期刊文献+

中国大数据产业技术效率及其影响因素分析 被引量:16

Analysis on Technology Efficiency of China Big-data Industry and its Affecting Factors
下载PDF
导出
摘要 基于中国大数据产业上市公司的面板数据,采用DEA方法测度并分析技术效率及其分解指标的变动趋势。结果表明,中国大数据产业技术效率较为低下,其主要原因在于纯技术效率增长拖累;东部地区技术效率和纯技术效率均高于中西部地区,但规模效率水平低于中西部地区;从变动趋势看,考察期内中国大数据产业技术效率及其构成总体均呈现波动下降的"U"型动态演变趋势,但不同区域的变动存在一定差异。进一步分析发现,盈利水平对纯技术效率起到了显著的促进作用,但阻碍了规模效率的提高;资本结构显著抑制了纯技术效率提升,却不利于规模效率改善;资金运用能力对纯技术效率和规模效率均有促进作用;成长能力不足和收益质量不高均在一定程度上抑制了纯技术效率和规模效率的提升。 Based on the panel data of China big - data industrial listed companies and using DEA method, this paper evaluates and analyzes the changing tendency of the technology efficiency and the decomposition indicator. The study shows that the technology efficiency of big - data industry is relatively low, which is mainly caused by the drag of the pure technology efficiency; both technology efficiency and the pure technology efficiency in western China are higher than central and western regions, while the scale efficiency is lower. From the perspective of tendency, there is the fluctuating decreased "U" type dynamic evolution trend on the China big - data industrial technology efficiency and its components. However, the changes in different regions differ. From the further analysis, we found that: the profitability highly promotes the pure technology efficiency, while blocks the improvement of scale efficiency; the capital structure significantly inhibits the pure tech- nical efficiency and is not conducive to improve scale efficiency; utilizing ability of the capital promotes both the pure technology efficiency and scale efficiency; to a certain extent, the inefficient growth and the poor profitability hinder the improvement of the pure technology efficiency and the scale efficiency.
作者 韩先锋 惠宁
出处 《科技管理研究》 CSSCI 北大核心 2016年第14期107-112,共6页 Science and Technology Management Research
基金 国家自然科学基金项目"西部区域创新环境质量评价 监测与空间差异研究"(71273209)
关键词 大数据 技术效率 纯技术效率 规模效率 影响因素 big- data technology efficiency pure technology efficiency scale efficiency affecting factors
  • 相关文献

参考文献10

二级参考文献43

  • 1陶雪娇,胡晓峰,刘洋.大数据研究综述[J].系统仿真学报,2013,25(S1):142-146. 被引量:344
  • 2王兵,颜鹏飞.技术效率、技术进步与东亚经济增长——基于APEC视角的实证分析[J].经济研究,2007,42(5):91-103. 被引量:97
  • 3Caves D W, Christensen L R, Diewert W E. The eco- nomic theory of index numbers and the measurement of input, output and productivity [ J ]. Econometrica, 1982, (6) : 1393 - 1414.
  • 4Grosskopf S. Efficiency and productivity [ A ]. Fried H, Lovell C A K, Schmidt S. The Measurement of Produc- tive Efficiency: Techniques and Applications [ C ]. Ox- ford : Oxford University Press, 1993 : 160 - 194.
  • 5Fare R, Grosskopf S, Norris M, Zhangz. Productivity growth, technical progress, and efficiency changes in in- dustrialised countries [ J ]. American Economic Review, 1994:66 - 84.
  • 6Fried H O, Lovell C A K, Schmidt S S, et al. Accounting tor environmental effects and statistical noise in data envelopment analysis[J].Journal of productivity Analysis, 2002, 17(1-2) : 157-174.
  • 7Pastor J T, Lovell C A. A global malmquist productivity index [J]. Economics Letters, 2005, 88.(2): 266-271.
  • 8Kumar S. Environmentally sensitive productivity growth: a global analysis using malmquist-luenberger index[J]. Ecological Economics, 2006, 56(2): 280-293.
  • 9Jonathan Stuart Ward, Adam Barker. Undefined By Data: A Survey of Big Data Definitions [EB/OL]. [2013-09-20] http: / /arxiv. org/abs/1309.5821.
  • 10Richard L V, Carl W O, Matthew Eastwood. Big Data: What It Is and Why You Should Care [J]. IDC Analyze the Future, 2011 (6).

共引文献205

同被引文献119

引证文献16

二级引证文献44

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部