避免直接联系的陷阱:衡量科学对专利影响的创新驱动方法

IF 3.4 2区 管理学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Nils M. Denter, Joe Waterstraat, Martin G. Moehrle
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引用次数: 0

摘要

科学知识在新技术知识的产生中起着重要作用。我们提出了一种新的创新驱动的方法来衡量科学对专利的影响。我们克服了以前基于引用或语义相似度的方法的缺点,这两种方法都表示文档之间的直接联系。我们将专利新颖性测量与特定于技术的科学词典相结合,这使我们能够通过稳定的间接联系来测量专利与科学的接近程度。我们将我们的指标“科学驱动的新颖性”应用于RFID技术的试验台,并通过专家调查来证实其有效性。随后,我们检验了科学对专利价值的影响,发现科学影响增加了专利的平均价值。我们的研究结果提出了几点启示。对于学术界,我们建议不要仅仅依靠分析论文和专利之间的直接联系来确定科学对技术的影响。对于管理层来说,我们提供了一种新的工具来评估专利的科学影响,从而评估他们公司自己的专利组合以及第三方专利组合的价值。使用文本作为数据,该工具在非常早期的阶段是可行的,并且可以帮助技术管理做出是否进行的决策。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Avoiding the pitfalls of direct linkage: A novelty-driven approach to measuring scientific impact on patents
Scientific knowledge plays a major role in the generation of new technological knowledge. We present a new novelty-driven approach to measure the influence of science on patents. We overcome the weaknesses of previous methods based on either citations or semantic similarities, both representing direct linkages between documents. We combine patent novelty measurement with technology-specific, scientific dictionaries, which allow us to measure a patent's nearness to science by stable indirect linkages. We apply our indicator “science-driven novelty” to the testbed of RFID technology and confirm its validity by conducting an expert survey. Subsequently, we test how science impacts patent value, finding that scientific influence increases the average value of a patent. Our results suggest several implications. For academics, we recommend not relying solely on analyzing direct links between papers and patents to determine the influence of science on technology. For management, we provide a new tool to assess scientific influences in patents and thus the value of their company's own patent portfolio as well as the portfolios of third parties. Using text as data, the tool is viable at a very early stage and can be helpful in go/no-go decisions for technology management.
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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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