| 基于RFM模型和随机行动者导向模型的技术机会识别 |
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| 引用本文： | 张振刚,罗泰晔.基于RFM模型和随机行动者导向模型的技术机会识别[J].情报学报,2021(1):53-61. |
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| 作者姓名： | 张振刚 罗泰晔 |
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| 作者单位： | 华南理工大学工商管理学院;广州数字创新研究中心;广东省科技革命与技术预见智库 |
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| 基金项目： | 国家社科基金重大项目“数据赋能激励制造业企业创新驱动发展及其对策研究”(18ZDA062)。 |
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| 摘 要： | 技术机会识别对于研发组织的创新管理具有重要意义,本文以人工智能领域2013—2015年的专利数据为例,提出了一种识别领域内技术机会的新方法。借鉴RFM(recency,frequency,monetary)模型的思路,使用K均值聚类法基于平均出现时间长度、出现频率和组合能力三个指标对知识元素进行聚类,进而发现了能够反映领域内技术发展方向的四个趋势性知识元素。使用随机行动者导向模型对知识网络的演化进行分析,在此基础上提出了发现知识元素的新技术机会的公式,并使用该公式识别出了趋势性知识元素潜在的技术机会。本研究利用人工智能领域2016—2018年的专利数据验证了所提出的方法的有效性,应用3D打印领域2014—2018年的专利数据验证了所提方法的稳健性。
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| 关 键 词： | RFM模型 技术机会识别 聚类 知识网络 随机行动者导向模型 |
Technology Opportunity Identification Based on RFM Model and Stochastic Actor-oriented Model |
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| Zhang Zhengang,Luo Taiye.Technology Opportunity Identification Based on RFM Model and Stochastic Actor-oriented Model[J].Journal of the China Society for Scientific andTechnical Information,2021(1):53-61. |
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| Authors: | Zhang Zhengang Luo Taiye |
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| Institution: | (School of Business Administration,South China University of Technology,Guangzhou 510640;Guangzhou Digital Innovation Research Center,Guangzhou 510640;Science and Technology Revolution and Technology Forecasting Think Tank of Guangdong Province,Guangzhou 510640) |
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| Abstract: | Technology opportunity identification is very significant to the innovation management of R&D organizations.Taking the patent data from 2013 to 2015 in the field of artificial intelligence as an example,this study proposes a novel method to identify technological opportunities.Using the idea of RFM(recency,frequency,monetary)model,we employed the K-means algorithm to cluster knowledge elements based on three indicators(i.e.,length of the average occurrence time,frequency of occurrence,and combination capacity)and yielded four knowledge elements that could reflect the direction of technology development in the field.The stochastic actor-oriented model was used to analyze the evolution of knowledge networks,and a formula was proposed to discover new technology opportunities for knowledge elements.Using this formula,we predicted the potential technology opportunities for the four yielded knowledge elements.The validity of the proposed method was tested by using the patent data of 2016-2018 in the field.The robustness of the proposed method was also tested by using the patent data from 2014 to 2018 in the field of 3D printing. |
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| Keywords: | RFM model technology opportunity identification clustering knowledge network stochastic actor-oriented models |
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