|
|
|
| Evolution of Technology-Science Synergistic Innovation from the Perspective of Industrial Chain |
| Yan Jingbo, Yu Haiyan |
| School of Economics and Management, East China Normal University, Shanghai 200062 |
|
|
|
|
Abstract Industrial chain collaborative innovation serves as a crucial pathway for enhancing industrial competitiveness, and scientific assessment of collaborative innovation evolution patterns holds significant importance for formulating effective innovation management strategies. This study proposes a technology-science collaborative innovation evolution analysis framework. Based on patent and paper data from industrial chain segments, the framework employs an improved graph convolutional network method to identify collaborative innovation communities. It also establishes a two-dimensional evaluation system using collaborative evolution synchronization rate and innovation transfer efficiency to comprehensively analyze the evolution patterns and management characteristics of industrial chain collaborative innovation. An empirical analysis of the new energy vehicle lithium-ion battery industrial chain validates the effectiveness of the proposed framework. The proposed analytical framework can effectively improve the accuracy of community identification and evolution analysis in large-scale networks, and the constructed collaborative innovation evaluation system can effectively identify the evolution process and existing bottlenecks of technology-science collaborative innovation capabilities in industrial chains, providing a valuable reference for industries to conduct collaborative innovation.
|
|
Received: 23 July 2025
|
|
|
|
1 陈志远, 张杰, 孙昊, 等. 创新链和产业链融合下的产业政策[J]. 经济研究, 2024, 59(9): 154-172.
2 赵彬彬, 梅亮, 陈凯华, 等. 创新链产业链融合的内涵解析、影响因素和优化路径: 基于创新过程与创新系统整合视角[J]. 中国软科学, 2025(2): 26-39.
3 杨浩昌, 付庆波, 李廉水. 数字化转型影响产业链韧性的机制与效应研究——兼议制造业产业链韧性的分维度比较[J]. 科学学研究, 2025, 43(12): 2532-2543, 2585.
4 Chesbrough H. The logic of open innovation: managing intellectual property[J]. California Management Review, 2003, 45(3): 33-58.
5 Vivona R, Demircioglu M A, Audretsch D B. The costs of collaborative innovation[J]. The Journal of Technology Transfer, 2023, 48(3): 873-899.
6 陈劲, 阳银娟. 协同创新的理论基础与内涵[J]. 科学学研究, 2012, 30(2): 161-164.
7 Liu J L, Chen Y Y, Liang F H. The effects of digital economy on breakthrough innovations: evidence from Chinese listed companies[J]. Technological Forecasting and Social Change, 2023, 196: 122866.
8 Hu F, Xi X, Zhang Y Y. Influencing mechanism of reverse knowledge spillover on investment enterprises’ technological progress: an empirical examination of Chinese firms[J]. Technological Forecasting and Social Change, 2021, 169: 120797.
9 Luo T, Zhang Y Q, Zheng M G, et al. Can science and technology resources co-evolve with high-tech industries? Empirical evidence from China[J]. Technological Forecasting and Social Change, 2024, 208: 123665.
10 许海云, 王超, 陈亮, 等. 颠覆性技术的科学-技术-产业互动模式识别与分析[J]. 情报学报, 2023, 42(7): 816-831.
11 王焘, 付少雄. 面向前沿交叉领域的科学-技术-产业融合创新组态效应研究——基于复杂适应系统理论[J]. 图书与情报, 2024(6): 21-32.
12 Freeman C. Networks of innovators: a synthesis of research issues[J]. Research Policy, 1991, 20(5): 499-514.
13 Powell W W, Koput K W, Smith-Doerr L. Interorganizational collaboration and the locus of innovation: networks of learning in biotechnology[J]. Administrative Science Quarterly, 1996, 41(1): 116-145.
14 Wang G, Li Y, Zuo J, et al. Who drives green innovations? Characteristics and policy implications for green building collaborative innovation networks in China[J]. Renewable and Sustainable Energy Reviews, 2021, 143: 110875.
15 王志明, 刘启航, 廖雪琴, 等. 新疆创新发展试验区协同创新网络特征及其对创新绩效的影响[J/OL]. 管理评论, (2025-03-27). https://doi.org/10.14120/j.cnki.cn11-5057/f.20250327.004.
16 Du Y N, Wang Q X, Song Y, et al. How cross-regional collaborative innovation networks affect regional economic resilience: evidence from 283 cities in China[J]. Technological Forecasting and Social Change, 2025, 215: 124057.
17 刘佳, 钟永恒, 何晓东, 等. 基于多元关系融合的科学-技术-产业关联模式识别方法研究[J]. 现代情报, 2024, 44(6): 67-81.
18 Wang J, Li S Y, Chen J, et al. Study on the evolution of multi-level collaborative innovation networks in China’s cloud manufacturing industry[J]. Technology Analysis & Strategic Management, 2025, 37(12): 2655-2676.
19 Wang J F, Wang N T, Zhao W Y, et al. Identifying and evaluating R&D partners via patent-based multilayer networks from the perspective of knowledge complementarity: a case study of unmanned ship technology[J]. Computers & Industrial Engineering, 2025, 204: 111085.
20 Xu H Y, Yue Z H, Pang H S, et al. Integrative model for discovering linked topics in science and technology[J]. Journal of Informetrics, 2022, 16(2): 101265.
21 Chen X, Ye P F, Huang L, et al. Exploring science-technology linkages: a deep learning-empowered solution[J]. Information Processing & Management, 2023, 60(2): 103255.
22 Palla G, Barabási A L, Vicsek T. Quantifying social group evolution[J]. Nature, 2007, 446(7136): 664-667.
23 Greene D, Doyle D, Cunningham P. Tracking the evolution of communities in dynamic social networks[C]// Proceedings of the 2010 International Conference on Advances in Social Networks Analysis and Mining. Piscataway: IEEE, 2010: 176-183.
24 Beck F, Burch M, Diehl S, et al. A taxonomy and survey of dynamic graph visualization[J]. Computer Graphics Forum, 2017, 36(1): 133-159.
25 施必翔, 陈劲, 李亚东, 等. 根技术视角下关键核心技术动态演化路径研究——以芯片光刻技术为例[J]. 科学学研究, 2026, 44(1): 154-165.
26 刘颖, 于春梅, 李晓晨, 等. 基于改进BERTopic模型的领域主题表征及演化研究[J]. 图书情报工作, 2025, 69(3): 78-89.
27 Qiu Z P, Wang Z. Technological origination and evolution analysis by combining patent claims and citations: a case of surgical robot domain[J]. Advanced Engineering Informatics, 2023, 58: 102145.
28 Newman M E J, Girvan M. Finding and evaluating community structure in networks[J]. Physical Review E, 2004, 69(2): 026113.
29 Blondel V D, Guillaume J L, Lambiotte R, et al. Fast unfolding of communities in large networks[J]. Journal of Statistical Mechanics: Theory and Experiment, 2008, 2008(10): P10008.
30 Traag V A, Waltman L, Van Eck N J. From Louvain to Leiden: guaranteeing well-connected communities[J]. Scientific Reports, 2019, 9: Article No.5233.
31 Wang X F, Li J H, Yang L, et al. Unsupervised learning for community detection in attributed networks based on graph convolutional network[J]. Neurocomputing, 2021, 456: 147-155.
32 Liu H T, Wei J H, Xu T Y. Community detection based on community perspective and graph convolutional network[J]. Expert Systems with Applications, 2023, 231: 120748.
33 Huang X Y, Chen D M, Ren T, et al. A survey of community detection methods in multilayer networks[J]. Data Mining and Knowledge Discovery, 2021, 35(1): 1-45.
34 Zhang L, Song H D, Aletras N, et al. Node-feature convolution for graph convolutional networks[J]. Pattern Recognition, 2022, 128: 108661.
35 Dong W, Wo?niak M, Wu J S, et al. Denoising aggregation of graph neural networks by using principal component analysis[J]. IEEE Transactions on Industrial Informatics, 2023, 19(3): 2385-2394.
36 Bing R, Yuan G, Zhu M, et al. Heterogeneous graph neural networks analysis: a survey of techniques, evaluations and applications[J]. Artificial Intelligence Review, 2023, 56(8): 8003-8042.
37 滕婕, 刘莉, 李硕, 等. 动态语义网的高价值热点主题识别与演化路径分析[J]. 图书情报工作, 2023, 67(7): 92-106.
38 陈翔, 黄璐, 倪兴兴, 等. 基于动态语义网络分析的主题演化路径识别研究[J]. 情报学报, 2021, 40(5): 500-512.
39 Huang L, Chen X, Zhang Y, et al. Identification of topic evolution: network analytics with piecewise linear representation and word embedding[J]. Scientometrics, 2022, 127(9): 5353-5383.
40 Wei S, Liu W H, Choi T M, et al. The influence of key components and digital technologies on manufacturer’s choice of innovation strategy[J]. European Journal of Operational Research, 2024, 315(3): 1210-1220.
41 Deng H P, Duan S X, Wibowo S. Digital technology driven knowledge sharing for job performance[J]. Journal of Knowledge Management, 2023, 27(2): 404-425.
42 冯思达, 韩芳, 杨斌, 等. 锂离子电池产业链全球科学-技术创新格局研究[J]. 科研管理, 2024, 45(12): 70-78.
43 安同良, 姜舸, 王大中. 中国高技术制造业技术测度与赶超路径——以锂电池行业为例[J]. 经济研究, 2023, 58(1): 192-208.
44 刘颖琦, 宋泽源, 高宏伟, 等. 中国新能源汽车10年推广效果研究: 空间效应视角[J]. 管理评论, 2023, 35(3): 3-16.
45 郭靖怡, 王学昭, 陈小莉. 基于专利文本中产品关联关系的产业技术链构建与实证研究——以锂离子电池产业为例[J]. 图书情报工作, 2023, 67(5): 108-118.
46 国务院办公厅关于印发新能源汽车产业发展规划(2021—2035年)的通知[EB/OL]. (2020-10-20) [2025-10-12]. https://www.gov.cn/gongbao/content/2020/content_5560291.htm.
47 Bajolle H, Lagadic M, Louvet N. The future of lithium-ion batteries: exploring expert conceptions, market trends, and price scenarios[J]. Energy Research & Social Science, 2022, 93: 102850.
48 Zhong M R, Huang G L, He R F. The technological innovation efficiency of China’s lithium-ion battery listed enterprises: evidence from a three-stage DEA model and micro-data[J]. Energy, 2022, 246: 123331.
49 Higashide N, Zhang Y, Asatani K, et al. Quantifying advances from basic research to applied research in material science[J]. Technovation, 2024, 135: 103050.
50 Lee A, Sarker S, Saal J E, et al. Machine learned synthesizability predictions aided by density functional theory[J]. Communications Materials, 2022, 3(1): Article No.73.
51 Yang X G, Liu T, Wang C Y. Thermally modulated lithium iron phosphate batteries for mass-market electric vehicles[J]. Nature Energy, 2021, 6(2): 176-185. |
|
|
|
