北京邮电大学学报(社科版) ›› 2017, Vol. 19 ›› Issue (1): 71-78.

• 经济与哲学 • 上一篇    下一篇

基于DEA方法的中国大数据企业创新绩效评价研究

  

  1. 北京邮电大学 经济管理学院,北京100876
  • 收稿日期:2016-10-18 出版日期:2017-02-28

Evaluation of Innovation Efficiency of Big Data Enterprises in China#br# ——Based on DEA Method

  1. School of Economics and Management, Beijing University of Posts and Telecommunications,
    Beijing 100876, China
  • Received:2016-10-18 Online:2017-02-28

摘要: 创新绩效是影响企业竞争力的重要因素。选取2015年32家样本大数据企业数据,运用DEA方法对大数据企业创新绩效进行实证分析,研究结果表明:中国大数据企业创新绩效整体水平低,纯技术效率偏低是主要诱因;大数据企业DEA无效现象普遍;大数据企业总体处于规模收益递增阶段,创新要素投入规模偏小;企业投入冗余和产出不足问题严重,投入冗余是技术无效的主因。产业链层级上,应用与服务层企业的创新绩效问题尤其严重。为了提高大数据企业创新绩效,在实证结果分析的同时提出相应对策建议。

关键词: 大数据企业, 大数据产业链, 创新绩效, DEA方法

Abstract: Innovation efficiency has always been the critical factor for strengthening competition power of enterprises Based on data from 32 big data enterprises in the year of 2015, DEA method is used to calculate efficiency values and analyze the influencing factors of innovation performance The results show that the overall level of innovation efficiency of big data enterprises is low, especially for technical efficiency; invalidation of DEA results is prevalent; most big data enterprises are in the stage of increasing returns to scale with the insufficient input of innovation; input redundancy and output insufficiency, especially the former, are severe to big data enterprises In the layer of industrial chain, big data enterprises in application-service layer is confronting more serious problems To improve the innovation efficiency of big data enterprises, corresponding countermeasures are put forward at the end of empirical part

Key words: big data enterprise, big data industrial chain, innovation efficiency, DEA method

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