|
|
Impact of Scientific and Technological Linkage on the Innovation Performance of High-tech Industries —The Moderating Effect of Foreign Technology Dependence |
Ma Yaxue1,2, Ba Zhichao1,3, Cao Zhenting1,2, Sun Jianjun1,2 |
1.Laboratory for Data Intelligence and Interdisciplinary Innovation, Nanjing University, Nanjing 210023 2.School of Information Management, Nanjing University, Nanjing 210023 3.Research Institute for Data Management & Innovation, Nanjing University, Suzhou 215163 |
|
|
Abstract The high-tech industry is the pillar that drives the construction of an innovative country. The development of ways to improve the innovation performance of the high-tech industry is a crucial issue that urgently needs to be addressed to optimize the economic structure and enhance the competitiveness of the national industry. The development of the high-tech industry cannot be separated from the support of scientific and technological collaborative innovation. This paper focuses on the Chinese high-tech industry and uses scientific papers and patent literature as proxies for science and technology. We propose a mapping method for scientific papers and patent literature specific to certain industries. The degree of scientific-technological linkage in the Chinese high-tech industry is then measured from the perspective of knowledge content association. Finally, we adopt panel models to analyze the impact mechanism of scientific-technological linkage on the innovation performance of the Chinese high-tech industry and explore the moderating effect of foreign technology dependence on the above relationship. The results indicate that (1) the degree of scientific-technological linkage in 17 Chinese high-tech industries is relatively low, and there are differences in the evolutionary characteristics of scientific-technological linkage in different high-tech industries. (2) There is an inverted U-shaped relationship between scientific-technological linkage and the innovation performance of the high-tech industry. (3) Foreign technology dependence has a negative moderating effect on the inverted U-shaped relationship between scientific-technological linkage and the innovation performance of the high-tech industry. This study can provide management insights for the government to formulate scientific and technological innovation development plans and technology introduction policies, as well as for Chinese high-tech industry-related research institutions and companies to select and adjust innovation directions.
|
Received: 18 September 2023
|
|
|
|
1 师修繁. 财政支持、技术获取与高技术产业发展——基于我国30个省份面板数据的实证分析[J]. 科技管理研究, 2022, 42(22): 24-30. 2 Lin Z Z, Li S N, Zhou H W, et al. Nonlinear impact of the interaction between internal knowledge and knowledge spillover on patent quality: evidence from China's provincial high‐tech industry[J]. Managerial and Decision Economics, 2023, 44(1): 562-575. 3 Lin S F, Lin R Y, Sun J, et al. Dynamically evaluating technological innovation efficiency of high-tech industry in China: provincial, regional and industrial perspective[J]. Socio-Economic Planning Sciences, 2021, 74: 100939. 4 Almutairi A, Everatt J, Snape P, et al. Exploring the relationship between science and technology in the curriculum[J]. Australasian Journal of Technology Education, 2014, 1: 49-63. 5 董坤, 许海云, 罗瑞, 等. 科学与技术的关系分析研究综述[J]. 情报学报, 2018, 37(6): 642-652. 6 陈一梅, 王兴旺. “科学—技术”关联性研究及其在情报分析中的应用分析[J]. 创新科技, 2018, 18(3): 80-83. 7 Liu Z Y, Chen X F, Chu J F, et al. Industrial development environment and innovation efficiency of high-tech industry: analysis based on the framework of innovation systems[J]. Technology Analysis & Strategic Management, 2018, 30(4): 434-446. 8 庞兰心, 官建成. 政府财税政策对高技术企业创新和增长的影响[J]. 科学学研究, 2018, 36(12): 2259-2269. 9 Guo Y T, Zheng G. Recombinant capabilities, R&D collaboration, and innovation performance of emerging market firms in high-technology industry[J]. IEEE Transactions on Engineering Management, 2023, 70(7): 2431-2446. 10 Yang N N, Liu Q M, Qian F R, et al. Does global value chain position affect innovation performance of China’s high-tech industries?[J]. Chinese Management Studies, 2023, 17(3): 660-682. 11 李培楠, 赵兰香, 万劲波. 创新要素对产业创新绩效的影响——基于中国制造业和高技术产业数据的实证分析[J]. 科学学研究, 2014, 32(4): 604-612. 12 Zhang R H, Sun B, Liu M Y. Do external technology sourcing and industrial agglomeration successfully facilitate an increase in the innovation performance of high-tech industries in China?[J]. IEEE Access, 2019, 7: 15414-15423. 13 Chen H, Hou J, Chen W. Threshold effect of knowledge accumulation between innovation path and innovation performance: new evidence from China’s high-tech industry[J]. Science, Technology and Society, 2018, 23(1): 163-184. 14 罗亚非, 蔡乾龙. 对外技术依存度测评方法研究[J]. 科技进步与对策, 2009, 26(22): 132-136. 15 王淑英, 张远芳. 数字化转型能否缓解产业结构趋同程度[J]. 产业经济评论, 2022(6): 119-132. 16 曹勇, 肖琦, 刘弈, 等. 知识异质性与新产品开发绩效: 转化式学习的中介作用与高管支持的调节效应[J]. 科学学与科学技术管理, 2020, 41(12): 20-34. 17 Laursen K, Salter A. Open for innovation: the role of openness in explaining innovation performance among U.K. manufacturing firms[J]. Strategic Management Journal, 2006, 27(2): 131-150. 18 杨雨寒, 闫亚飞, 张立佳, 等. 协同发展背景下京津冀科技资源分布与共享现状分析[J]. 科技管理研究, 2020, 40(22): 94-103. 19 李洪涛, 王丽丽. 中心城市科技创新对城市群结构体系的影响[J]. 中国科技论坛, 2020(7): 170-179. 20 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. 21 刘岩, 苏可蒙, 高艳慧. 企业基础研究对技术创新绩效的影响: 来自中国生物制药企业的分析[J]. 科技进步与对策, 2022, 39(12): 102-111. 22 迟培娟, 丁洁兰, 冷伏海. 突破性论文的三元计量特征及识别研究——以生物医学领域为例[J]. 情报学报, 2022, 41(7): 663-675. 23 Haans R F J, Pieters C, He Z L. Thinking about U: Theorizing and testing U- and inverted U-shaped relationships in strategy research[J]. Strategic Management Journal, 2016, 37(7): 1177-1195. 24 孙丽文, 曹璐, 吕静韦. 基于DPSIR模型的工业绿色转型评价研究——以河北省为例[J]. 经济与管理评论, 2017, 33(4): 120-127. 25 吴鹏, 常远, 陈广汉. 技术创新的中等收入分配效应: 原创还是引进再创新[J]. 财经研究, 2018, 44(7): 126-141. 26 王楠, 杨柯巍. 新形势下强化企业创新主体地位的问题及对策研究[J]. 科学学与科学技术管理, 2024, 45(1): 3-11. 27 Duan Y L, Liu S L, Cheng H, et al. The moderating effect of absorptive capacity on transnational knowledge spillover and the innovation quality of high-tech industries in host countries: evidence from the Chinese manufacturing industry[J]. International Journal of Production Economics, 2021, 233: 108019. 28 王金凤, 王孟琪, 冯立杰. 外部知识异质性、知识多元化与突破式创新绩效——基于企业生命周期视角[J]. 软科学, 2020, 34(12): 14-19. 29 Zhou Z J, Zhang P Y, Lu M M, et al. The influence of government intervention on the performance of independent innovation under financial support based on data of listed companies in strategic emerging industries[J]. Mathematical Problems in Engineering, 2020, 2020: 5063986. 30 陈洪玮, 徐清如, 陈霏. 制度环境与研发投入对高技术产业创新绩效的影响[J]. 统计与决策, 2021, 37(18): 166-170. 31 Meyer M. Tracing knowledge flows in innovation systems[J]. Scientometrics, 2002, 54(2): 193-212. 32 Marx M, Fuegi A. Reliance on science: worldwide front-page patent citations to scientific articles[J]. Strategic Management Journal, 2020, 41(9): 1572-1594. 33 Marx M, Fuegi A. Reliance on science by inventors: hybrid extraction of in-text patent-to-article citations[J]. Journal of Economics & Management Strategy, 2022, 31(2): 369-392. 34 Ba Z C, Liang Z T. A novel approach to measuring science-technology linkage: from the perspective of knowledge network coupling[J]. Journal of Informetrics, 2021, 15(3): 101167. 35 姬中洋, 李彦龙. 非研发创新与高技术产业创新绩效[J]. 经济经纬, 2019, 36(4): 94-101. 36 Zhu J W, Wang Y Y, Wang C Y. A comparative study of the effects of different factors on firm technological innovation performance in different high-tech industries[J]. Chinese Management Studies, 2019, 13(1): 2-25. 37 Narin F, Olivastro D. Linkage between patents and papers: an interim EPO/US comparison[J]. Scientometrics, 1998, 41(1/2): 51-59. 38 沈宏超. 我国战略性新兴产业发展隐患的形成机理及治理对策[J]. 现代经济探讨, 2014(6): 29-33. 39 俞立平. 高技术产业引进技术为什么会下降[J]. 科学学研究, 2016, 34(11): 1646-1654. |
|
|
|