{"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":null,"pages":null},"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.
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.