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“…then came Cisco, and the rest is history”: a ‘history friendly’ model of the Local Area Networking industry

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Abstract

We study the role that switching costs, compatibility, and mergers and acquisitions play in influencing the evolution of a multi-market industry. By looking at the case of the Local Area Networking industry, we propose a ‘history friendly model’ to replicate its evolution during the 1990s. Our model explains how a firm can start from a dominant position in one of the existing markets and exploit switching costs and compatibility to enter a new market. Mergers and acquisitions also play an important role as the new market is pioneered by a start-up, which is soon acquired by the dominant incumbent. As a result of the acquisition, the acquiring firm becomes the leader also in the new market.

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Notes

  1. Malerba et al.(1999) presents a model of the evolution of the computer industry. Other sectors that have been studied using this approach include pharmaceuticals and biotechnology (Malerba and Orsenigo 2002), and the DRAM industry (Kim and Lee 2003).

  2. Existing contributions have provided detailed evidence on the early history of the industry (von Burg 2001) and on the events that have characterized the most recent period especially concerning the optical networking market of the data communication industry (Carpenter et al. 2003).

  3. Multi-protocol routers embody several algorithms and support several communication protocols to deliver data packets intelligently to their destination. Access-routers provide customers with an interface between their end stations and the network.

  4. In a similar move, Cabletron Systems introduced the ‘Spectrum’ network management software.

  5. Again, major competitors did not stand still. 3Com responded with the Superstack and Office Connect line of equipment, the former targeting big users, and the latter customers with smaller networks. Bay Networks and Cabletron Systems marketed an entire new product line: the BayStack and Smartswitch, respectively.

  6. In those cases in which there is a ‘tie’ (i.e., we observe the same probability of mergers for all pairs in a subset of manufacturers), the first pair is formed by the manufactures with the highest indexes associated to each of them. The second pair is composed by the manufactures with the highest indexes associated to each of them, excluding the manufacturers in the first pair and so on. This ‘tie-breaking’ rule is adopted to save on computational time. Since ties basically occur when the probability of mergers is 0, the tie-breaking rule is almost irrelevant.

  7. The assumption that p is exogenous may appear too extreme. Indeed, in most evolutionary models it is common practice to assume that the probability to innovate is endogenous and depends (positively) on previous innovativeness, profits or firm size. While this assumption is made for simplifying purposes, we observe that, from a theoretical view point, incentives for product innovation depend less than process innovation on firm size (Cohen and Klepper 1996).

  8. This particular utility function borrows from Adner and Levinthal (2001), and many others, the idea that consumers differ in their marginal evaluation of quality. Differently from Adner and Levinthal, however, we do not explicitly consider prices. It can be noted, however, that Eq. (5) can be considered as a reduced form of a case where price is determined by a fixed mark-up over cost, and unit cost of production is quadratic in quality.

  9. The exponential rule for representing consumer choices has been often used in models of industrial and market dynamics (Weisbuch et al. 2000).

  10. The values of parameters that remain constant across simulations are reported in the Appendix.

  11. The results on the market shares of the leader firm across periods and markets mirror those just discussed for concentration (see Table S1 in Supplementary Material). For a given scenario, the leader’s shares increase over time in both markets 1 and 2, while the leader in market 3 has a lower share. The impact of switching cost level and compatibility is the same as in the case of concentration.

  12. We also considered the effect of our scenarios on another set of aspects that are relevant for describing the evolution of the LAN industry, namely: the frequency of runs in which the firm that was the first to introduce a product of type 3 was a start-up; the frequency of runs in which the start-up that pioneered a product of type 3 ends up being acquired and/or merged; the frequency of runs in which the start-up that starts the new market becomes the leader of the market. The detailed results of this exercise are available in Table S2 of the Supplementary Material.

  13. See Cowan and Foray (2002) and Tucker (1999) for a detailed discussion of this point.

  14. Within the ‘possible worlds’ view of counterfactuals we find exercises of ‘comparative dynamics’ in which key parameters are varied. Within the ‘branching’ view of counterfactuals, instead, one can include those experiments trying to verify the effect of specific public policies (which can be located in time) on industrial evolution. See the review of Garavaglia (2010), Section 4.2, for a selection of counterfactual exercises implemented in the literature.

  15. The detailed results of this exercise are presented in Table S3 of the Supplementary Material.

  16. The importance of switching costs is also confirmed by an experiment (nor reported here) in which, for each firm, the switching cost parameter was positive with probability 0.5, and null otherwise. Out of 100 simulations, it turns out that 97 % of the times a market leader emerges with positive switching costs in market 1, 94 % of the times in market 2, and 67 % of the times in market 3.

  17. See Table S4 of the Supplementary Material.

  18. It must be highlighted that M&As may not be needed if switching costs are high and compatibility low. In this case, first mover advantages (in a market, then extended to others through ‘system closure’) can be so strong as to lead to dominance without mergers.

  19. See Table S5 of the Supplementary Material.

  20. Fontana and Vezzulli (2015) show that technological leaders in the LAN industry were more likely to be persistent product innovators in their market. However, they do not investigate the relationship between innovation persistence and market leadership.

  21. We also observe that, although the effect is not particularly strong, our results that the presence of demand heterogeneity tends to mitigate market concentration are consistent with some recent works on the dynamics of some industries characterized by the presence of sub-markets and the absence of shake-outs (Klepper and Thompson 2006; Buenstorf and Klepper 2010).

  22. Moreover, based on the original HF exercise, extensions of the model may be used to investigate issues of more general interest. For an example of this strategy, see Garavaglia et al. (2012).

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Acknowledgments

we have greatly benefited from the comments of the editor and two anonymous referees. Shane Greenstein, the late Steven Klepper, William Lazonick, and Franco Malerba commented on a very early version of this paper. Preliminary versions were also presented at the 2006 Schumpeter Society Conference in Nice, at the October 2008 DIME workshop on ‘Demand, Innovation, and Industrial Dynamics’ in Milano, and at a seminar at CIRCLE, Lund University in December 2012. The usual disclaimers apply.

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Authors and Affiliations

  1. Department of Economics & Management, University of Pavia, Via San Felice 5, 27100, Pavia, Italy

    Roberto Fontana

  2. CRIOS - Bocconi University, Via Sarfatti 25, 20139, Milan, Italy

    Roberto Fontana & Lorenzo Zirulia

  3. Department of Economics, University of Bologna, Bologna, Italy

    Lorenzo Zirulia

  4. RCEA, Rimini, Italy

    Lorenzo Zirulia

Authors
  1. Roberto Fontana
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Correspondence to Roberto Fontana.

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Appendix 1

Appendix 1

Table 8 Numerical values for invariant parameters
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Fontana, R., Zirulia, L. “…then came Cisco, and the rest is history”: a ‘history friendly’ model of the Local Area Networking industry. J Evol Econ 25, 875–899 (2015). https://doi.org/10.1007/s00191-015-0422-8

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