颠覆性技术识别研究进展综述  

Review of Studies Identifying Disruptive Technologies

作　　者：张金柱[1] 王秋月 仇蒙蒙 Zhang Jinzhu;Wang Qiuyue;Qiu Mengmeng(School of Economics and Management,Nanjing University of Science and Technology,Nanjing 210094,China)

机构地区：[1]南京理工大学经济管理学院,南京210094

出　　处：《数据分析与知识发现》2022年第7期12-31,共20页Data Analysis and Knowledge Discovery

基　　金：国家自然科学基金(项目编号:71974095);江苏省研究生科研与实践创新计划项目(项目编号:SJCX21_0168)的研究成果之一。

摘　　要：【目的】对颠覆性技术识别相关文献进行综述,发现研究主题,总结研究重点和发展方向,形成研究框架并展望。【文献范围】利用颠覆性技术的相关关键词在CNKI和Web of Science中检索,获取2011-2020年间的1 974篇论文进行定量分析,对2001-2020年间的61篇相关论文进行定性解读。【方法】首先,通过定量分析得到研究主题和研究方向;其次,选取有代表性的高被引论文和最新论文进行研读,对研究方法进行总结和述评;最后,根据分析结果梳理研究框架,预测未来发展趋势。【结果】颠覆性技术识别在信息技术、医疗、化工、高端制造等领域应用较多,形成基于技术本身、市场产品、科技信息挖掘、外部环境等视角下的多种识别指标和方法;构建含理论基础、识别方法、结果评判三部分内容的识别框架,并展望未来发展趋势。【局限】数据范围还不够广,社会、经济等宏观指标综述不够全面。【结论】颠覆性技术识别的多学科交叉性日益明显,定量研究已成为主流,部分指标定量计算方式尚需明确,基于深度学习的指标非线性组合正成为趋势。[Objective] This paper reviews the literature identifying disruptive technologies, aiming to examine research topics and development trends, as well as establish a framework for further studies. [Coverage] We searched Chinese and English papers from CNKI and Web of Science with relevant keywords. We retrieved 1 974 papers published between 2011 and 2020 for quantitative analysis, and 61 papers published between 2001 and 2020 for qualitative analysis. [Methods] First, we identified the popular topics and development trends through quantitative analysis. Then, we examined the highly cited papers and the latest literature to review their research methods. Finally, we built a framework based on the results of quantitative and qualitative analysis which also predicted future trends. [Results] Studies identifying disruptive technologies were more popular in the fields of information technology, medical treatment, chemical industry, and high-end manufacturing. They included multiple-methodology from the perspectives of technologies themselves, products, sci-tech information mining,and external environment. We established three frameworks for disruptive technology identification and explored some future developments. [Limitations] More research on macro indicators, such as society-and economyrelated issues, need to be reviewed comprehensively. [Conclusions] The research on disruptive technology identification has become inter-disciplinary, which include more quantitative methodology and the nonlinear algorithms based on deep learning.

关 键 词：颠覆性技术 识别方法 研究进展 综述 

分 类 号：G350[文化科学—情报学]