SPECIAL TOPIC:Technology Forecasting Method and Practice Under the Background of Innovation-driven Strategy

Radical Innovative Topic Identification from a Perspective of Dynamic Topic Network:Taking the Field of Blockchain as an Example

  • Chen Hongshu ,
  • Song Yahui ,
  • Jin Qianqian ,
  • Wang Xuefeng
Expand
  • School of Management and Economics, Beijing Institute of Technology, Beijing 100081

Received date: 2021-11-21

  Revised date: 2022-02-16

  Online published: 2022-06-01

Abstract

[Purpose/Significance]Radical innovation plays a key role in the development of science and technology. In the big data environment, the complex, multidimensional, and continuous evolutionary characteristics of science and technology development itself is becoming more observable than ever before. It is important to identify these topics from a dynamic perspective to provide solutions for countries, enterprises and universities to analyze radical innovation areas, allocate innovation resources rationally and seek innovation upgrades.[Method/Process] This paper integrated methods of topic modeling, word embedding algorithm, and complex network analysis to construct dynamic topic networks, and evaluate the structural characteristics of the topics within different time windows and the topic evolution states between these time windows. Based on dynamic topic networks, this paper then combined the novelty, mutation, impact and interdisciplinary characteristics of radical innovation to identify topics of radical innovation.[Result/Conclusion] Through the empirical study on blockchain, this paper recognizes that two topics with the most significant radical innovative characteristics are Neural Network and Edge Computing. With existing research of blockchain and the list of critical and emerging technologies issued by the National Science and Technology Council (NSTC) of the United States, this paper finally verifies the feasibility and effectiveness of the proposed method. However, further quantitative verification of the result of this paper, and identification of radical innovative topics by fusing multi-source data, require further research in the future.

Cite this article

Chen Hongshu , Song Yahui , Jin Qianqian , Wang Xuefeng . Radical Innovative Topic Identification from a Perspective of Dynamic Topic Network:Taking the Field of Blockchain as an Example[J]. Library and Information Service, 2022 , 66(10) : 45 -58 . DOI: 10.13266/j.issn.0252-3116.2022.10.004

References

[1] 郭小超, 雷婧, 冯银虎, 等. 基于知识图谱的国际突破性创新理论研究综述[J]. 科学管理研究, 2020, 38(1):20-26.
[2] 邵云飞, 詹坤, 吴言波. 突破性技术创新:理论综述与研究展望[J]. 技术经济, 2017, 36(4):30-37.
[3] HAIN D S, CHRISTENSEN J L. Capital market penalties to radical and incremental innovation[J]. European journal of innovation management, 2020, 23(2):291-313.
[4] 万宁. 浅析颠覆性创新、破坏性创新和突破性创新三者关系[J]. 商, 2015(30):122-123.
[5] ABERNATHY W J, UTTERBACK J M J T R. Patterns of innovation in technology[J]. Technology review, 1978, 80(7):41-47.
[6] MCDERMOTT C M, O'CONNOR G C. Managing radical innovation:an overview of emergent strategy issues[J]. Journal of product innovation management, 2002, 19(6):424-438.
[7] LEIFER R. Radical innovation:how mature companies can outsmarts upstatrs[M]. Brighton:Harvard Business Press, 2000.
[8] 张金柱, 张晓林. 基于专利科学引文的突破性创新识别研究述评[J]. 情报学报, 2016, 35(9):955-962.
[9] SCHOENMAKERS W, DUYSTERS G. The technological origins of radical inventions[J]. Research policy, 2010, 39(8):1051-1059.
[10] DAHLIN K B, BEHRENS D M. When is an invention really radical? defining and measuring technological radicalness[J]. Research policy, 2005, 34(5):717-737.
[11] YOON J, KIM K. Identifying rapidly evolving technological trends for R&D planning using SAO-based semantic patent networks[J]. Scientometrics, 2011, 88(1):213-228.
[12] SHIBATA N, KAJIKAWA Y, TAKEDA Y, et al. Detecting emerging research fronts based on topological measures in citation networks of scientific publications[J]. Technovation, 2008, 28(11):758-775.
[13] 刘亚辉, 许海云. 突破性创新早期识别与弱信号分析综述[J]. 图书情报工作, 2021, 65(4):89-101.
[14] ZHANG Y, ZHANG G, CHEN H, et al. Topic analysis and forecasting for science, technology and innovation:methodology with a case study focusing on big data research[J]. Technological forecasting and social change, 2016, 105:179-191.
[15] 李慧, 玄洪升. 专利视角下融合多属性的技术创新主题挖掘方法——以芯片领域专利为例[J]. 图书情报工作, 2020, 64(11):96-107.
[16] CHEN H, WANG X, PAN S, et al. Identify topic relations in scientific literature using topic modeling[J]. IEEE transactions on engineering management, 2021, 68(5):1232-1244.
[17] CHEN H, ZHANG G, ZHU D, et al. Topic-based technological forecasting based on patent data:a case study of Australian patents from 2000 to 2014[J]. Technological forecasting and social change, 2017, 119:39-52.
[18] SUOMINEN A, TOIVANEN H. Map of science with topic modeling:comparison of unsupervised learning and human-assigned subject classification[J]. Journal of the Association for Information Science and Technology, 2016, 67(10):2464-2476.
[19] JUNG S, YOON W C. An alternative topic model based on common interest authors for topic evolution analysis[J]. Journal of informetrics, 2020, 14(3):101040.
[20] MIKOLOV T, SUTSKEVER I, CHEN K, et al. Distributed representations of words and phrases and their compositionality[C]//Proceedings of the 26th international conference on neural information processing systems. New York:ACM, 2013:3111-3119.
[21] ZHANG Y, LU J, LIU F, et al. Does deep learning help topic extraction? a kernel k-means clustering method with word embedding[J]. Journal of informetrics, 2018, 12(4):1099-1117.
[22] CHEN H, JIN Q, WANG X, et al. Profiling academic-industrial collaborations in bibliometric-enhanced topic networks:a case study for digitalization research[J]. Technological forecasting and social change, 2022, 175:121402.
[23] DEWAR R D, DUTTON J E. The adoption of radical and incremental innovations:an empirical analysis[J]. Management science, 1986, 32(11):1422-1433.
[24] LI H, ZHANG Q, ZHENG Z. Research on enterprise radical innovation based on machine learning in big data background[J]. Journal of supercomputing, 2020, 76(5):3283-3297.
[25] 曹艺文, 许海云, 武华维, 等. 基于引文曲线拟合的新兴技术主题的突破性预测——以干细胞领域为例[J]. 图书情报工作, 2020, 64(5):100-113.
[26] ZHOU K Z, YIM C K, TSE D K. The effects of strategic orientations on technology- and market-based breakthrough innovations[J]. Journal of marketing, 2005, 69(2):42-60.
[27] 许海云, 刘亚辉, 罗瑞. 突破性科学创新早期识别研究综述[J]. 情报理论与实践, 2021, 44(4):198-205.
[28] ARTS S V R. The technological origins and novelty of breakthrough inventions[C]//35th DRUID celebration conference. Spain:Barcelona, 2013:1-30.
[29] 周磊, 杨威, 张玉峰. 基于专利挖掘的突破性创新识别框架研究[J]. 情报理论与实践, 2016, 39(9):73-76,46.
[30] 黄鲁成, 蒋林杉, 吴菲菲. 萌芽期颠覆性技术识别研究[J]. 科技进步与对策, 2019, 36(1):10-17.
[31] PONOMAREV I, LAWTON B K, WILLIAMS D E, et al. Breakthrough paper indicator 2.0:can geographical diversity and interdisciplinarity improve the accuracy of outstanding papers prediction?[J]. Scientometrics, 2014, 100(3):755-765.
[32] KLEINBERG J. Bursty and hierarchical structure in streams[J]. Data mining and knowledge discovery, 2003, 7(4):373-397.
[33] 张金柱, 张晓林. 基于被引科学知识主题突变的突破性创新识别[J]. 现代图书情报技术, 2016(Z1):42-50.
[34] CHEN C, CHEN Y, HOROWITZ M, et al. Towards an explanatory and computational theory of scientific discovery[J]. Journal of informetrics, 2009, 3(3):191-209.
[35] AHUJA G, LAMPERT C M. Entrepreneurship in the large corporation:a longitudinal study of how established firms create breakthrough inventions[J]. Strategic management journal, 2001, 22(6/7):521-543.
[36] 张军. 破坏性创新的特征分析[J]. 商场现代化, 2007(27):76.
[37] 张栋. 面向2035年的突破性创新测度、识别与预测[J]. 中国科技论坛, 2020(8):11-14.
[38] 庄子银, 贾红静, 肖春唤. 突破性创新研究进展[J]. 经济学动态, 2020(9):145-160.
[39] DELLA MALVA A, KELCHTERMANS S, LETEN B, et al. Basic science as a prescription for breakthrough inventions in the pharmaceutical industry[J]. Journal of technology transfer, 2015, 40(4):670-695.
[40] DESS G G P S D. Porter's generic strategies as determinants of strategic group membership and organizational performance[J]. Academy of management journal, 1984, 27(3):467-488.
[41] DOSI G. Technological paradigms and technological trajectories:a suggested interpretation of the determinants and directions of technical change[J]. Research policy, 1982, 11(3):147-162.
[42] 付玉秀, 张洪石. 突破性创新:概念界定与比较[J]. 数量经济技术经济研究, 2004(3):73-83.
[43] ANDERSON P, TUSHMAN M L. Technological discontinuities and dominant designs:a cyclical model of technological change[J]. Administrative science quarterly, 1990, 35(4):604-633.
[44] 李良德, 陈劲, 莫昕玮. 突破性创新管理模式研究[J]. 中外科技信息, 2001(11):38-41.
[45] WANG X, WANG Z, HUANG Y, et al. Identifying R&D partners through subject-action-object semantic analysis in a problem & solution pattern[J]. Technology analysis & strategic management, 2017, 29(10):1167-1180.
[46] 胡正银, 方曙. 专利文本技术挖掘研究进展综述[J]. 现代图书情报技术, 2014(6):62-70.
[47] BLEI D M. Probabilistic topic models[J]. Communications of the ACM, 2012, 55(4):77-84.
[48] DE BATTISTI F, FERRARA A, SALINI S. A decade of research in statistics:a topic model approach[J]. Scientometrics, 2015, 103(2):413-433.
[49] WATTS R J, PORTER A L. Innovation forecasting[J]. Technological forecasting and social change, 1997, 56(1):25-47.
[50] 赵蓉英, 郭凤娇, 赵月华. 科学计量学主流研究领域与热点前沿研究[J]. 图书情报工作, 2015, 59(2):66-74.
[51] TORTORIELLO M, MCEVILY B, KRACKHARDT D. Being a catalyst of innovation:the role of knowledge diversity and network closure[J]. Organization science, 2015, 26(2):423-438.
[52] 张金柱, 张晓林. 利用引用科学知识突变识别突破性创新[J]. 情报学报, 2014, 33(3):259-266.
[53] 卢超, 侯海燕, DING Y, 等. 国外新兴研究话题发现研究综述[J]. 情报学报, 2019, 38(1):97-110.
[54] GRIFFITHS T L, STEYVERS M. Finding scientific topics[J]. Proceedings of the National Academy of Sciences of the United States of America, 2004, 101:5228-5235.
[55] WEI X, CROFT W B. LDA-based document models for ad hoc retrieval[C]//The 29th annual international ACM SIGIR conference on researchand development in information retrieval. New York:Association for Computing Machinery, 2006:178-185.
[56] LEVY O, GOLDBERG Y, DAGAN I. Improving distributional similarity with lessons learned from word embeddings[J]. Transactions of the Association for Computational Linguistics, 2015, 3:211-225.
[57] ZHANG Y, ZHANG G Q, ZHU D H, et al. Scientific evolutionary pathways:identifying and visualizing relationships for scientific topics[J]. Journal of the Association for Information Science and Technology, 2017, 68(8):1925-1939.
[58] 沈君, 王续琨, 高继平, 等. 技术坐标视角下的主题分析——以第三代移动通信技术为例[J]. 情报学报, 2012, 31(6):603-611.
[59] 姜鑫, 王德庄, 马海群. 社会网络分析方法在图书情报学科的应用研究[M]. 北京:知识产权出版社, 2019.
[60] KONG X J, SHI Y J, YU S, et al. Academic social networks:modeling, analysis, mining and applications[J]. Journal of network and computer applications, 2019, 132:86-103.
[61] 宋歌. 网络结构视域下的创新潜力指标研究[J]. 图书情报工作, 2014, 58(3):64-71.
[62] 路甬祥. 学科交叉与交叉科学的意义[J]. 中国科学院院刊, 2005(1):58-60.
[63] 商琦, 陈洪梅. 区块链技术创新态势专利情报实证[J]. 情报杂志, 2019, 38(4):23-28,59.
[64] 闫凯伦. 面向区块链的交易传播算法和去中心化机器学习框架研究[D].桂林:广西师范大学, 2021.
[65] 何帅, 黄襄念, 刘谦博, 等. DPoS区块链共识机制的改进研究[J]. 计算机应用研究, 2021, 38(12):3551-3557.
[66] 朱书坤. 基于区块链和卷积神经网络的电动汽车能源交易方案设计与实现[D].武汉:华中师范大学, 2020.
[67] NSTC. Critical and emerging technologies, CETs[R]. Washington, DC:National Science and Technology Council, 2022.
[68] 武继刚, 刘同来, 李境一, 等. 移动边缘计算中的区块链技术研究进展[J]. 计算机工程, 2020, 46(8):1-13.
[69] 何申, 陆璐, 李征, 等. 区块链+边缘计算技术白皮书[R]. 杭州:中国移动5G联合创新中心, 2020.
[70] 王晨旭, 程加成, 桑新欣, 等. 区块链数据隐私保护:研究现状与展望[J]. 计算机研究与发展, 2021, 58(10):2099-2119.
Outlines

/

〈 〉