Research On The Technical Innovation Efficiency Of Big Data Listed Companies

Science and technology are the primary productive forces.With the rapid development of the Internet,the networking and cloud computing,and the other information of science and technology result in a large number of datum of human material and cultural life.People began to realize that there are much economical and political value in the datum,therefore,the big data industry came into being,and entered into the big data market in 2012.In the same year,the Chinese government launched many development plans to support the big data industry which had promoted the development of the big data industry.By 2014,the development of the big data industry has entered a stage of rapid development.According to the relevant datum,between 2011 and 2015 year,the GDP of China are different,the market scales of China's big data industry are also different.the proportions of market scales to GDP are getting high.We can see that the market scale of China's big data is increasing year by year,and the proportion of GDP increases year by year.Besides the proportion of 2015 is as high as 69.16%,which has a significant impact on China's economy.In addition,year on year growth rates of the big data market scale are getting fast.These data show that China's big data industry develops faster and faster,gradually becoming a new growth point of economic development.So it is necessary to vigorously develop the big data industry.And the level of the technical innovation efficiency determines the good or bad of the development of enterprises.The big data of listed companies are the main subject of the big data industry,thus we need enhance-the technical innovation efficiency of the big data listed company and promote the development of big data industry,and promote China's economic growth in the end.In order to promote the development of national economy,in fact this paper uses three stage DEA method to study the technical innovation efficiency of 30 listed companies between 2012 and 2015.In the first stage,I take the environmental factors and random errors into consideration,establishing innovation efficiency evaluation system and estimating the technical innovation efficiency of the big data listed company from the two dimensions of investment and personnel input,namely economic benefits,and scientific and technological achievements.In the second stage,I remove the environmental factors and random errors.In the third stage,I estimate the technical innovation efficiency excluding the environmental factors and random errors.The results show that environmental factors and random errors have a significant impact on the efficiency of listed companies.After stripping environmental factors and random errors in the second stage,total efficiency,pure technical efficiency and scale efficiency have increased in the third stage,besides the number of listed companies that are effective in DEA model has increased.These suggest that the effect of environmental factors and random errors of big data on technical innovation efficiency of listed companies is not good,as the whole level,and the estimated results show that the average of pure technical efficiency is lower than scale efficiency,as the whole level,which shows that the pure technical efficiency is the main reason that lows the level of the technical innovation efficiency.Finally,according to the conclusions,this paper aimed to enhance the technical innovation efficiency of the big data listed companies and put forward to three suggestions,such as optimizing the allocation of resources,creating favorable environment conditions and enhancing the management and technical level in order to promote the development of China's economy.The innovation of this paper is that the research field is novel,and it is suitable for the development of the times.At the same time,the three stage DEA research method avoids the disadvantages of the traditional DEA and stochastic frontier analysis which are used by many scholars at present.However,due to the limited level of research,this paper has some shortcomings,such as the small number of samples,the subjectivity of sample selection and the lack of data acquisition.