研究论文

创新活动中的知识测度:理论进展与热点方向

  • 黄珂敏 ,
  • 曲建升 ,
  • 陈文杰
展开
  • 1 中国科学院成都文献情报中心 成都 610299;
    2 中国科学院大学经济与管理学院信息资源管理系 北京 100190
黄珂敏,博士研究生;曲建升,研究员,博士,通信作者,E-mail:qujs@clas.ac.cn;陈文杰,高级工程师,博士研究生。

收稿日期: 2025-01-09

  修回日期: 2025-06-10

  网络出版日期: 2026-03-14

基金资助

本文系国家社会科学基金青年项目“基于超网络的关键核心技术识别与技术机会发现研究”(项目编号:24CTQ044)研究成果之一。

Knowledge Measurement in Innovation Activities: Theoretical Progress and Research Hotspots

  • HUANG Kemin ,
  • QU Jiansheng ,
  • CHEN Wenjie
Expand
  • 1 National Science Library (CHENGDU), Chinese Academy of Sciences, Chengdu 610299;
    2 Department of Information Resources Management, School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190
Huang Kemin, Doctoral Candidate; Qu Jiansheng, Professor, PhD, Corresponding Author, E-mail: qujs@clas.ac.cn; Chen Wenjie, Senior Engineer, Doctoral Candidate.

Received date: 2025-01-09

  Revised date: 2025-06-10

  Online published: 2026-03-14

Supported by

This work is supported by National Social Science Fund of China project titled “Research on Key Core Technology Identification and Technology Opportunity Discovery Based on Supernetwork” (Grant No. 24CTQ044).

摘要

[目的/意义] 在创新活动的全流程中,知识在创造、流动、转化、应用等过程中表现出各自的特征和价值,基于创新链视角探究知识测度的理论与研究进展对创新生态系统构建及知识管理效率的提高具有重要的理论和实践意义。[方法/过程] 聚焦创新链视角下知识测度的相关理论与研究内容,从知识属性结构、知识状态形式、社会经济价值等3方面系统性地梳理知识测度的研究理论,并基于创新全过程、创新主体、创新价值链等视角分别剖析知识流动、多链知识协同、知识增值等热点方向的研究进展。[结果/结论] 从产业关联不足、创新内容分析力度不够、创新主体知识协同机制不明、知识增值评估和作用机制有待完善等方面阐述该领域未来的探索空间,并从数据、内容、方法、技术、场景等多维度为创新活动中知识测度研究的进一步发展提出建议。

本文引用格式

黄珂敏 , 曲建升 , 陈文杰 . 创新活动中的知识测度:理论进展与热点方向[J]. 图书情报工作, 2026 , 70(6) : 92 -103 . DOI: 10.13266/j.issn.0252-3116.2026.06.008

Abstract

[Purpose/Significance] In the whole process of innovation activities, knowledge shows its own characteristics and value in the process of creation, flow, transformation, and application. Exploring the theory and research progress of knowledge measurement from the perspective of innovation chain has important theoretical and practical significance for the construction of innovation ecosystem and the improvement of knowledge management efficiency. [Method/Process] Focusing on the theories and research related to knowledge measurement from the perspective of innovation chain, this paper systematically reviewed the research theories from three aspects: knowledge attribute structure, knowledge state form, and socio-economic value. Then, it analyzed the research progress of knowledge flow, multi-chain knowledge collaboration, and knowledge value-added from the perspectives of the entire innovation process, innovation subjects, and the innovation value chain. [Result/Conclusion] This paper expounds the future exploration space in this field from the aspects of insufficient industrial linkage, insufficient analysis of innovation content, unclear knowledge coordination mechanism of innovation subjects, and the need for refinement in the assessment and functional mechanisms of knowledge value-added evaluation. It also puts forward suggestions for the further development of knowledge measurement research in innovation activities from the aspects of data, content, method, technology, and scene.

参考文献

[1] 吴月辉, 蒋建科, 喻思南, 等. 抢占科技竞争和未来发展制高点[N]. 人民日报, 2024-06-26(2). (WU Y, JIANG J K, YU S N, et al. Seize the commanding heights of science and technology competition and future development[N]. People’s daily, 2024-06-26(2).)
[2] LAWRENCE M, POLIQUIN C. The growth of hierarchy in organizations: managing knowledge scope[J]. Strategic management journal, 2023, 44(13): 3155-3184.
[3] STIRLING A. A general framework for analyzing diversity in science, technology and society[J]. Journal of the Royal Society, 2007, 4(15): 707-719.
[4] 陈祖胜, 任浩, 林明. 知识互补性对研发网络内异位势企业间联盟的影响——基于知识基础结构的调节效应[J]. 预测, 2015, 34(2): 28-33. (CHEN Z S, REN H, LIN M. The impact of knowledge complementary on alliance choice of firm within the network: an analysis based on the moderation of knowledge structure[J]. Frontiers of science and technology of engineering management, 2015, 34(2): 28-33.)
[5] BOH W F, EVARISTO R, OUDERKIRK A. Balancing breadth and depth of expertise for innovation: a 3M story[J]. Research policy, 2014, 43(2): 349-366.
[6] GAN R, CHEN X H, WANG Z Y, et al. Knowledge heterogeneity and corporate innovation performance: the mediating influence of task conflict and relationship conflict[J]. Plos one, 2023, 18(10).
[7] RODAN S, GALUNIC C. More than network structure: how knowledge heterogeneity influences managerial performance and innovativeness[J]. Strategic management journal, 2004, 25(6): 541-541).
[8] 覃熙. 铜鼓资源组织中的史料文献细粒度知识元研究[J]. 四川图书馆学报, 2024(4): 23-31. (TAN X. Research on the fine-grained knowledge elements of historical documents in the organizing of timbal resources[J]. Journal of the Library Science Society of Sichuan, 2024(4): 23-31.)
[9] HOWISON J, BULLARD J. Software in the scientific literature: problems with seeing, finding, and using software mentioned in the biology literature[J]. Journal of the association for information science & technology, 2015, 67(9): 2137-2155.
[10] LU W, HUANG Y, BU Y, et al. Functional structure identification of scientific documents in computer science[J]. Scientometrics, 2018, 115(1): 463-486.
[11] KAYVAN B, YASMEEN Z B, GIORGIO A. Assisted neuroscience knowledge extraction via machine learning applied to neural reconstruction metadata on NeuroMorpho.org[J]. Brain informatics, 2022, 9(1): 26-26.
[12] 陆伟, 黄永, 程齐凯. 学术文本的结构功能识别——功能框架及基于章节标题的识别[J]. 情报学报, 2014, 33(9): 979-985. (LU W, HUANG Y, CHENG Q K. The structure function of academic text and its classification[J]. Journal of the China Society for Scientific and Technical Information, 2014, 33(9): 979-985.)
[13] 刘昊坦, 刘家伟, 张帆, 等. 科技文献的多层次结构功能识别[J]. 信息资源管理学报, 2024, 14(3): 90-103. (LIU H T, LIU J W, ZHANG F, et al. Multi-level functional structure recognition of scientific literature[J]. Journal of information resources management, 2024, 14(3): 90-103.)
[14] WU J, SHANLEY M T. Knowledge stock, exploration, and innovation: research on the United States electromedical device industry[J]. Journal of business research, 2009, 62(4): 474-483.
[15] DIERICKX I, COOL K. Asset stock accumulation and sustainability of competitive advantage[J]. Management science, 1989, 35: 1504-1511.
[16] TODO Y. Knowledge spillovers from foreign direct investment in R&D: evidence from Japanese firm-level data[J]. Journal of Asian economics, 2006, 17(6): 996-1013.
[17] 李瑾坤, 张炼, 李永建. 基于知识量的知识作业评测研究[J]. 管理学报, 2009, 6(11): 1466-1470. (LI J K, ZHANG L, LI Y J. The assessment of knowledge work based on knowledge quantity[J]. Chinese journal of management, 2009, 6(11): 1466-1470.)
[18] AUDRETSCH D B. The dynamic role of small firms: evidence from the US[J]. Small bus econ, 2002, 18: 13-40.
[19] ZHANG Y, LIN N L, GU J P, et al. Research on urban economic centrality in the perspectives of knowledge stocks and flows[J]. Heliyon, 2024, 10(1).
[20] 冉征, 张玉昌, 郑江淮. 政府创新补贴、知识存量与企业研发投入——基于研发活动不确定性的分析与检验[J]. 中国经济问题, 2023(6): 52-66. (RANG Z, ZHANG Y C, ZHENG J H. Government innovation subsidies, knowledge stock and corporate R&D investment: an analysis based on uncertainty of R&D activities[J]. China economic studies, 2023(6): 52-66.)
[21] CASSIMAN B, VEUGELERS R. In search of complementarity in innovation strategy: cooperation in R&D internal R&D and external technology acquisition[J]. Manage sci public policy, 2006, 52(1): 68-82.
[22] 杨志锋, 邹珊刚. 知识资源、知识存量和知识流量:概念、特征和测度[J]. 科研管理, 2000(4): 105-111. (YANG Z F, ZOU S G. Stores and flows of knowledge resources: features and measurement[J]. Science research management, 2000(4): 105-111.)
[23] TEECE D J. Technology transfer by multinational firms: the resource cost of transferring technological know-how[J]. The economic journal, 1977, 87(346): 242-261.
[24] GELDRES V V, URIBE C T, COUDOUNARIS D N, et al. Innovation and experiential knowledge in firm exports: applying the initial U-model[J]. Journal of business research, 2016, 69(11): 5076-5081.
[25] 董睿, 张海涛. 复杂网络视角下创新生态系统知识转移建模及仿真研究[J]. 软科学, 2022, 36(6): 122-129. (DONG R, ZHANG H T. The model and simulation research for knowledge transfer of innovation ecosystem from the perspective of complex network[J]. Soft science, 2022, 36(6): 122-129.)
[26] 王立良, 李琰, 宋艳辉. 知识计量学与“五计学”的关系辨析[J]. 科研管理, 2018, 39(S1): 372-377. (WANG L L, LI Y, SONG Y H. Relationship between knowledge metrics and five metrologies[J]. Science research management, 2018, 39(S1): 372-377.)
[27] DEKKER R, HOOG R. The monetary value of knowledge assets: a micro approach[J]. Expert systems with applications, 2000, 18(2): 111-124.
[28] LANJOUW JO, SCHANKERMAN M. Patent quality and research productivity-measuring innovation with multiple indicators [J]. The economic journal, 2004, 114(4): 441.
[29] ZHAO Y, TANG L C M, DARLINGTON M J, et al. High value information in engineering organizations[J]. International journal of information management, 2008, 28(4): 246-258.
[30] XU Y, BERNARD A. Knowledge value chain: an effective tool to measure knowledge value[J]. International journal of computer integrated manufacturing, 2002, 23(11): 957-967.
[31] NEMET M, BARI? F, BENCI?, et al. Comparative technology assessment using patent evaluation, 2018 Zooming Innovation in Consumer Technologies Conference (ZINC) [J]. Novi sad Serbia, 2018: 162-165.
[32] SHI C, HU P, FAN W, et al. How learning effects influence knowledge contribution in online Q&A community? A social cognitive perspective[J]. Decision support systems, 2021, 149: 113610.
[33] 崔智斌, 涂艳. 社会化问答社区在线学习者知识贡献绩效影响因素研究[J]. 中国远程教育, 2022(2): 57-64, 75. (CUI Z B, TU Y. Factors influencing knowledge contribution performance of online learners in social Q&A community[J]. Chinese journal of distance education, 2022(2): 57-64, 75.)
[34] 张云秋, 黄麒霏. 兼顾不同词频知识单元价值与重要性的知识组合发现方法研究[J]. 数据分析与知识发现, 2025, 9(7): 66-78. (ZHANG Y Q, HUANG Q F. A study of knowledge combination discovery methods taking into account the value and importance of knowledge elements with different word frequencies [J]. Data analysis and knowledge discovery, 2025, 9(7): 66-78.)
[35] HOELTING K R, MARTINEZ D E, SCHUSTER R M, et al. Advancing knowledge pluralism and cultural benefits in ecosystem services theory and application[J]. Ecosystem services, 2024, 65: 101583.
[36] TIMMERS P. Building effective public R&D programmes [C] //Proceedings of PICMET’99: Portland international conference on management of engineering and technology. Portland: IEEE, 1999: 591-597.
[37] SEN N. Innovation chain and CSIR[J]. Current science, 2003, 85(5): 570-574.
[38] 庞杰. 知识流动理论框架下的科学前沿与技术前沿研究[D]. 大连: 大连理工大学, 2011. (PANG J. Research on measuring S&T frontiers based on the theoretical framework of knowledge flow[D]. Dalian: Dalian University of Technology, 2011.)
[39] HEGDE D, LUO H. Patent publication and the market for ideas[J]. Management science, 2015, 64(2): 652-672.
[40] 王腾飞. 知识跨区域流动的隐形壁垒及其作用机制[J]. 地理科学, 2023, 43(12): 2101-2109. (WANG T F. The invisible barriers of knowledge cross-regional flow and its mechanism[J]. Scientia geographica sinica, 2023, 43(12): 2101-2109.)
[41] 洪勇, 王鹤楠. 基于“范围—流向”分析的产业技术主体知识流动机理——以平板显示技术领域为例[J]. 科学学与科学技术管理, 2020, 41(12): 99-113. (HONG Y, WANG H N. Knowledge flow mechanism of industrial technical actors based on “scope-direction” analysis: a case study of the flat panel display technology[J]. Science of science and management of S.& T., 2020, 41(12): 99-113.)
[42] 瞿浩翔, 徐江, 徐静妤. 未来产业颠覆性技术创新的时空现象学研究[J]. 中国工程科学, 2024, 26(3): 239-256. (QU H X, XU J, XU J Y. Spatiotemporal phenomenology of disruptive technological innovations in future industries[J]. Strategic study of CAE, 2024, 26(3): 239-256.)
[43] 侯剑华, 王仲禹. 研究主题的知识流动测度及其实证分析——以H指数研究为例[J]. 图书情报工作, 2017, 61(10): 87-93. (HOU J H, WANG Z Y. Knowledge flow measurement and empirical analysis of research topics: a case study of H-index[J]. Library and information service, 2017, 61(10): 87-93.)
[44] LIU Y, ROUSSEAU R. Knowledge diffusion through publications and citations: a case study using ESI-fields as unit of diffusion[J]. Journal of the American Society for Information Science and Technology, 2010, 61(2): 340-351.
[45] 巴志超, 李纲, 朱世伟. 科研合作网络的知识扩散机理研究[J]. 中国图书馆学报, 2016, 42(5): 68-84. (BA Z C, LI G, ZHU S W. Knowledge diffusion mechanism of scientific cooperation network[J]. Journal of library science in China, 2016, 42(5): 68-84.)
[46] 李昌, 周锦锦, 杨中楷. 动态演化过程视角下技术融合生长点识别研究[J]. 图书情报工作, 2022, 66(7): 99-109. (LI C, ZHOU J J, YANG Z K. 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.)
[47] BATTKE B, SCHMIDT T S, STOLLENWERK S, et al. Internal or external spillovers: which kind of knowledge is more likely to flow within or across technologies[J]. Research policy, 2016, 45(1): 27-41.
[48] JIANG M, YANG S, GAO Q. Multidimensional indicators to identify emerging technologies: perspective of technological knowledge flow[J]. Journal of informetrics, 2024, 18(1): 101483.
[49] WANG T, YU C, SHI X, et al. From recombination to diffusion: exploring the impact of knowledge recombination characteristics on geographical knowledge diffusion[J]. Journal of engineering and technology management, 2024, 72: 101818.
[50] 张伟然. 创新全过程视角下潜在合作对象识别方法研究[D]. 济南: 齐鲁工业大学, 2023. (ZHANG W R. Research on the identification method of potential cooperation objects from the perspective of the whole innovation process[D]. Jinan: Qilu University of Technology, 2023.)
[51] XU S, LIU Z, AN X. Linkages among science, technology, and industry [C] //Proceedings of the joint workshop of the 4th extraction and evaluation of knowledge entities from scientific documents and the 3rd AI + informetrics (EEKE-ALL 2023). Santa Fe and Online: ACM/IEEE, 2023: 13-15.
[52] 刘春丽, 臧东宇, 陈爽. 科学—技术—产业关联测度与主题演化规律研究——以生物医药领域为例[J]. 图书情报工作, 2024, 68(14): 95-116. (LIU C L, ZANG D Y, CHEN S. Research on the correlation measure and theme evolution law of science-technology-industry: taking the field of biomedicine as an example[J]. Library and information service, 2024, 68(14): 95-116.)
[53] DAVID B A, MAKSIM B. The knowledge spillover of innovation[J]. Industrial and corporate change, 2022, 31(6): 1329-1357.
[54] JAVORCIK B S. Does foreign direct investment increase the productivity of domestic firms? Research of spillovers through backward linkages[J]. American economic review, 2004, 94(3): 605-627.
[55] ZHANG S, WANG X, ZHANG B. The policy effects of innovative city pilot on the dual efficiency of industry–university–research knowledge flow[J]. Technology analysis & strategic management, 2021, 34(9): 1038-1049.
[56] 王超, 许海云, 武华维, 等. 基于动态结构熵的颠覆性技术知识网络扩散特征识别方法研究[J]. 图书情报工作, 2023, 67(24): 54-71. (WANG C, XU H Y, WU H W, et al. Research on the method of identifying disruptive technology knowledge networks diffusion characteristics based on dynamic structure entropy[J]. Library and information service, 2023, 67(24): 54-71.)
[57] 王腾飞, 马仁锋, 庄汝龙. 数字经济背景下长三角城市产业比较优势演化及其知识流动驱动研究[J]. 地理科学进展, 2024, 43(2): 203-214. (WANG T F, MA R F, ZHUANG R L. Evolution of industrial comparative advantages and its driving mechanism of knowledge flow of cities in the Yangtze River Delta under the background of digital economy[J]. Progress in geography, 2024, 43(2): 203-214.)
[58] OBSCHONKA M, TAVASSOLI S, RENTFROW P J, et al. Innovation and inter-city knowledge spillovers: social, geographical, and technological connectedness and psychological openness[J]. Research policy, 2023, 52(8): 104849.
[59] KONG J, ZHANG J, DENG S, et al. Knowledge convergence of science and technology in patent inventions[J]. Journal of informetrics, 2023, 17(3): 101435.
[60] FRIEDMAN Y. Where are drugs invented, and why does it matter? [J]. ACS medicinal chemistry letters, 2017, 8(6): 589-591.
[61] WANG J P. Shapira. Funding acknowledgement analysis: an enhanced tool to investigate research sponsorship impacts: the case of nanotechnology[J]. Scientometrics, 2011, 87(3): 563-586.
[62] 王建胜. 长三角城市群产学研知识流动与先进制造业发展的机制与效应[J]. 经济地理, 2024, 44(2): 134-144, 218. (WANG J S. The knowledge flow of industry-university-research and the development of advanced manufacturing in the Yangtze River Delta Urban Agglomeration: mechanisms and effects[J]. Economic geography, 2024, 44(2): 134-144, 218.)
[63] 王超, 许海云, 齐砚翠, 等. 知识网络视角下科学、技术、产业间创新驱动关系识别方法研究. 情报学报[J], 2024, 43(1): 10-24. (WANG C, XU H Y, QU Y C, et al. Identification method of innovation driving relationships among science, technology, and industry from the perspective of the knowledge network[J]. Journal of the China Society for Scientific and Technical Information, 2024, 43(1): 10-24.)
[64] 薛澜, 姜李丹, 黄颖, 等. 资源异质性、知识流动与产学研协同创新——以人工智能产业为例[J]. 科学学研究, 2019, 37(12): 2241-2251. (XUE L, JIANG L D, HUANG Y, et al. Resource heterogeneity, knowledge flow and synergy innovation of industry-university-research institute: an empirical study on AI industry[J]. Studies in science of science, 2019, 37(12): 2241-2251.)
[65] DU J, LI P, GUO Q, et al. Measuring the knowledge translation and convergence in pharmaceutical innovation by funding-science-technology-innovation linkages analysis[J]. Journal of informetrics, 2019, 13(1): 132-148.
[66] INNOCENTI N, CAPONE F, LAZZERETTI L, et al. Knowledge networks and industrial structure for regional innovation[J]. Regional science, 2020, 99(1): 55-72.
[67] 张任之. 研发联盟、知识流动与企业数字技术创新[J]. 北京师范大学学报(社会科学版), 2024(2): 142-153. (ZHANG R Z. On R&D alliances, knowledge flow and enterprise digital technology innovation[J]. Journal of Beijing Normal University (social sciences), 2024(2): 142-153.)
[68] ZHANG X, LEI S, SUN J, et al. Robustness of multi-project knowledge collaboration network in open source community[J]. Entropy, 2023, 25(1): 108.
[69] BALLAND P A, BELSOMARTINEZ J A, MORRISON A. The dynamics of technical and business knowledge networks in industrial clusters: embeddedness, status, or proximity? [J]. Economic geography, 2016, 92(1): 35-60.
[70] 张凤婷. 知识协同视角下知识供应链伙伴选择策略研究[D]. 重庆: 重庆工商大学, 2024. (ZHANG F T. Partner selection strategy of knowledge supply chain based on knowledge collaboration[D]. Chongqing: Chongqing Technology and Business University, 2024.)
[71] PORTER M E. 竞争优势[M]. 北京: 华夏出版社, 1985. (PORTER M E. Competitive advantage[M]. Beijing: Huaxia Press, 1985.)
[72] 经济合作与发展组织. 以知识为基础的经济[M]. 北京: 机械工业出版社, 1997. (OECD. A knowledge-based economy[M]. Beijing: China Machine Press, 1997.)
[73] LEE M C. Knowledge-based new product development through knowledge transfer and knowledge innovation, innovation through knowledge transfer smart innovation[J]. Systems and technologies, 2010, 5: 303-320.
[74] LEE M C. Knowledge management and innovation management: best practices in knowledge sharing and knowledge value chain[J]. International journal of innovation and learning, 2016, 19(2): 206.
[75] LEE C C, YANG J. Knowledge value chain[J]. Journal of management development, 2002, 19(9): 783-794.
[76] 周霞, 陈为东, 曾思瑜. 知识链模型视角下政府开放数据流转与价值增值模式研究[J]. 情报资料工作, 2024, 45(3): 86-95. (ZHOU X, CHEN W D, ZENG S Y. Study on the government open data flow and transformation and value appreciation mode: under the perspective of the knowledge chain model[J]. Information and documentation services, 2024, 45(3): 86-95.)
[77] 宋曼祺, 徐一旻, 吕伟, 等. 政用产学研战略联盟知识价值链的研究[J]. 软科学, 2018, 32(2): 96-100. (SONG M Q, XU Y M, LY W, et al. Study on the knowledge value chain of government customer-industry-university-institute strategic alliance[J]. Soft science, 2018, 32(2): 96-100.)
[78] 赖一飞, 谢潘佳, 叶丽婷, 等. 我国区域科技创新效率测评及影响因素研究——基于超效率SBM-Malmquist-Tobit模型[J]. 科技进步与对策, 2021, 38(13): 37-45. (LAI Y F, XIE P J, YE L T, et al. Research on the efficiency and influencing factors of science and technology innovation in China: based on the Super-Efficiency SBM-Malmquist-Tobit model[J]. Science & technology progress and policy, 2021, 38(13): 37-45.)
[79] AYOUB M, LHUILLERY S. Mix and match: what is the best R&D recipe for eco-innovation? [J]. Industry and innovation, 2024, 31(7): 896-921.
[80] 李佳轩, 储节旺. 数智化知识生态系统的稳态演化与知识生成能力分析——基于耗散结构理论[J]. 情报资料工作, 2024, 45(4): 53-63. (LI J X, CHU J W. Steady-state evolution and knowledge generation capability analysis of digital intelligence knowledge ecosystem: based on dissipative structure theory[J]. Information and documentation services, 2024, 45(4): 53-63.)
文章导航

/

〈 〉