Identifying Promising Technologies Considering Technology Convergence: A Patent-Based Machine-Learning Approach

IF 4.6 3区 管理学 Q1 BUSINESS
Jinhong Kim;Youngjung Geum
{"title":"Identifying Promising Technologies Considering Technology Convergence: A Patent-Based Machine-Learning Approach","authors":"Jinhong Kim;Youngjung Geum","doi":"10.1109/TEM.2024.3477508","DOIUrl":null,"url":null,"abstract":"With drastic changes in technology and its converging power in new product development, technology convergence has long been considered imperative in the innovation literature. Despite these efforts, previous articles neglected the importance of technology convergence in identifying promising technologies. To address this limitation, this article assumes that patents with high mediating power for subsequent technology convergence are likely to be promising. For this purpose, this article proposes the concept of convergence distance, which is measured by the differences in IPCs in backward and forward citations of patents, and defines it as the mediating power of technology convergence. Three indicators are defined: convergence distance, convergence intensity, and convergence diversity. Using these convergence-related indicators, we developed a machine-learning model to predict promising technologies. Consequently, the models with new evolution indicators outperformed the original models. Moreover, our suggested indicators turned out to be very important for predicting promising technologies, implying that the mediating power of technology convergence is very important for predicting future promising technologies and should be considered very significant for technology opportunity discovery.","PeriodicalId":55009,"journal":{"name":"IEEE Transactions on Engineering Management","volume":"71 ","pages":"15096-15109"},"PeriodicalIF":4.6000,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Engineering Management","FirstCategoryId":"91","ListUrlMain":"https://ieeexplore.ieee.org/document/10712649/","RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BUSINESS","Score":null,"Total":0}
引用次数: 0

Abstract

With drastic changes in technology and its converging power in new product development, technology convergence has long been considered imperative in the innovation literature. Despite these efforts, previous articles neglected the importance of technology convergence in identifying promising technologies. To address this limitation, this article assumes that patents with high mediating power for subsequent technology convergence are likely to be promising. For this purpose, this article proposes the concept of convergence distance, which is measured by the differences in IPCs in backward and forward citations of patents, and defines it as the mediating power of technology convergence. Three indicators are defined: convergence distance, convergence intensity, and convergence diversity. Using these convergence-related indicators, we developed a machine-learning model to predict promising technologies. Consequently, the models with new evolution indicators outperformed the original models. Moreover, our suggested indicators turned out to be very important for predicting promising technologies, implying that the mediating power of technology convergence is very important for predicting future promising technologies and should be considered very significant for technology opportunity discovery.
考虑技术融合,识别有前途的技术:基于专利的机器学习方法
随着技术的急剧变化及其在新产品开发中的融合力量,技术融合在创新文献中一直被认为是势在必行的。尽管做出了这些努力,但以往的文章忽视了技术融合在识别有前途技术方面的重要性。为了解决这一局限性,本文假定对后续技术融合具有高中介力的专利很可能是有前途的技术。为此,本文提出了趋同距离的概念,用专利前后向引用的 IPC 差异来衡量,并将其定义为技术趋同的中介力。本文定义了三个指标:趋同距离、趋同强度和趋同多样性。利用这些与趋同相关的指标,我们开发了一个机器学习模型来预测有前途的技术。结果,采用新演化指标的模型优于原始模型。此外,我们提出的指标对于预测有前途的技术非常重要,这意味着技术融合的中介作用对于预测未来有前途的技术非常重要,对于发现技术机会也非常重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
IEEE Transactions on Engineering Management
IEEE Transactions on Engineering Management 管理科学-工程:工业
CiteScore
10.30
自引率
19.00%
发文量
604
审稿时长
5.3 months
期刊介绍: Management of technical functions such as research, development, and engineering in industry, government, university, and other settings. Emphasis is on studies carried on within an organization to help in decision making or policy formation for RD&E.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信