|
|
Technology Convergence Path and Effect of China's Solar Energy Industry Driven by Core Technology |
Liu Li, Su Lifang, Lou Xuming, Cheng Long |
College of Economics and Management, Xi'an University of Posts and Telecommunications, Xi'an 710061 |
|
|
Abstract The core technology provides technical opportunities for reversing the plight of enterprise innovation and achieving technological breakthroughs. Researching the technology convergence path driven by core technology can help innovation subject to clearly define the trajectory of technological development so that R&D resources can be allocated reasonably in different technological fields, which would improve innovation performance. We use the NPCIA core technology identification framework, taking the patent data of China’s solar energy industry from 2004 to 2018 as the research object, analyzing the core technology-driven technology convergence path based on the perspective of knowledge flow, and using LMDI model to test the driving effect of core technologies from four aspects: technology breadth, cross-integration strength, technology scale, and technology convergence depth. The results show that the core technology-driven technology convergence path includes the core path, edge absorption path, and edge diffusion path. Technology breadth and technology scale positively affect the technology convergence. Technology convergence depth and cross-integration strength exhibit a two-way trend. Among them, the driving effect of cross-integration strength is the strongest.
|
Received: 13 April 2022
|
|
|
|
1 Luan C J, Sun X M, Wang Y L. Driving forces of solar energy technology innovation and evolution[J]. Journal of Cleaner Production, 2021, 287: Article No.125019. 2 Karvonen M, Lehtovaara M, K?ssi T. Build-up of understanding of technological convergence: evidence from printed intelligence industry[J]. International Journal of Innovation and Technology Management, 2012, 9(3): Article No.1250020. 3 Caviggioli F. Technology fusion: identification and analysis of the drivers of technology convergence using patent data[J]. Technovation, 2016, 55/56: 22-32. 4 辜胜阻, 吴华君, 吴沁沁, 等. 创新驱动与核心技术突破是高质量发展的基石[J]. 中国软科学, 2018(10): 9-18. 5 全裕吉, 陈益云. 从非核心技术创新到核心技术创新: 中小企业创新的一种战略[J]. 科学管理研究, 2003, 21(3): 5-8, 27. 6 吕璐成, 赵亚娟. 基于专利数据的技术融合研究综述[J]. 图书情报工作, 2021, 65(6): 138-148. 7 郑素丽, 吴盛豪, 郭京京. 自动驾驶汽车技术轨道演进研究——基于社群识别和主路径分析的整合框架[J]. 科研管理, 2022, 43(2): 126-136. 8 陈悦, 王康, 宋超, 等. 一种用于技术融合与演化路径探测的新方法: 技术群相似度时序分析法[J]. 情报学报, 2021, 40(6): 565-574. 9 Xiao Z L, Du X Y. Measurement and convergence in development performance of China’s high-tech industry[J]. Science, Technology and Society, 2017, 22(2): 212-235. 10 罗吉利, 李孟军, 姜江, 等. 基于证据推理的核心技术识别方法研究[J]. 情报杂志, 2015, 34(1): 38-43, 31. 11 黄鲁成, 刘春文, 吴菲菲, 等. 基于NPCIA的核心技术识别模型及应用研究[J]. 科学学研究, 2020, 38(11): 1998-2007. 12 Choi C, Kim S, Park Y. A patent-based cross impact analysis for quantitative estimation of technological impact: the case of information and communication technology[J]. Technological Forecasting and Social Change, 2007, 74(8): 1296-1314. 13 Kim M S, Kim C. On A patent analysis method for technological convergence[J]. Procedia-Social and Behavioral Sciences, 2012, 40: 657-663. 14 娄岩, 赵培培, 黄鲁成. 基于专利共类的无人机技术融合趋势研究[J]. 情报杂志, 2020, 39(11): 68-75, 81. 15 李慧, 孟玮, 徐存真. 基于专利知识流网络的技术融合分析——以石墨烯领域为例[J]. 现代情报, 2021, 41(5): 121-130. 16 赵玉林, 李丫丫. 技术融合、竞争协同与新兴产业绩效提升——基于全球生物芯片产业的实证研究[J]. 科研管理, 2017, 38(8): 11-18. 17 王宏起, 夏凡, 王珊珊. 新兴产业技术融合方向预测: 方法及实证[J]. 科学学研究, 2020, 38(6): 1009-1017, 1075. 18 Song B M, Suh Y. Identifying convergence fields and technologies for industrial safety: LDA-based network analysis[J]. Technological Forecasting and Social Change, 2019, 138: 115-126. 19 Ko N, Yoon J, Seo W. Analyzing interdisciplinarity of technology fusion using knowledge flows of patents[J]. Expert Systems with Applications, 2014, 41(4): 1955-1963. 20 Tsouri M, Hansen T, Hanson J, et al. Knowledge recombination for emerging technological innovations: the case of green shipping [J]. Technovation, 2022, 114: Article No.102454. 21 翟东升, 蔡力伟, 张杰, 等. 基于专利的技术融合创新轨道识别模型研究——以云计算技术为例[J]. 情报学报, 2015, 34(4): 352-360. 22 Kwon O, An Y, Kim M, et al. Anticipating technology-driven industry convergence: evidence from large-scale patent analysis[J]. Technology Analysis & Strategic Management, 2020, 32(4): 363-378. 23 苗红, 赵润博, 黄鲁成, 等. 基于LMDI分解模型的技术融合驱动因素研究[J]. 科技进步与对策, 2019, 36(3): 11-18. 24 周阳, 周冬梅, 丁奕文, 等. 军民融合技术转移的路径演化及其驱动因素研究——“中物技术”2004—2017案例研究[J]. 管理评论, 2020, 32(6): 323-336. 25 周磊, 杨威. 基于专利的技术知识流特征研究[J]. 情报杂志, 2016, 35(5): 108-112. 26 潘微微, 菅利荣, 刘涛. 基于关键节点与关键路径的专利集群网络演进研究[J]. 科技进步与对策, 2020, 37(12): 1-8. 27 魏红芹, 周成. 专利间知识流动与技术融合趋势研究[J]. 科技进步与对策, 2018, 35(22): 17-22. 28 Yuan X D, Cai Y C. Forecasting the development trend of low emission vehicle technologies: based on patent data[J]. Technological Forecasting and Social Change, 2021, 166: Article No.120651. 29 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. |
|
|
|