计算机科学
专利分析
成熟度(心理)
技术预测
技术进化
引用
透视图(图形)
数据科学
技术开发
领域(数学)
运筹学
人工智能
工程类
制造工程
数学
心理学
发展心理学
万维网
纯数学
作者
Ying Huang,Ruinan Li,Fang Zou,Lidan Jiang,Alan L. Porter,Lin Zhang
标识
DOI:10.1016/j.techfore.2022.121760
摘要
Technology development is a blend of inheritance and innovation that follows certain rules along a technology trajectory. This trajectory charts a technology's evolution and is among the best-supporting data for decision making. Technology life cycle (TLC) analysis, as one of the foundational topics in the field of technology management, is of vital importance for describing the evolutionary path of technology. However, most current methods simply rely on static patent indicators, which neglect the dynamic aspects of a technology's development. To overcome this limitation, we propose a framework of diverse characteristics and novel procedures for identifying the entire span of a technology's life cycle from a series of patent citation networks. After retrieving patent data with a well-defined search strategy, a sequence of patent citation networks is constructed year by year. Network attribute indicators are then calculated for each of the networks and used to inform an evolution model, which reveals the TLC. To illustrate the strengths and potential of this approach, we have taken additive manufacturing technology as an example for analysis. Through the framework, we are able to demonstrate the technology at various stages of its maturity and how it has changed over time, along with some areas of competitive advantage and promising future opportunities.
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