Modeling the Dynamics of Network Technology Adoption and the Role of Converters
Modeling the Dynamics of Network Technology Adoption and the Role of Converters
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DOI：
10.1109/tnet.2010.2048923
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发表时间：
2010-12
期刊：
影响因子：
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通讯作者：
S. Sen;Youngmi Jin;R. Guérin;K. Hosanagar
中科院分区：
文献类型：
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作者：
S. Sen;Youngmi Jin;R. Guérin;K. Hosanagar
新的网络技术不断寻求取代现有的网络技术。他们的成功取决于技术优势，现有的安装基础的规模，用户的采用行为，以及其他各种因素。本文的目标是了解网络技术之间的竞争，并确定不同因素，特别是转换器（又名：#21453;，影响结果。转换器可以通过实现跨技术互操作性来帮助进入者克服现有安装基础的影响。但是，它们具有开发、部署和运营成本，并且可能会导致性能下降和功能限制，因此它们是否、何时、为什么以及如何提供帮助往往不清楚。为此，本文提出并解决了一个模型，通过竞争网络技术的个人用户。该模型采用了一个简单的效用函数，捕捉用户的采用决策的关键方面。它的解决方案揭示了一些有趣的，有时是意想不到的行为，包括转换器可能会降低技术的整体市场渗透率，并阻止收敛到稳定状态，这在没有转换器的情况下永远不会发生。对研究结果进行了稳健性测试，例如，不同的效用函数和采用模型，并发现在广泛的场景中仍然有效。
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.