Modeling the Dynamics of Network Technology Adoption and the Role of Converters

• Published in: IEEE/ACM transactions on networking 2010-12, Vol.18 (6), p.1793-1805
• Main Authors: Soumya Sen, Youngmi Jin, Guérin, Roch, Hosanagar, Kartik
• Format: Article
• Language: English
• Subjects: Consumer electronics; Convergence; Converters; Cost function; Degradation; diffusion; Displacement; dynamics; Economics; equilibrium; externality; Internet; Interoperability; IP networks; Market penetration; Markets; Mathematical models; Networks; Paper technology; Robustness; Studies; Systems engineering and theory; Technology adoption; Testing; Utilities
• ISSN: 1063-6692; 1558-2566
• DOI: 10.1109/TNET.2010.2048923

New network technologies constantly seek to displace incumbents. Their success depends on technological superiority, the size of the incumbent's installed base, users' adoption behaviors, and various other factors. The goal of this paper is to develop an understanding of competition between network technologies and identify the extent to which different factors, in particular converters (a.k.a. gateways), affect the outcome. Converters can help entrants overcome the influence of the incumbent's installed base by enabling cross-technology interoperability. However, they have development, deployment, and operations costs and can introduce performance degradations and functionality limitations, so that if, when, why, and how they help is often unclear. To this end, the paper proposes and solves a model for adoption of competing network technologies by individual users. The model incorporates a simple utility function that captures key aspects of users' adoption decisions. Its solution reveals a number of interesting and at times unexpected behaviors, including the possibility for converters to reduce overall market penetration of the technologies and to prevent convergence to a stable state, something that never arises in their absence. The findings were tested for robustness, e.g., different utility functions and adoption models, and found to remain valid across a broad range of scenarios.