计量与演化视角下的新兴技术识别研究进展评述

doi:10.3772/j.issn.1000-0135.2020.06.009

情报学报

2020, Vol. 39

Issue (6): 651-661 DOI: 10.3772/j.issn.1000-0135.2020.06.009

研究进展与文献综述

本期目录 | 过刊浏览 | 高级检索

计量与演化视角下的新兴技术识别研究进展评述

卢小宾¹, 杨冠灿¹, 徐硕², 张杨燚¹

1.中国人民大学信息资源管理学院，北京 100872
2.北京工业大学经济管理学院，北京 100124

Review of Research Progress on Emerging Technologies Identification Based on Quantitative and Evolutionary Perspectives

Lu Xiaobin¹, Yang Guancan¹, Xu Shuo², Zhang Yangyi¹

1.School of Information Resource Management, Renmin University of China, Beijing 100872
2.College of Economic and Management, Beijing University of Technology, Beijing 100124

摘要
图/表
参考文献
相关文章 (15)

全文: PDF (1368 KB) HTML (125 KB)
输出: BibTeX | EndNote (RIS)

摘要新兴技术识别一直是科技创新管理、科技政策制定和技术竞争情报研究领域关注的问题。学术界已经对此开展了大量的学术研究，然而“新兴技术”概念的界定问题却严重制约着新兴技术识别的发展，其根源在于当前研究实践中存在两种迥异的认知视角：计量视角与演化视角。在这两种视角共同的作用下，新兴技术的概念边界不断扩展，因此，厘清新兴技术识别概念的基础就是首先理解两种视角不同的特点与应用场景。本文首先归纳了一个由新兴技术识别的特征、数据表示与识别方法三部分构成的框架，该框架能够较为全面地涵盖当前计量视角下新兴技术识别的研究进展。然后通过文献分析发现，演化视角下的新兴技术识别方法更关注于以下四个方面：基于技术重组的根本性创新，学科、技术网络融合的协同效应，技术实用性与功能性驱动效应，以及颠覆性创新问题，而这四种特征推动了演化视角数据表示与识别方法的转化。最后，本文对新兴技术识别数据表示与方法的拓展进行了初步的展望。本文希望通过比较不同视角下新兴技术识别实践工作，为未来深入开展新兴技术识别活动提供参考。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	卢小宾
	杨冠灿
	徐硕
	张杨燚

关键词 ：新兴技术识别, 演化视角, 计量视角, 技术涌现, 融合

收稿日期: 2019-10-21

基金资助:国家重点研发计划项目“知识产权信息智能采集及深度加工技术研究与应用示范”（2017YFB1401902）。

作者简介: 卢小宾，男，1963 年生，博士，教授，博士生导师，研究方向为信息分析；杨冠灿，男，1981 年生，博士，讲师，研究方向为专利数据挖掘、技术竞争情报，E-mail：yanggc@ruc.edu.cn；徐硕，男，1980 年生，博士，教授，研究方向为技术预见、科学前沿探测、数据挖掘；张杨燚，女，1997 年生，硕士研究生，研究方向为专利数据挖掘、技术竞争情报。

引用本文:

卢小宾, 杨冠灿, 徐硕, 张杨燚. 计量与演化视角下的新兴技术识别研究进展评述[J]. 情报学报, 2020, 39(6): 651-661.
Lu Xiaobin, Yang Guancan, Xu Shuo, Zhang Yangyi. Review of Research Progress on Emerging Technologies Identification Based on Quantitative and Evolutionary Perspectives. 情报学报, 2020, 39(6): 651-661.

链接本文:

https://qbxb.istic.ac.cn/CN/10.3772/j.issn.1000-0135.2020.06.009 或 https://qbxb.istic.ac.cn/CN/Y2020/V39/I6/651

1 Leydesdorff L, Rafols I. Local emergence and global diffusion of research technologies: An exploration of patterns of network formation[J]. Journal of the American Society for Information Science and Technology, 2011, 62(5): 846-860.
2 van den Oord A, van Witteloostuijn A. A multi-level model of emerging technology: An empirical study of the evolution of biotechnology from 1976 to 2003[J]. PLoS ONE, 2018, 13(5): e0197024.
3 Emerging technology and national security[R]. Analytic Exchange Program, 2018.
4 Small H, Boyack K W, Klavans R. Identifying emerging topics in science and technology[J]. Research Policy, 2014, 43(8): 1450-1467.
5 Gl?nzel W, Thijs B. Using ‘core documents’ for detecting and labelling new emerging topics[J]. Scientometrics, 2012, 91: 399-416.
6 Adner R, Levinthal D A. The emergence of emerging technologies[J]. California Management Review, 2002, 45(1): 50-66.
7 Alexander J, Chase J, Newman N, et al. Emergence as a conceptual framework for understanding scientific and technological progress[C]// Proceedings of PICMET’12: Technology Management for Emerging Technologies. IEEE, 2012.
8 Burmaoglu S, Sartenaer O, Porter A. Conceptual definition of technology emergence: A long journey from philosophy of science to science policy[J]. Technology in Society, 2019, 59: 101126.
9 Burmaoglu S, Sartenaer O, Porter A, et al. Analysing the theoretical roots of technology emergence: An evolutionary perspective[J]. Scientometrics, 2019, 119(1): 97-118.
10 Cozzens S, Gatchair S, Kang J, et al. Emerging technologies: Quantitative identification and measurement[J]. Technology Analysis & Strategic Management, 2010, 22(3): 361-376.
11 Halaweh M. Emerging technology: What is it?[J]. Journal of Technology Management & Innovation, 2013, 8(3): 19-20.
12 Litvinski O. Emerging technology: Toward a conceptual definition[J]. International Journal of Trade, Economics and Finance, 2018, 9(6): 256-262.
13 Rotolo D, Hicks D, Martin B R. What is an emerging technology?[J]. Research Policy, 2015, 44(10): 1827-1843.
14 王玏, 吴新年. 新兴技术识别方法研究综述[J]. 图书情报工作, 2020, 64(4): 125-135.
15 Porter A L, Garner J, Carley S F, et al. Emergence scoring to identify frontier R&D topics and key players[J]. Technological Forecasting and Social Change, 2019, 146: 628-643.
16 Verhoeven D, Bakker J, Veugelers R. Measuring technological novelty with patent-based indicators[J]. Research Policy, 2016, 45(3): 707-723.
17 王敏. 新兴技术“三要素多层次”共生演化机制研究[D]. 成都: 电子科技大学, 2010.
18 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.
19 Kajikawa Y, Yoshikawa J, Takeda Y, et al. Tracking emerging technologies in energy research: Toward a roadmap for sustainable energy[J]. Technological Forecasting and Social Change, 2008, 75(6): 771-782.
20 Cobo M J, López-Herrera A G, Herrera-Viedma E, et al. An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the fuzzy sets theory field[J]. Journal of Informetrics, 2011, 5(1): 146-166.
21 Yan E J. Finding knowledge paths among scientific disciplines[J]. Journal of the Association for Information Science and Technology, 2014, 65(11): 2331-2347.
22 Dernis H, Squicciarini M, de Pinho R. Detecting the emergence of technologies and the evolution and co-development trajectories in science (DETECTS): A ‘burst’ analysis-based approach[J]. The Journal of Technology Transfer, 2016, 41(5): 930-960.
23 Joung J, Kim K. Monitoring emerging technologies for technology planning using technical keyword based analysis from patent data[J]. Technological Forecasting and Social Change, 2017, 114: 281-292.
24 Kyebambe M N, Cheng G, Huang Y Q, et al. Forecasting emerging technologies: A supervised learning approach through patent analysis[J]. Technological Forecasting and Social Change, 2017, 125: 236-244.
25 Lee C, Kwon O, Kim M, et al. Early identification of emerging technologies: A machine learning approach using multiple patent indicators[J]. Technological Forecasting and Social Change, 2018, 127: 291-303.
26 Wang Q. A bibliometric model for identifying emerging research topics[J]. Journal of the Association for Information Science and Technology, 2018, 69(2): 290-304.
27 Song K, Kim K, Lee S. Identifying promising technologies using patents: A retrospective feature analysis and a prospective needs analysis on outlier patents[J]. Technological Forecasting and Social Change, 2018, 128: 118-132.
28 Corning P A. The re-emergence of “emergence”: A venerable concept in search of a theory[J]. Complexity, 2002, 7(6): 18-30.
29 Arthur W B. The nature of technology: What it is and how it evolves[M]. New York: Simon and Schuster, 2009.
30 Strumsky D, Lobo J E. Identifying the sources of technological novelty in the process of invention[J]. Research Policy, 2015, 44(8): 1445-1461.
31 Schoenmakers W, Duysters G. The technological origins of radical inventions[J]. Research Policy, 2010, 39(8): 1051-1059.
32 Curran C S, Leker J. Patent indicators for monitoring convergence - examples from NFF and ICT[J]. Technological Forecasting and Social Change, 2011, 78(2): 256-273.
33 Karvonen M, K?ssi T. Patent citations as a tool for analysing the early stages of convergence[J]. Technological Forecasting and Social Change, 2013, 80(6): 1094-1107.
34 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.
35 Kim H, Joung J, Kim K. Semi-automatic extraction of technological causality from patents[J]. Computers & Industrial Engineering, 2018, 115: 532-542.
36 Choi S, Park H, Kang D, et al. An SAO-based text mining approach to building a technology tree for technology planning[J]. Expert Systems with Applications, 2012, 39(13): 11443-11455.
37 Choi S, Yoon J, Kim K, et al. SAO network analysis of patents for technology trends identification: A case study of polymer electrolyte membrane technology in proton exchange membrane fuel cells[J]. Scientometrics, 2011, 88(3): 863-883.
38 Lucio-Arias D, Leydesdorff L. An indicator of research front activity: Measuring intellectual organization as uncertainty reduction in document sets[J]. Journal of the American Society for Information Science and Technology, 2009, 60(12): 2488-2498.
39 Carley S F, Newman N C, Porter A L, et al. An indicator of technical emergence[J]. Scientometrics, 2018, 115(1): 35-49.
40 Fleming L. Recombinant uncertainty in technological search[J]. Management Science, 2001, 47(1): 117-132.
41 Peng T Q. Assortative mixing, preferential attachment, and triadic closure: A longitudinal study of Tie-generative mechanisms in journal citation networks[J]. Journal of Informetrics, 2015, 9(2): 250-262.
42 Harris J K. An introduction to exponential random graph modeling[M]. Thousand Oaks: SAGE Publications, 2014.
43 Zhang C W, Bu Y, Ding Y, et al. Understanding scientific collaboration: Homophily, transitivity, and preferential attachment[J]. Journal of the Association for Information Science and Technology, 2018, 69(1): 72-86.
44 杨冠灿, 陈亮, 张静, 等. 专利引用关系形成的解释框架: 一个指数随机图模型视角[J]. 图书情报工作, 2019, 63(5): 100-109.
45 李欣, 谢前前, 黄鲁成, 等. 基于SAO结构语义挖掘的新兴技术演化轨迹研究[J]. 科学学与科学技术管理, 2018, 39(1): 17-31.
46 许海云, 武华维, 罗瑞, 等. 基于多元关系融合的科技文本主题识别方法研究[J]. 中国图书馆学报, 2019, 45(1): 82-94.
47 Leone Sciabolazza V, Vacca R, Kennelly Okraku T, et al. Detecting and analyzing research communities in longitudinal scientific networks[J]. PLoS ONE, 2017, 12(8): e0182516.
48 Sha Z H, Wang M X, Huang Y, et al. Modeling product co-consideration relations: A comparative study of two network models[C]// Proceedings of the 21st International Conference on Engineering Design, Vancouver, Canada, 2017, 6: 317-326.
49 White H D, Wellman B, Nazer N. Does citation reflect social structure?: Longitudinal evidence from the “Globenet” interdisciplinary research group[J]. Journal of the American Society for information Science and Technology, 2004, 55(2): 111-126.