Research On Innovation Performance Of China Big-data Industry

The emergence of Big-data has led to a great era of Industrial transformation, in which development and utilization of data resources spawn an emerging industry, namely Big-data industry. Big-data industry is not only a catalyst for the transformation and upgrading of traditional industries, but also a hotspot for economic growth, which has had a broad and profound impact on China's economic society. At present, China is accelerating the implementation of national Big-data strategy and cultivating Big-data industry as a new economic growth point, which is of great significance to promote China's economic development mode transformation and upgrading. Technological innovation is the source and inexhaustible motive force of the development of Big-data industry. And the innovation performance directly affects the practice effect of innovation behavior.Based on a comprehensive analysis of the status quo of Big-data industry in China, this paper uses DEA method to measure the technical efficiency, pure technical efficiency and scale efficiency of Big-data industry innovation. The main conclusions are as follows: the overall innovation performance is not high, and the low pure technical efficiency is the main inducement; the whole industry is in the stage of diminishing returns to scale, and the innovation factors are over-invested; the innovation performance has shown regional difference, of which the Pearl River Delta is at the highest level, while the central-western regions is at the lowest.In order to further analyze the influencing factors of innovation performance, this paper estimates the coefficient of each innovation factor by Random Effects Mode. The main conclusions are as follow: innovation investment is the main factor influencing the innovation performance of Big-data industry, among which capital investment is the most influential,but has a negative effect; organizational management, market demand and firm size have significant positive impacts on innovation performance,while the government support poses a weak negative impact.Based on the above results of analysis, this paper puts forward some countermeasures and suggestions to improve the innovation performance of China's Big-data industry, including defining the government function orientation and creating a good environment for innovation activities;strengthening the construction of an industrial ecological system and improving the ability of collaborative innovation; optimizing the internal innovation mechanism to enhance innovation quality.Compared with other scholars' studies, two innovations can be found in this dissertation. Firstly, the topic and research perspective of this paper is relatively novel. As is known to all, Big-data industry is an emerging industry and the related studies are in the exploratory stage, especially the research on industrial innovation performance is very few. With the desire for academic exploration, I have estimated the innovation performance of Big-data industry in 4 regions of China, which has never be done before.Secondly, the analytical method of this paper is relatively unique. Although the DEA method and Random Effect Model are not the first of their own,and a few of scholars have begun to do empirical research on the innovation performance of Big-data industry, there is no scholar to build Big-data industry innovation performance evaluation index system from the perspective of multi-input and multi-output precedent. In addition, this paper constructs an influencing factors system of Big-data industry innovation performance based on Potter diamond theory of national innovation system, and uses the Random Effects Model by regression analysis, which also can be said to be the originality.