本文選題：大數(shù)據(jù) + 上市公司　； 參考：《安徽大學(xué)》2017年碩士論文

【摘要】：科學(xué)技術(shù)是第一生產(chǎn)力,隨著互聯(lián)網(wǎng)、物聯(lián)網(wǎng)和云計(jì)算等信息科學(xué)技術(shù)的迅猛發(fā)展,人類(lèi)物質(zhì)文化生活產(chǎn)生了大量的數(shù)據(jù),人們開(kāi)始意識(shí)到海量的數(shù)據(jù)當(dāng)中蘊(yùn)藏著豐富的經(jīng)濟(jì)和政治價(jià)值,因此,大數(shù)據(jù)產(chǎn)業(yè)應(yīng)運(yùn)而生,并且在2012年進(jìn)入了大數(shù)據(jù)市場(chǎng)成長(zhǎng)時(shí)期,同年,我國(guó)政府推出了相關(guān)的大數(shù)據(jù)發(fā)展扶持計(jì)劃,促進(jìn)了大數(shù)據(jù)產(chǎn)業(yè)的發(fā)展。我國(guó)大數(shù)據(jù)市場(chǎng)規(guī)模逐年增大,并且占國(guó)內(nèi)生產(chǎn)總值的比例逐年增大,到2015年則高達(dá)69.16%,對(duì)我國(guó)經(jīng)濟(jì)具有重大影響,此外大數(shù)據(jù)市場(chǎng)規(guī)模同比增長(zhǎng)率也很大,這說(shuō)明我國(guó)大數(shù)據(jù)產(chǎn)業(yè)發(fā)展速度越來(lái)越快,成為經(jīng)濟(jì)發(fā)展新的增長(zhǎng)點(diǎn),因此要大力發(fā)展大數(shù)據(jù)產(chǎn)業(yè)。而技術(shù)創(chuàng)新效率水平的高低決定著企業(yè)發(fā)展的好壞,大數(shù)據(jù)類(lèi)上市公司是大數(shù)據(jù)產(chǎn)業(yè)的主體,因此要提升大數(shù)據(jù)類(lèi)上市公司的技術(shù)創(chuàng)新效率,進(jìn)而促進(jìn)大數(shù)據(jù)產(chǎn)業(yè)發(fā)展,并在最終推動(dòng)我國(guó)國(guó)民經(jīng)濟(jì)的增長(zhǎng)。在推動(dòng)國(guó)民經(jīng)濟(jì)發(fā)展的需求下,本文運(yùn)用三階段DEA方法對(duì)2012-2015年30家大數(shù)據(jù)類(lèi)上市公司進(jìn)行技術(shù)創(chuàng)新效率研究。在第一階段,將環(huán)境因素和隨機(jī)誤差項(xiàng)考慮在內(nèi),建立創(chuàng)新效率評(píng)價(jià)體系,從經(jīng)費(fèi)投入和人員投入以及經(jīng)濟(jì)效益和科技成果產(chǎn)出兩個(gè)維度測(cè)算大數(shù)據(jù)類(lèi)上市公司的技術(shù)創(chuàng)新效率;在第二階段,將環(huán)境因素和隨機(jī)誤差項(xiàng)剝離;在第三階段,測(cè)算剔除了環(huán)境因素和隨機(jī)誤差項(xiàng)后的技術(shù)創(chuàng)新效率。結(jié)果表明:環(huán)境因素和隨機(jī)誤差項(xiàng)對(duì)大數(shù)據(jù)類(lèi)上市公司技術(shù)創(chuàng)新效率具有顯著的影響。在第二階段,剝離環(huán)境因素和隨機(jī)誤差項(xiàng)后,第三階段測(cè)算的綜合效率、純技術(shù)效率和規(guī)模效率,大部分企業(yè)的技術(shù)創(chuàng)新效率所提高,并且DEA有效的企業(yè)數(shù)量增加。這說(shuō)明環(huán)境因素和隨機(jī)誤差項(xiàng)對(duì)大數(shù)據(jù)類(lèi)上市公司的技術(shù)創(chuàng)新效率在整體上表現(xiàn)為不利影響,同時(shí)測(cè)算的結(jié)果表明,純技術(shù)效率均值整體上低于規(guī)模效率均值,這說(shuō)明,導(dǎo)致企業(yè)技術(shù)創(chuàng)新效率水平低下主要是純技術(shù)效率。最后,根據(jù)得出的結(jié)論,本文針對(duì)提升大數(shù)據(jù)類(lèi)上市公司技術(shù)創(chuàng)新效率,提出了優(yōu)化資源配置、創(chuàng)造有利環(huán)境條件和提升經(jīng)營(yíng)管理和技術(shù)水平三個(gè)建議,以期促進(jìn)我國(guó)經(jīng)濟(jì)的發(fā)展。本文的創(chuàng)新點(diǎn)在于研究領(lǐng)域新穎,切合時(shí)代發(fā)展實(shí)際;同時(shí)所用三階段DEA研究方法避免了很多學(xué)者目前較多使用的傳統(tǒng)DEA和隨機(jī)前沿分析法的缺點(diǎn)。然而,由于研究水平有限,本文存在著研究樣本數(shù)量較少、樣本篩選存在主觀性、數(shù)據(jù)獲取不全等不足之處。
[Abstract]:Science and technology is the first productivity, with the rapid development of information science and technology, such as the Internet of things, Internet of things and cloud computing, human material and cultural life has produced a large number of data.People began to realize that there were abundant economic and political values in the huge amount of data. Therefore, big data industry came into being, and in 2012 it entered the period of market growth of big data, the same year.China's government launched the relevant big data development support plan, promoted the big data industry's development.The market scale of big data in China has increased year by year, and the proportion of GDP has increased year by year. By 2015, it will be as high as 69.16, which has a significant impact on the economy of our country. In addition, the market scale of big data is also growing at a very large rate from the same period last year.This shows that big data's industry is developing more and more rapidly and becomes a new growth point of economic development.The level of technological innovation efficiency determines the quality of enterprise development. Big data listed company is the main body of big data industry. Therefore, we should improve the efficiency of technological innovation of big data listed companies, and then promote the industrial development of big data.And in the end promote the growth of our national economy.Under the demand of promoting the development of national economy, this paper studies the technological innovation efficiency of 30 big data listed companies in 2012-2015 by using the three-stage DEA method.In the first stage, environmental factors and random errors are taken into account to establish an innovation efficiency evaluation system.The technological innovation efficiency of big data listed companies is measured from the aspects of investment of funds and personnel, economic benefits and output of scientific and technological achievements. In the second stage, environmental factors and random errors are separated. In the third stage,The efficiency of technological innovation is calculated after excluding environmental factors and random errors.The results show that environmental factors and random errors have a significant impact on the efficiency of technological innovation of big data listed companies.In the second stage, after stripping off the environmental factors and random errors, the comprehensive efficiency, pure technical efficiency and scale efficiency of the third stage are increased, the technological innovation efficiency of most enterprises is improved, and the number of DEA efficient enterprises increases.This shows that environmental factors and random errors have a negative impact on the efficiency of technological innovation of big data listed companies on the whole, and the results of the calculation show that the average value of pure technical efficiency is lower than the average of scale efficiency as a whole.The low level of technological innovation efficiency of enterprises is mainly pure technical efficiency.Finally, according to the conclusions, this paper puts forward three suggestions to improve the efficiency of technological innovation of big data listed companies, such as optimizing the allocation of resources, creating favorable environmental conditions and enhancing the management and technology level.With a view to promoting the economic development of our country.The innovation of this paper is that the research field is novel and suitable for the development of the times, and the three-stage DEA research method avoids the disadvantages of traditional DEA and stochastic frontier analysis, which are widely used by many scholars at present.However, due to the limited level of research, there are some shortcomings in this paper, such as the small number of samples, the subjectivity of sample selection and the incomplete data acquisition.
【學(xué)位授予單位】：安徽大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：F49;F273.1

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 趙婧;吳珍珠;謝朝華;;金融支持促進(jìn)技術(shù)創(chuàng)新的區(qū)域性差異研究[J];財(cái)經(jīng)理論與實(shí)踐;2016年05期

2 包英群;魯若愚;熊麟;;基于二階段DEA模型中國(guó)平板顯示產(chǎn)業(yè)創(chuàng)新效率研究[J];科學(xué)學(xué)與科學(xué)技術(shù)管理;2016年09期

3 韓東林;葛磊;程琪;;基于DEA模型的中國(guó)物聯(lián)網(wǎng)上市公司創(chuàng)新效率評(píng)價(jià)[J];科技管理研究;2015年15期

4 李天柱;王圣慧;馬佳;;基于概念置換的大數(shù)據(jù)定義研究[J];科技管理研究;2015年12期

5 姜強(qiáng);趙蔚;王朋嬌;王麗萍;;基于大數(shù)據(jù)的個(gè)性化自適應(yīng)在線學(xué)習(xí)分析模型及實(shí)現(xiàn)[J];中國(guó)電化教育;2015年01期

6 秦榮生;;大數(shù)據(jù)、云計(jì)算技術(shù)對(duì)審計(jì)的影響研究[J];審計(jì)研究;2014年06期

7 熊嬋;買(mǎi)憶媛;何曉斌;肖仁橋;;基于DEA方法的中國(guó)高科技創(chuàng)業(yè)企業(yè)運(yùn)營(yíng)效率研究[J];管理科學(xué);2014年02期

8 迪莉婭;;我國(guó)大數(shù)據(jù)產(chǎn)業(yè)發(fā)展研究[J];科技進(jìn)步與對(duì)策;2014年04期

9 張軍;姚飛;;大數(shù)據(jù)時(shí)代的國(guó)家創(chuàng)新系統(tǒng)構(gòu)建問(wèn)題研究[J];中國(guó)科技論壇;2013年12期

10 嚴(yán)霄鳳;張德馨;;大數(shù)據(jù)研究[J];計(jì)算機(jī)技術(shù)與發(fā)展;2013年04期

，

本文編號(hào)：1756184