The chilling effects of network externalities Jacob Goldenberg a , Barak Libai b , Eitan Muller b,c, ⁎ a School of Business Administration, Hebrew University, Jerusalem, 91905, Israel b Leon Recanati Graduate School of Business Administration, Tel Aviv University, Tel Aviv, 69978, Israel c Leonard Stern School of Business, New York University, New York, NY 10012, United States abstract article info Article history: First received in 18, December 2007 and was under review for 5 months Area editor: Marnik G. Dekimpe Keywords: Agent-based models Contagion Net present value Network externalities New product growth Threshold levels Conventional wisdom suggests that network effects should drive faster market growth due to the bandwagon effect. However, as we show, network externalities may also create an initial slowdown effect on growth because potential customers wait for early adopters, who provide them with more utility, before they adopt. In this study, we explore the financial implications of network externalities by taking the entire network process into account. Using an agent-based as well as an aggregate-level model, and separating network effects from word of mouth, we find that network externalities have a substantial chilling effect on the net present value associated with new products. This effect may occur not only in a competitive framework, such as a competing standards scenario, but also in the absence of competition. Drawing on the collective action literature in order to relate network effects to individual consumer threshold levels, we find that the chilling effect is stronger with a small variability in the threshold distribution, and is especially affected by the process early on in the product life cycle. We also find a “hockey stick” growth pattern by empirically examining the growth of fax machines, CB radios, CD players, DVD players, and cellular services. © 2009 Elsevier B.V. All rights reserved. 1. Introduction How do network externalities affect the diffusion rate and the consequent economic value associated with a new product? Despite the sizeable academic literature on the dynamics of network goods markets, the answer to this question is not obvious. Network effects and network externalities exist when consumers derive utility from a product based on the number of other users; conventional wisdom suggests that such effects should drive faster market growth due to the bandwagon effect (Economides & Himmelberg, 1995; Rohlfs, 2001; Shapiro & Varian, 1999). Therefore, the rapid diffusion of fast-growing product categories has been attributed to network externalities (Doganoglu & Grzybowski, 2007). However, initial network effects may also have a chilling effect on growth due to the “wait-and-see” position adopted by consumers who derive little utility from an innovation that has few other adopters (Farrell & Saloner, 1986). Therefore, the growth of network goods may follow a two-stage process, that is, slow initial diffusion followed by a very fast growth stage (Rogers, 2003). The question remains as to the overall network effects with respect to the time it takes for an innovation to develop. This growth rate is of considerable managerial importance due to the time value of money, as acceleration in growth can translate into a sizeable difference in the Net Present Value (NPV) of an innovation. However, little is known about the NPV impact of network externalities with respect to the growth rate and the factors that drive it. This lack of knowledge is noteworthy given the growing interest in optimal product strategies for network goods. Various market entry strategies or reactions to market entry of network goods have been suggested in recent years (Lee & O'Connor, 2003; Montaguti, Kuester, & Robertson, 2002; Sun, Xie, & Cao, 2004). Such strategies typically have an impact on or are affected by the rate of growth of the network good in question. A change in the economic value of network goods due to the growth rate should therefore be taken into account in any such analysis. In this study, we analyze the fundamental effects of network exter- nalities on new product growth rates and consequent profitability. To do so, we combine a classical diffusion model similar to the Bass model with a social threshold model consistent with the collective action literature in sociology (Chwe, 1999; Granovetter, 1978; Macy, 1991). We apply two modeling approaches toward this goal. First, we apply an agent- based model to simulate the growth of the market for a given network good. This bottom-up approach enables us to understand how individual-level network good decisions aggregate to market phenom- ena. We compare the profitability of similar growth processes with and without network externalities and examine how market characteristics affect the difference. Second, we present an aggregate diffusion modeling approach that enables an analysis using market-level data that is analogous to our first estimation. Consistent with diffusion research, all analyses as well as profitability measures are conducted at the industry level. A brand-level analysis of this diffusion process, even without network externalities, is beyond the scope of this paper (Libai, Muller, & Peres, 2009a,b). Intern. J. of Research in Marketing 27 (2010) 4–15 ⁎ Corresponding author. Leonard Stern School of Business, New York University, New York, NY 10012, United States. E-mail addresses: msgolden@mscc.huji.ac.il (J. Goldenberg), libai@post.tau.ac.il (B. Libai), emuller@stern.nyu.edu (E. Muller). 0167-8116/$ – see front matter © 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.ijresmar.2009.06.006 Contents lists available at ScienceDirect Intern. J. of Research in Marketing journal homepage: www.elsevier.com/locate/ijresmar