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突变视角下潜在颠覆性技术识别与分析方法研究 被引量:15

Research on the Methods of Identifying and Analyzing Potential Disruptive Technologies from the Perspective of Catastrophe
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摘要 [目的/意义]将社会网络分析与突变理论相结合,提出识别潜在颠覆性技术的新方法,帮助企业获得市场、经济的先行优势,同时有助于加强政府对重点技术的前期引导作用。[方法/过程]首先,基于文本三元组结构,构建语义信息层面的技术共现网络。其次,基于社区网络结构和颠覆性技术特征,提出社区网络关系强度和对外资源利用力两个指标,利用突变理论模型识别易突变社区。最后,对易突变社区内节点进行主题词突发性监测和词频异常监测确定技术突发可能性,以此识别潜在颠覆性技术。[结果/结论]以造血干细胞领域2001—2018年论文数据作为分析对象,验证了方法的有效性,发现“建立完善的关于免疫缺陷疾病及相关移植治疗手段的临床研究模型”是该领域的潜在颠覆性技术。 [Purpose/significance]Combining social network analysis with catastrophe theory,this paper proposes a new method to identify potential disruptive technologies,so as to help enterprises gain the early advantage of market and economy,and at the same time,to strengthen the government’s early guiding role in key technologies.[Method/process]Firstly,based on the text triplet structure,the semantic information level technical co-occurrence network is constructed.Secondly,based on the characteristics of social network structure and disruptive technology,two indicators of social network relationship strength and external resource utilization are proposed,and a catastrophe theoretical model is established to identify the communities that are prone to catastrophe.Finally,word burst monitoring and abnormal word frequency monitoring are carried out on nodes in the catastrophe-prone community to determine the possibility of technology burst,so as to identify potential disruptive technologies.[Result/conclusion]The data from the hematopoietic stem cell field from 2001 to 2018 were analyzed to verify the effectiveness of the method.This study found that the establishment of a perfect clinical research model for immunodeficiency diseases and related transplantation therapy is a potential disruptive technology in this field.
出处 《情报理论与实践》 CSSCI 北大核心 2022年第3期157-164,156,共9页 Information Studies:Theory & Application
基金 国家社会科学基金青年项目“创新过程视角下的颠覆性技术识别研究”的成果之一,项目编号:19CTQ028。
关键词 颠覆性技术 突变理论 语义信息 社会网络分析 三元组结构 disruptive technology catastrophe theory semantic information social network analysis triplet structure
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