图书情报工作

图书情报工作 >

2022 , Vol. 66 >Issue 10: 4 - 18

DOI: https://doi.org/10.13266/j.issn.0252-3116.2022.10.001

专题：创新驱动战略下的技术预测方法与实践

技术预测研究现状、趋势及未来思考:数据分析视角

  • 张硕 ,
  • 汪雪锋 ,
  • 乔亚丽 ,
  • 刘玉琴
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  • 1. 北京理工大学管理与经济学院 北京 100081;
    2. 北京印刷学院新闻出版学院 北京 102600
张硕,博士研究生;乔亚丽,博士研究生;刘玉琴,高级工程师,博士。

收稿日期: 2021-11-19

  修回日期: 2022-01-16

  网络出版日期: 2022-06-01

基金资助

本文系国家自然科学基金项目"生物医学领域潜在颠覆性技术识别方法研究"(项目编号:72074020)研究成果之一。

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Research Status, Trends and Future Thinking of Technology Forecasting: From the Perspective of Data Analytics

  • Zhang Shuo ,
  • Wang Xuefeng ,
  • Qiao Yali ,
  • Liu Yuqin
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  • 1. School of Management and Economics, Beijing Institute of Technology, Beijing 100081;
    2. School of Journalism and Publishing, Beijing Institute of Graphic Communication, Beijing 102600

Received date: 2021-11-19

  Revised date: 2022-01-16

  Online published: 2022-06-01

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摘要

[目的/意义]基于数据分析视角,从研究数据以及研究方法的变迁出发,对技术预测研究做出系统性分析。[方法/过程]为厘清发展脉络,本研究将基于数据分析的技术预测研究划分为萌芽阶段(1981-1991年)、成长阶段(1992-2010年)、扩张阶段(2011-2017年)和瓶颈阶段(2018年至今),通过综合运用文献计量法和知识图谱分析工具,对不同阶段的研究前沿进行深入分析。[结果/结论] 研究表明,技术预测一直朝着多层次、系统化的方向发展,但尚未完成"技术可能如何发展"到复杂环境下"技术应该如何发展"的跨越,而搭建科学数据共享平台,构建智能化分析软件以及发挥政府的宏观调控作用将是未来关注的焦点。

关键词： 技术预测; 数据分析; 文献计量学; 研究前沿; ITGInsight

本文引用格式

张硕 , 汪雪锋 , 乔亚丽 , 刘玉琴 . 技术预测研究现状、趋势及未来思考:数据分析视角[J]. 图书情报工作, 2022 , 66(10) : 4 -18 . DOI: 10.13266/j.issn.0252-3116.2022.10.001

Abstract

[Purpose/Significance] From the change of the research data and research methods, this paper makes a systematic analysis of technology forecasting research from the perspective of data analytics.[Method/Process] In order to clarify its development process, this research divided the technology forecasting research based on data analytics into four stages of nascent phase (1981-1991), growth phase (1992-2010), expansion phase (2011-2017) and bottleneck phase (2018-present), and made an in-depth analysis of the research fronts under each stage through the comprehensive use of bibliometrics and knowledge map analysis tools.[Result/Conclusion] The results show that technology forecasting has been moving towards a multi-level and systematic direction, but it has not yet completed the leap from 'how technology may develop' to 'how technology should develop' in complex environments. Building a scientific data sharing platform and intelligent analysis software and giving full play to the role of government macro-control will be the focus of future attention.

Key words： technology forecasting; data analytics; bibliometrics; research fronts; ITGInsight

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