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| Evolution and Evaluation of China's Convergence Innovation Policies |
| Chen Zifeng,Chen Yuanyuan,Jia Weifeng,Cheng Long |
| (School of Economics and Management,Xi'an University of Posts and Telecommunications, Xi'an 710061, China) |
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Abstract Resource competition among countries is becoming increasingly fierce in globalization. Countries across the world are eager to effectively utilize the existing resources to promote innovation performance,gain market share,improve core competitiveness and raise international status. Innovation in a single field may not be enough to break through the bottlenecks of core technologies, and the convergence innovation between different technical fields may provide more possibilities for this problem. As an innovation activity that blurs the existing boundaries and constraints, convergence innovation is becoming the key driving force of current innovation by converging knowledge from different science, technology, market and industry fields. However, convergence innovation does not happen naturally, it needs to be promoted and supported by policies. Therefore, it is particularly important to analyze the evolutionary process and characters, assess the representative policies effectively and summarize the insufficiencies of convergence innovation policies. Nevertheless, the existing relevant studies mainly focus on some particular industries from the perspective of a single or dual dimensions , with rare systematic studies on the evaluation of convergence innovation policies. #br#In order to study China's national convergence innovation policies during 2001-2020, this paper constructs an analytical framework from four dimensions, i.e. policy theme evolution, policy tool usage, policy relationship network and policy quantitative evaluation, by using text mining and keyword co-occurrence network, supply-demand-environment policy tools, policy citation network and PMC index model, respectively. China's convergence innovation policies have experienced three phrases and 30 polices have been identified for this study. Firstly, the results show that the importance of convergence policies has been well recognized, but the policy system is still in its early stage. Though the numbers of relative policy keywords and the frequencies of co-words have been increased significantly, they still mainly appeared in general innovation policies in subordinate positions. Policies elaborated for convergence innovation are still deficient and mainly focus on a few technology and industry fields. Secondly, for policy tools, the long-term oriented supply-side and environmental-side tools are favored, referring to a wide range of disciplines, technologies and industries, while the relatively fast-acting demand-side are used less. The most common means are human capital support and infrastructure, mechanism and safeguard, demonstration and popularization for three policy tools respectively. Thirdly, the policy citation network has been initially formed, but the interconnections are not close enough, with quite a few isolated policies. The most commonly cited policies are three famous general innovation policies, while the influence of specific convergence policies is less significant. It means that the top-level policy design for convergence innovation has not yet been sound and the synergistic effect of policies needs to be strengthened. Fourthly, the quantitative evaluations have been carried on seven classic policies. Three policies with convergence innovation in their titles show relative lower scores than the comprehensive scores, with two of them just at an acceptable level, which means that there is great room for improvement in the convergence innovation policies .#br#For policy makers, top-level design and overall planning are urgently needed for convergence innovation, then more dedicated policies in wider technology and industrial fields can be established accordingly in order to form a sound and robust convergence innovation policy network with the aim to lead, guide and support convergence innovation activities. At the policy level, more demand-side policy tools which can provide the initial market pull and promote the market cycle are recommended, especially in government procurement and service outsourcing, in order to make policy “combination punches”. If so, the support effect from long-term oriented supply-side and environment-side tools can be synergized with short-term oriented demand-side tools, and thus they can jointly drive convergence innovation activities. #br#This paper proposes a universal and comprehensive framework for policy research by integrating the main policy study perspectives. It can be used for other policy studies as well. The empirical study results of China's convergence innovation policies have important practical significance for policy makers.#br#
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Received: 15 January 2022
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[1] 刘娜,毛荐其,余光胜.技术会聚研究探析与展望[J].科研管理,2017,38(12):20-28.
[2] LIU N, MAO J, GUAN J. Knowledge convergence and organization innovation: the moderating role of relational embeddedness[J]. Scientometrics ,2020,125(5): 189-202.
[3] RAHUL C BASOLE, HYUNWOO PARK, BRANDON C BARNETT. Coopetition and convergence in the ICT ecosystem [J]. Telecommunication Policy, 2015, 39(5): 537-552.
[4] NATHALIE SICK, NINA PRESCHITSCHEK, JENS LEKER, et al. A new framework to assess industry convergence in high technology environments [J]. Technovation, 2019, 84-85(6-7): 48-58.
[5] 林如海,彭维湘.企业创新理论及其对企业创新能力评价意义的研究[J].科学学与科学技术管理,2009,30(11):118-121.
[6] 张超,官建成.基于政策文本内容分析的政策体系演进研究——以中国创新创业政策体系为例[J].管理评论,2020,32(5):138-150.
[7] 黄红华.政策工具理论的兴起及其在中国的发展[J].社会科学,2010,32(4):13-19,187.
[8] 顾建光,吴明华.公共政策工具论视角述论[J].科学学研究,2007,25(1):47-51.
[9] 王班班,齐绍洲.市场型和命令型政策工具的节能减排技术创新效应——基于中国工业行业专利数据的实证[J].中国工业经济,2016,34(6):91-108.
[10] ROTHWELL R,ZEGVELD W. Reindusdalization and technology [M]. Logman Group Limited,1985.
[11] 张永安,马昱.基于熵权TOPSIS法的区域技术创新政策评价研究[J].科技管理研究,2017,37(6):92-97.
[12] 宗芳,张慧永,贾洪飞.基于贝叶斯网络的停车收费政策评价[J].华南理工大学学报(自然科学版),2010,38(7):78-83.
[13] ESTRADA M. Policy modeling: definition, classification and evaluation [J]. Journal of Policy Modeling, 2011, 33(4):523-536.
[14] 张永安,郄海拓.“大众创业、万众创新”政策量化评价研究——以2017的10项双创政策情报为例[J].情报杂志,2018,37(3):158-164,186.
[15] 臧维,张延法,徐磊.我国人工智能政策文本量化研究——政策现状与前沿趋势[J].科技进步与对策,2021,38(15):125-134.
[16] 赵杨,陈雨涵,陈亚文.基于PMC指数模型的跨境电子商务政策评价研究[J].国际商务(对外经济贸易大学学报),2018,32(6):114-126.
[17] 臧维,李甜甜,徐磊.北京市众创空间扶持政策工具挖掘及量化评价研究[J].软科学,2018,32(9):56-61.
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