatibility , 1985 .

[4]  Joseph Farrell,et al.  Standardization, Compatibility, and Innovation , 1985 .

[5]  Joseph Farrell,et al.  Installed base and compatibility : innovation, product preannouncements and predation , 1986 .

[6]  Whitney K. Newey,et al.  Efficient estimation of limited dependent variable models with endogenous explanatory variables , 1987 .

[7]  C. Manski Identification of Endogenous Social Effects: The Reflection Problem , 1993 .

[8]  Garth Saloner,et al.  Digitized by the Internet Archive in 2011 with Funding from Adoption of Technologies Uith Network Effects: an Empirical Examination of the Adoption of Automated Teller Machines , 2022 .

[9]  C. Shapiro,et al.  Systems Competition and Network Effects , 1994 .

[10]  Austan Goolsbee,et al.  Evidence on Learning and Network Externalities in the Diffusion of Home Computers , 1999 .

[11]  Marc Rysman,et al.  Competition between Networks: A Study of the Market for Yellow Pages , 2002 .

[12]  G. Bijwaard Instrumental Variable Estimation for Duration Data: A Reappraisal of the Illinois Reemployment Bonus Experiment , 2002 .

[13]  G. Gowrisankaran,et al.  Network Externalities and Technology Adoption: Lessons from Electronic Payments , 2002 .

[14]  Harikesh S. Nair,et al.  Empirical Analysis of Indirect Network Effects in the Market for Personal Digital Assistants , 2004 .

[15]  Lyle H. Ungar,et al.  Statistical Relational Learning for Link Prediction , 2003 .

[16]  B. Schölkopf,et al.  A Regularization Framework for Learning from Graph Data , 2004, ICML 2004.

[17]  T. Valletti,et al.  Mobile Termination: What is the “Right” Charge?* , 2005 .

[18]  M. Armstrong Competition in Two-Sided Markets ¤ , 2005 .

[19]  Arun Sundararajan,et al.  Local Network Effects and Network Structure , 2005 .