中国大数据产业创新绩效研究

发布时间：2018-06-25 02:57

本文选题：大数据产业 + 创新绩效　；参考：《北京邮电大学》2017年硕士论文

【摘要】：大数据开启了一次重大的产业变革时代,围绕数据资源的开发利用催生出一种新兴业态——大数据产业。大数据既是传统产业转型升级的“催化剂”,同时也是经济发展的增长热点,对我国经济社会产生了广泛而深刻的影响。当前,我国加快实施国家大数据战略,将大数据产业作为新的经济增长点加以培育,进而推动我国经济发展方式转型升级。技术创新是大数据产业发展的源泉和不竭动力,创新绩效直接影响产业创新行为的实践效果。以全面分析我国大数据产业发展现状为切入点,本文运用DEA方法测度了大数据产业创新的综合效率、纯技术效率和规模效率,得出的主要结论有:创新绩效总体水平不高,纯技术效率偏低是主要诱因;产业总体处于规模收益递减阶段,创新要素投入过度化问题严重;创新绩效水平存在区域差异,珠三角最高,中西部地区最低。为了进一步分析大数据产业创新绩效影响因素,本文运用随机效应模型分析了各个创新要素的影响系数,得出的主要结论有:创新投入是影响大数据产业创新绩效的主要因素,其中资本投入比人力投入影响力度大,但二者均呈负向效应;组织管理、市场需求和企业规模对创新绩效均有显著的正向影响;政府扶持对创新绩效有微弱的负向影响。基于以上分析结果,本文提出了我国大数据产业创新绩效提升的对策建议,包括明确政府功能定位,营造良好创新环境;加强产业生态体系建设,提高协同创新能力;优化企业内部创新机制,提升创新成果质量。与同类研究成果相比,本文有这样两个独到之处:1.选题和研究视角新颖。大数据产业是一种新兴业态,相关研究处于探索阶段,尤其是产业创新绩效方面的研究文章实不多见。本文大胆探索、另辟蹊径,从京津冀、长三角、珠三角、中西部4个维度对大数据产业创新绩效进行评价分析,在国内学术界尚属首次。2.选用的分析方法新颖。尽管DEA方法和随机效应模型并非本人首创,而且也有少数学者开始对大数据产业创新绩效问题进行实证研究,从现有的文献来看,还没有学者从多投入多产出视角构建大数据产业创新绩效评价指标体系的先例。另外,本文基于波特国家创新系统钻石理论构建大数据产业创新绩效影响因素体系,并运用随机效应模型进行回归分析,也可以说是本文研究的独到之处。
[Abstract]:Big data has opened an important era of industrial transformation, and the development and utilization of data resources has given birth to a new type of industry-big data industry. Big data is not only a "catalyst" for the transformation and upgrading of traditional industries, but also a hot point of economic development, which has a wide and profound impact on the economy and society of our country. At present, our country speeds up the implementation of the national big data strategy, cultivates the big data industry as the new economic growth point, and then promotes the transformation and upgrading of our country's economic development mode. Technological innovation is the source and inexhaustible power of big data industry development, and innovation performance directly affects the practical effect of industrial innovation behavior. Based on the comprehensive analysis of the current situation of big data industry development in China, this paper measures the comprehensive efficiency, pure technical efficiency and scale efficiency of big data industry innovation by using DEA method. The main conclusions are as follows: the overall level of innovation performance is not high. The low efficiency of pure technology is the main inducement; the industry is in the stage of diminishing returns on scale in general, the problem of excessive investment of innovation elements is serious; the level of innovation performance exists regional differences, the Pearl River Delta is the highest, and the central and western regions are the lowest. In order to further analyze the influencing factors of industrial innovation performance of big data, this paper analyzes the influence coefficient of each innovation factor by using stochastic effect model. The main conclusions are as follows: innovation investment is the main factor influencing the innovation performance of big data industry. Among them, capital investment has more influence than manpower input, but both have negative effect; organizational management, market demand and enterprise scale have significant positive influence on innovation performance; government support has weak negative effect on innovation performance. Based on the above analysis results, this paper puts forward the countermeasures and suggestions for improving the industrial innovation performance of big data in China, including defining the function of the government, creating a good innovation environment, strengthening the construction of industrial ecological system and improving the ability of collaborative innovation. Optimize the internal innovation mechanism and improve the quality of innovation results. Compared with similar research results, this paper has two unique features: 1. The selection of topics and the perspective of research are novel. Big data industry is a new type of industry. The related research is still in the exploratory stage, especially in the field of industrial innovation performance. This article boldly explores, another way, from the Beijing-Tianjin-Hebei, Yangtze River Delta, Pearl River Delta, the central and western four dimensions of big data industry innovation performance evaluation analysis, in domestic academia is the first. 2. The analytical method selected is novel. Although DEA method and stochastic effect model are not the first by myself, and a few scholars have begun to do empirical research on the performance of big data industry innovation, from the existing literature, There is no precedent for scholars to construct the performance evaluation index system of big data industry innovation from the perspective of multi-input and multi-output. In addition, this paper based on Porter National Innovation system Diamond theory to construct the big data industry innovation performance impact factors system, and the use of stochastic effect model for regression analysis, it can be said that this study is unique.
【学位授予单位】：北京邮电大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：F273.1;F49

【参考文献】