【作者】 张艳；

【导师】 陈红川；

【作者基本信息】 广州大学 ， 企业管理， 2025， 硕士

【摘要】 在新一轮科技革命驱动下,数字化、网络化与智能化技术的深度融合正加速重构全球创新生态格局,推动其向多元协同方向转型,并加速创新资源的高频跨界流动。作为典型的技术和知识双密集型产业,我国医药制造业面临着研发投入力度不足、市场转化能力欠缺和供应链关键节点韧性不足等挑战,严重制约产业附加值提升与健康中国战略纵深推进。作为我国经济总量连续36年蝉联榜首的省份,广东省以高新技术产业为战略支点,医药制造业作为高新技术产业的重要领域,已成为经济增长的新动力。因此,本文立足于广东省医药制造业,开展创新资源配置效率评价研究,为产业优化升级提供决策参考,具有较好的现实意义。主要内容如下:首先,由于创新资源配置过程是一个多环节链式传导、动态演进的系统过程,不同阶段对资源要素的协同性、适配性需求存在显著差异,故本文以创新价值链理论和投入产出理论为基础,充分借鉴相关研究成果,并紧密贴合医药制造业特性,从创新开发和成果转化这两个关键阶段入手探究创新资源配置效率。在此基础上,综合考量相关概念,从资源投入和产出效果两个关键维度出发构建医药制造业创新资源配置效率评价指标体系;其次,在传统DEA-CCR模型、传统交叉效率DEA模型的基础上,引入改进交叉效率DEA模型对24个地区医药制造业创新资源配置效率进行静态效率测算,同时运用交叉效率-Malmquist指数模型测算动态效率,重点分析广东省医药制造业效率状况,并将其与两阶段均表现优异的浙江省、山东省和北京市进行对比分析;再次,运用Tobit回归模型分析医药制造业创新资源配置效率影响因素,并进一步探究“高-高”型地区医药制造业影响因素现状变动趋势;最后,基于以上分析结果,提出广东省医药制造业创新资源配置效率提升建议。本文研究发现:(1)2011-2023年广东省在医药制造业资源投入和以有效发明专利数和新产品项目数为体现的知识产出方面表现优异,相关指标排名大多保持在全国前四,但在新产品销售收入方面存在明显不足。2021-2023年广东省医药制造业新产品销售收入增长率分别为19.89%、6%和-21.29%,增长速度放缓。同时其新产品销售收入与江苏省、山东省、浙江省相比存在显著差距。此外,2011-2023年广东省医药制造业新产品出口销售收入占新产品销售收入比重最大为16.13%,最低为1.27%,2023年跌至8.28%,出口波动显著且占比较低。(2)基于静态效率测算结果,发现改进交叉效率DEA模型通过全排序能力、有效区分、微调排名和优化权重,使得评价结果更客观。根据测算结果显示,创新开发阶段广东省效率表现较为突出,成果转化阶段效率波动幅度较小,但整体效率水平不高。在创新开发阶段,2011-2021年广东省医药制造业创新资源配置效率均值为0.767,排名稳定在前七,效率值在0.578至0.852之间波动。在成果转化阶段,2012-2022年广东省医药制造业创新资源配置效率均值降至0.680,排名第十二位,其中七个年份效率值介于0.6至0.7之间,波动较小。(3)基于动态效率测算结果,全要素生产率提升主要源于创新开发阶段与成果转化阶段的技术进步与技术效率的协同作用。在创新开发阶段,2011-2021年广东省医药制造业全要素生产率指数均值为1.245,位居第一。其中,技术进步变化指数增长17.7%,技术效率变化指数增长9.8%。在成果转化阶段,2012-2022年全要素生产率指数均值为1.202,排名第四,其中,技术进步变化指数增长14%,技术效率变化指数增长11.7%。(4)综合分析24个地区医药制造业创新资源配置动静态效率结果,发现广东省在创新开发阶段和创新成果转化阶段均属于“高-高”型,且在“高-高”型中,东部地区占据席位较多,在创新开发阶段和成果转化阶段均占据6席。(5)对比分析广东省与同属“高-高”类型的浙江省、山东省、北京市发现,广东省在不同阶段效率表现呈现差异化特征。在创新开发阶段,广东省呈现出“综合领先,均值优势相对不足”的发展特点。在成果转化阶段,则呈现出“稳步增长,效率水平相对不足”的发展特点。(6)影响因素对医药制造业创新资源配置效率的作用呈现出显著的阶段性特征,且在“高-高”型地区与非“高-高”型地区之间存在差异,在此基础上,分析“高-高”型地区医药制造业创新资源配置效率影响因素的变动趋势发现,广东省医药制造业企业规模相对较小,除政府支持力度和成长能力呈减弱态势外,其余因素均呈现上升趋势。最后,根据上述分析结果,本文提出如下提升建议:优化政策支持模式,强化政策引导作用;优化区域创新环境,营造良好创新氛围;完善相关成果转化机制,精准对接市场需求,突破效率瓶颈;构建多维支撑的人才支持体系,激发自主创新动力;以数字化转型为核心抓手,加速智慧医药产业集群发展;优化资源投入结构,并着力改进出口环节,以强化投入产出的整体表现。更多还原

【Abstract】 Driven by the new round of scientific and technological revolution,the deep integration of digitalization,networking and intelligent technologies is accelerating the reconstruction of the global innovation ecological pattern,promoting its transformation towards multifaceted synergy and accelerating the high-frequency cross-border flow of innovative resources.As a typical technology-intensive and knowledge-intensive industry,China’s pharmaceutical manufacturing industry faces challenges,such as insufficient investment in R&D,lack of market transformation ability and insufficient toughness of key nodes in the supply chain,which seriously restricts the enhancement of industrial value-added and the deepening of the strategy of Healthy China.As the top province of China’s economic output for 36 consecutive years,Guangdong Province takes high-tech industry as the strategic fulcrum,and the pharmaceutical manufacturing industry as an important field of high-tech industry has become the new driving force of economic growth.Therefore,based on the pharmaceutical manufacturing industry in Guangdong Province,this paper carries out research on the evaluation of allocation efficiency of innovation resources to provide decision-making references for the optimization and upgrading of the industry,which has good practical significance.The main content is as follows:First of all,because the process of allocating innovation resources is a multi-link chain conduction and dynamic evolution of the system process,there are significant differences in the synergistic and adaptive needs of resource elements at different stages.Therefore,based on innovation value chain theory and input-output theory,this paper fully draws on relevant research results and closely follows the characteristics of the pharmaceutical manufacturing industry to explore the allocation efficiency of innovation resources from the two key stages of innovation development and achievement transformation.On this basis,the evaluation index system of allocation efficiency of innovation resources of the pharmaceutical manufacturing industry was constructed from the two key dimensions of resource input and output effect by comprehensively considering related concepts.Secondly,on the basis of the traditional DEA-CCR model and the traditional cross-efficiency DEA model,the improved cross-efficiency DEA model is introduced to measure the static efficiency of allocation efficiency of innovation resources of the pharmaceutical manufacturing industry in 24 regions,and at the same time,the cross-efficiency malmquist index model is used to measure the dynamic efficiency,focusing on the analysis of the efficiency of the pharmaceutical manufacturing industry in Guangdong Province,and comparing it with Zhejiang Province,Shandong Province and Beijing city,which performed well in both stages.Thirdly,the Tobit regression model is used to analyze the influencing factors of allocation efficiency of innovation resources of the pharmaceutical manufacturing industry,and the changing trend of influencing factors of the pharmaceutical manufacturing industry in the"high-high"region is further explored.Finally,based on results of the above analysis,suggestions are made to improve the allocation efficiency of innovation resources of the pharmaceutical manufacturing industry in Guangdong Province.This paper finds that:(1)Guangdong Province has performed well in terms of resource investment in the pharmaceutical manufacturing industry and knowledge output reflected in the number of effective invention patents and new product projects from 2011 to 2023,and most of the relevant indexes remain within the top four in the country,but there is a significant shortfall in terms of income from new product sales.The growth rate of income from new product sales of the pharmaceutical manufacturing industry in Guangdong Province from 2021 to 2023 is 19.89%,6%and-21.29%,respectively,with a slowdown in the growth rate.At the same time,its income from new product sales compared with Jiangsu Province,Shandong Province and Zhejiang Province,there is a significant gap.In addition,the share of export income from new products within the overall income generated by new products of Guangdong’s pharmaceutical manufacturing industry from 2011 to 2023 is the largest at16.13%,the lowest at 1.27%,and falls to 8.28%in 2023,indicating that exports fluctuate significantly and account for a relatively low proportion.(2)Based on the results of static efficiency measurement,it is found that the improved cross-efficiency DEA model makes the evaluation results more objective through full sorting capability,effective differentiation,fine-tuned ranking and optimization of weights.According to the measurement results,the efficiency of Guangdong Province is more prominent in the innovation development stage,and the fluctuation of efficiency in the achievement transformation stage is smaller,but the overall efficiency level is not high.In the innovation development stage,the average value of allocation efficiency of innovation resources of the pharmaceutical manufacturing industry in Guangdong Province from 2011 to 2021 is 0.767,maintaining a stable ranking in the top seven,with efficiency values fluctuating between 0.578 and 0.852.In the achievement transformation stage,the average value of allocation efficiency of innovation resources of the pharmaceutical manufacturing industry in Guangdong from 2012 to 2022 descends to 0.680,ranking twelfth.During this period,the efficiency values of seven years range between 0.6and 0.7,with little fluctuation.(3)Based on the results of dynamic efficiency measurement,the improvement of total factor productivity mainly stems from the synergistic effect of technological progress and technological efficiency in the innovation development stage and the achievement transformation stage.In the innovation development stage,the average value of total factor productivity index of the pharmaceutical manufacturing industry in Guangdong Province from 2011 to 2021 is 1.245,ranking the first.Among them,the growth of changing index of technological progress is 17.7%,and the growth of changing index of technological efficiency is 9.8%.At the achievement transformation stage,the average value of total factor productivity index from 2012 to 2022 is 1.202,ranking fourth.Among them,the growth of changing index of technological progress is 14%,and the growth of changing index of technological efficiency is 11.7%.(4)Comprehensively analyzing the dynamic and static results of allocation efficiency of innovation resources of the pharmaceutical manufacturing industry in 24 regions,it is found that Guangdong Province belongs to the"high-high"type in the innovation development stage and the achievement transformation stage,and the eastern regions occupy more seats in the"high-high"type,occupying six seats in the innovation development stage and the achievement transformation stage.(5)A comparative analysis of Guangdong Province with Zhejiang Province,Shandong Province and Beijing City,which are also in the"high-high"category,reveals that the efficiency performance of Guangdong Province at different stages is characterized by differentiation.At the innovation development stage,Guangdong Province is characterized by"comprehensive leadership but relatively insufficient average advantages".At the achievement transformation stage,it is characterized by"steady growth but relative lack of efficiency".(6)The effect of influencing factors of allocation efficiency of innovation resources of the pharmaceutical manufacturing industry shows significant characteristics in stages,and there are differences between the"high-high"regions and non-"high-high"regions.On this basis,this paper analyzes the trend of influencing factors of allocation efficiency of innovation resources of the pharmaceutical manufacturing industry in the"high-high"regions,finding that the scale of the pharmaceutical manufacturing enterprises in Guangdong Province is relatively small and the other factors show an upward trend except for the weakening of government support and growth capacity.Finally,based on the results of the above analysis,this paper puts forward the following enhancement suggestions:Optimizing the policy support mode to strengthen the guiding role of policies.Optimizing the regional innovation environment to create a positive innovation atmosphere.Improving the related achievement transformation mechanism to accurately match market demands and break through efficiency bottlenecks.Constructing a multi-dimensional talent support system to stimulate the power of independent innovation.Taking digital transformation as the core in order to accelerate the development of intelligent pharmaceutical industry cluster.Optimizing the structure of resource input and focusing on improving the export link to strengthen the overall performance of input and output.更多还原