图书情报工作

图书情报工作 >

2024 , Vol. 68 >Issue 10: 81 - 96

DOI: https://doi.org/10.13266/j.issn.0252-3116.2024.10.008

情报研究

弱链接突变视角下的技术机会识别研究

  • 吴柯烨 ,
  • 孙建军 ,
  • 张力 ,
  • 赵源
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  • 1 南京大学数据智能与交叉创新实验室 南京 210023;
    2 南京大学信息管理学院 南京 210023
吴柯烨，博士研究生；孙建军，教授，博士生导师，通信作者，E-mail：sjj@nju.edu.cn；张力，博士后；赵源，博士后。

收稿日期: 2023-10-12

  修回日期: 2024-01-31

  网络出版日期: 2024-06-04

基金资助

本文系国家社会科学基金重大项目“前沿交叉领域识别与融合创新路径预测方法研究”(项目编号：23&ZD225) 研究成果之一。

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A Study of Technology Opportunity Identification Based on Sudden Change of Weak Ties

  • Wu Keye ,
  • Sun Jianjun ,
  • Zhang Li ,
  • Zhao Yuan
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  • 1 Laboratory for Data Intelligence and Cross-Innovation of Nanjing University, Nanjing 210023;
    2 School of Information Management, Nanjing University, Nanjing 210023

Received date: 2023-10-12

  Revised date: 2024-01-31

  Online published: 2024-06-04

Supported by

This work is supported by the major program of National Social Science Fund of China titled “Research on forecasting methods for identifying and converging innovation paths in frontier cross-cutting fields” (Grant No. 23&ZD225).

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摘要

[目的/意义]随着技术迭代周期缩短，技术机会识别对于企业创新、国家发展均具有重要的管理和决策意义。[方法/过程]受弱链接理论启发，构建一个面向特定技术领域的基于弱链接突变的技术机会识别框架，将技术网络中链路状态由弱变强的技术组合关系识别为技术机会。首先，构建IPC共现网络并利用链路强度指标识别技术弱链接；然后，采用集成学习算法预测弱链接中具备突变潜力的有效弱链接；最后，将预测结果重构为技术机会网络，并采用社区检测算法得到未来技术机会发展主题。[结果/结论]以生物医药领域1950年至2020年4 104 839篇专利为研究对象的实证研究表明：①大部分技术弱链接在技术演化过程中被淘汰，只有少部分技术弱链接有机会突破成为技术强链接；②弱链接的链路强度、节点同配性是预测弱链接有效性的关键特征，表明技术弱链接并非越“弱”，其创新效率就越高，而是要经过充分论证并且与当前技术热点形成联动，才有可能实现突变；③集成学习模型不仅能够显著提升各技术阶段的有效弱链接预测精度，还可以有机融合各技术发展阶段的弱链接突变规律为预测未来技术机会提供可靠线索。最终，经过生物医药领域的多源技术发展报告验证，证实所识别4个技术机会主题均符合技术实际发展情况且具备前瞻性，证明该框架的有效性。

关键词： 技术机会识别; 弱链接; 链路强度; 集成学习; 社区检测

本文引用格式

吴柯烨 , 孙建军 , 张力 , 赵源 . 弱链接突变视角下的技术机会识别研究[J]. 图书情报工作, 2024 , 68(10) : 81 -96 . DOI: 10.13266/j.issn.0252-3116.2024.10.008

Abstract

[Purpose/Significance] As the technology iteration cycle shortens, the identification of technological opportunities has important management and decision-making significance for corporate innovation and national development. [Method/Process] Inspired by the weak tie theory, this study constructed a technology opportunity identification framework based on the sudden change of weak tie, which recognized the state of technology links in technology networks that may change from weak to strong as technology opportunities. This framework was oriented to specific technical fields. It first built an IPC co-occurrence network and used link strength indicators to identify technical weak links. It further used an ensemble learning algorithm to predict effective weak ties with breakthrough potential among the weak links. Finally, the prediction results were reconstructed into a technology opportunity network, and the future technology opportunity development theme was obtained based on the community detection algorithm. [Result/Conclusion] An empirical study based on 4,104,839 patents in biomedicine from 1950 to 2020 shows that: ① Most technical weak links are eliminated in the process of technology evolution, and only a small number of technical weak links have the opportunity to break through and become technologies strong links. ②The link strength and node compatibility of weak links are key features for predicting the effectiveness of weak links, which shows that the “weaker” the technical weak link is, the higher the innovation efficiency will be. Instead, it needs to be fully demonstrated and linked to current technology hotspots to achieve the breakthrough. ③The integrated learning model can not only significantly improve the prediction accuracy of effective weak links in each technology stage, but also organically integrate the emergence rules of effective weak links in each technology development stage to provide reliable clues for predicting future technology opportunities. The four technology opportunity themes in the biomedical field finally identified are verified by multi-source technology development reports, which are all in line with the actual development of technology, proving the effectiveness of the framework of this paper.

Key words： technology opportunity identification; weak ties; link strength; ensemble learning; community detection

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