INFORMATION RESEARCH

Research on the Identification of Technology Fusion Growth Points from the Perspective of Dynamic Evolution Process

  • Li Chang ,
  • Zhou Jinjin ,
  • Yang Zhongkai
Expand
  • 1. Faculty of Humanities and Social Sciences, Dalian University of Technology, Dalian 116024;
    2. College of Economics and Management, Northeast Agricultural University, Harbin 150006

Received date: 2021-11-02

  Revised date: 2022-01-15

  Online published: 2022-04-15

Abstract

[Purpose/Significance] This paper proposes a new method to identify the growth points of technology fusion from the perspective of the dynamic evolution process of technology, aiming to identify the technology fusion fields with growth potential.[Method/Process] First, the study proposed two stages in the evolution path, summarized the evolution path of the technological fusion growth points, and summarized significant attribute characteristics according to the characteristics of the technological fusion growth points, and constructed an index system for recognition. Finally, calculating the changes of attributes on the evolution path realizes the identification of the technological fusion growth points.[Result/Conclusion] Through experiments and comparisons with existing studies, this method can effectively identify the growth points of technology fusion, and trace the origin and process of technology.

Cite this article

Li Chang , Zhou Jinjin , Yang Zhongkai . Research on the Identification of Technology Fusion Growth Points from the Perspective of Dynamic Evolution Process[J]. Library and Information Service, 2022 , 66(7) : 99 -109 . DOI: 10.13266/j.issn.0252-3116.2022.07.010

References

[1] HACKLIN F, BATTISTINI B, KROGH G. Strategic choices in converging industries[J]. MIT sloan management review, 2013, 55(1):65-73.
[2] JEONG S, LEE S. What drives technology convergence? exploring the influence of technological and resource allocation contexts[J]. Journal of engineering and technology management, 2015, 36(2):78-96.
[3] CURRAN C. The anticipation of converging industries[M]. London:Springer, 2013.
[4] ROCO M C, BAINBRIDGE W S. The new world of discovery, invention, and innovation:convergence of knowledge, technology, and society[J]. Journal of nanoparticle research, 2013, 15(9):1946-1963.
[5] CURRAN C, LEKER J. Patent indicators for monitoring convergence-examples from NFF and ICT[J]. Technological forecasting and social change, 2011, 78(2):256-273.
[6] KARVONEN M, KÄSSI T. Patent citations as a tool for analyzing the early stages of convergence[J]. Technological forecasting and social change, 2013, 80(6):1094-1107.
[7] GATES A J, KE Q, VAROL O, et al. Nature's reach:narrow work has broad impact[J]. Nature,2019, 575(7781):32-34.
[8] MOON S, KIM. On a patent analysis method for technological convergence[J]. Procedia social & behavioral sciences, 2012,40(40):657-663.
[9] SONG C H, ELVERS D, LEKER J. Anticipation of converging technology areas-a refined approach for the identification of attractive fields of innovation[J]. Technological forecasting & social change, 2017, 116(3):98-115.
[10] EILERS K, FRISCHKOM J, EPPINGER E,et al. Patent-based semantic measurement of one-way and two-way technology convergence:the case of ultraviolet light emitting diodes (UV-LEDs)-ScienceDirect[J]. Technological forecasting and social change, 2019, 140(3):341-353.
[11] KIM T S, SOHN S Y. Machine-learning-based deep semantic analysis approach for forecasting new technology convergence[J]. Technological forecasting and social change, 2020, 157:120095.
[12] PARK H, YOON J. Assessing coreness and intermediarity of technology sectors using patent co-classification analysis:the case of korean national R&D[J]. Scientometrics,2014, 98(2):853-890.
[13] 栾春娟,覃雪,黄福. 技术大类之间会聚指数测度的理论与方法[J]. 科技管理研究, 2016, 36(8):188-193.
[14] 李姝影,方曙. 测度技术融合与趋势的数据分析方法研究进展[J]. 数据分析与知识发现, 2017, 1(7):2-12.
[15] YONGRAE C, MINSUNG K, Wolfgang G. Entropy and gravity concepts as new methodological indexes to investigate technological convergence:patent network-based approach[J]. Plos one, 2014, 9(6):e98009.
[16] 冯科,曾德明,周昕.技术融合的动态演化路径[J].科学学研究,2019,37(6):986-995.
[17] LEE C, HONG S, KIM J. Anticipating multi-technology convergence:a machine learning approach using patent information[J]. Scientometrics, 2021, 126(3):1867-1896.
[18] XU H, WINNINK J, YUE Z, et al. Topic-linked innovation paths in science and technology[J]. Journal of informetrics, 2020, 14(2):101014.
[19] DAHLIN K B, BEHRENS D M. When is an invention really radical?[J]. Research policy,2005, 34(5):717-737.
[20] 阿瑟.技术的本质:技术是什么,它是如何进化的[M].曹东溟,王健,译.杭州:浙江人民出版社,2014:329-344.
[21] CAVIGGIOLI F. Technology fusion:Identification and analysis of the drivers of technology convergence using patent data[J]. Technovation, 2016, 55/56:22-32.
[22] ROCO M C. Coherence and divergence of megatrends in science and engineering[J]. Journal of nanoparticle research,2002, 4(1):9-19.
[23] BAINBRIDGE W S, ROCO M C. Science and technology convergence:with emphasis for nanotechnology-inspired convergence[J]. Journal of nanoparticle research,2016, 18(7):211-230.
[24] HACKLIN F. Management of convergence in innovation[J]. Contributions to management science, 2007, 2010(42):1014-1021.
[25] 赵红洲,蒋国华. 知识单元与指数规律[J]. 科学学与科学技术管理, 1984(9):39-41.
[26] 刘则渊. 知识图谱的若干问题思考[R]. 大连:大连理工大学WISE实验室,2010.
[27] HACKLIN F, MARXT C, FAHRNI F. Coevolutionary cycles of convergence:an extrapolation from the ICT industry[J]. Technological forecasting & social change, 2009, 76(6):723-736.
[28] PORTER M E, STERN S. Measuring the "Ideas" production function:evidence from international patent output[J]. Nber working papers series, 2000,9(3):47-57.
[29] ZHOU Y, DONG F, KONG D, et al. Unfolding the convergence process of scientific knowledge for the early identification of emerging technologies[J]. Technological forecasting and social change,2019, 144(7):205-220.
[30] 李长玲,高峰,牌艳欣.试论跨学科潜在知识生长点及其识别方法[J].科学学研究,2021,39(6):1007-1014.
[31] 吴红,伊惠芳,马永新,等. 面向专利技术主题分析的WI-LDA模型研究[J]. 图书情报工作,2018, 62(17):68-74.
[32] RODRIGUEZ A, KIM B, TURKOZ M, et al. New multi-stage similarity measure for calculation of pairwise patent similarity in a patent citation network[J]. Scientometrics,2015, 103(2):565-581.
[33] MOEHRLE M G, PASSING F. Applying an anchor based patent mapping approach:basic conception and the case of carbon fiber reinforcements[J]. World patent information,2016, 45(1):1-9.
[34] PASSING F, MOEHRLE M G. Measuring technological convergence in the field of smart grids:a semantic patent analysis approach using textual corpora of technologies[C]//2015 Portland international conference on management of engineering and technology. Portland:IEEE, 2015.
[35] RODRIGUEZ A, KIM B, TURKOZ M, et al. New multi-stage similarity measure for calculation of pairwise patent similarity in a patent citation network[J]. Scientometrics,2015, 103(2):565-581.
[36] MILANEZ D H, FARIA L I L D, AMARAL R M D, et al. Claim-based patent indicators:a novel approach to analyze patent content and monitor technological advances[J]. World patent information,2017, 50(9):64-72.
[37] KIM J, LEE S. Forecasting and identifying multi-technology convergence based on patent data:the case of IT and BT industries in 2020[J]. Scientometrics,2017, 111(1):47-65.
[38] AN J, KIM K, Mortara L, et al. Deriving technology intelligence from patents:preposition-based semantic analysis[J]. Journal of informetrics,2018, 12(1):217-236.
[39] AN X, LI J, XU S, et al. An improved patent similarity measurement based on entities and semantic relations[J]. Journal of informetrics,2021, 15(2):1-16.
[40] SMALL H, BOYACK K W, KLAVANS R. Identifying emerging topics in science and technology[J]. Research policy,2014, 43(8):1450-1467.
[41] BREITZMAN A, THOMAS P. The emerging clusters model:a tool for identifying emerging technologies across multiple patent systems[J]. Research policy, 2015, 44(1):195-205.
[42] PELAZ B, ALEXIOU C, ALVAREZ-PUEBLA R A, et al. Diverse applications of nanomedicine[J]. ACS nano, 2017, 11(3):2313-2381.
Outlines

/