识别新兴技术的多维指标:技术知识流动的视角

IF 3.4 2区 管理学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Man Jiang , Siluo Yang , Qiang Gao
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引用次数: 0

摘要

新兴技术(ETs)的识别对于推动技术创新至关重要。然而,目前的方法未能充分阐明新兴技术的创新机制,也缺乏整合罗托洛提出的五个属性的一致视角。本文从技术知识流(TKF)的角度出发,提出了一个创新的术语级框架来识别和理解 ET。通过对 TKF 的全面剖析,包括知识的吸收、增长和扩散等方面,我们构建并阐释了反映 ET 特性的多维指标,包括相对快速增长、根本新颖性、连贯性、突出影响以及不确定性和模糊性。通过对数字医疗专利数据集的分析,我们的框架被证明能有效评估新兴得分,并在术语层面准确定位ET,明确其技术成分和功效。这有利于技术开发人员克服技术难题,也有利于战略决策者管理知识产权以获得竞争优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multidimensional indicators to identify emerging technologies: Perspective of technological knowledge flow

The identification of emerging technologies (ETs) is pivotal for advancing technological innovation. However, current methods fail to sufficiently clarify ETs' innovation mechanisms and lack a consistent perspective to integrate the five attributes proposed by Rotolo. This paper presents an innovative term-level framework to identify and comprehend ETs through the perspective of technological knowledge flow (TKF). By dissecting TKF comprehensively, encompassing aspects of knowledge absorption, growth, and diffusion, we construct and explicate multidimensional indicators reflective of ETs' attributes, including relatively rapid growth, radical novelty, coherence, prominent impact, as well as uncertainty and ambiguity. Through the analysis of digital medical patent dataset, our framework proves effective in assessing emergent scores and pinpointing ETs with specificity at the term level, clarifying their technological components and efficacy. This is beneficial for technology developers to overcome technical difficulties and strategic decision makers to manage IP for competitive advantage.

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来源期刊
Journal of Informetrics
Journal of Informetrics Social Sciences-Library and Information Sciences
CiteScore
6.40
自引率
16.20%
发文量
95
期刊介绍: Journal of Informetrics (JOI) publishes rigorous high-quality research on quantitative aspects of information science. The main focus of the journal is on topics in bibliometrics, scientometrics, webometrics, patentometrics, altmetrics and research evaluation. Contributions studying informetric problems using methods from other quantitative fields, such as mathematics, statistics, computer science, economics and econometrics, and network science, are especially encouraged. JOI publishes both theoretical and empirical work. In general, case studies, for instance a bibliometric analysis focusing on a specific research field or a specific country, are not considered suitable for publication in JOI, unless they contain innovative methodological elements.
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