技术创新、效率改进与企业高质量增长的技术路径

doi:10.16381/j.cnki.issn1003-207x.2023.1979

摘要/Abstract

摘要：

数据包络分析（DEA）是分析企业提质增效的重要技术手段。其中，DEA投影分析是该方法最突出的亮点和优势。但DEA投影也存在改进信息过于简单、改进方案的可操作性不足等问题。比如，DEA投影只能估计与优秀决策单元的差距，而不能为无效单元确定阶段性改进目标。因此，本文首先提出基于不同效率改进目标的DEA模型，然后，给出基于不同效率改进目标下的DEA投影公式。其次，提出测算决策单元效率改进和技术创新难度的定量方法，并进一步给出决策单元在多种效率目标下的改进策略。最后，应用该方法分析中国互联网行业上市企业实现高质量增长的技术路径。研究结果表明：本文方法不仅优化了传统DEA投影方法，而且也丰富了逆DEA模型体系，并为企业实现高质量增长提供了分析工具。

关键词: 技术创新, 效率改进, 企业高质量增长, 逆DEA模型, 面板数据

Abstract:

High-quality development is an important theme for China's economic development at present and in the coming period. It is also an important guarantee for the realization of Chinese-style modernization. As an important microeconomic entity in China， enterprises play an important role in driving China's economic development. High-quality growth of enterprises is crucial to realizing high-quality development of the Chinese economy. Therefore， it is of great practical significance to explore the theoretical and practical issues of realizing high-quality growth of enterprises.Data Envelopment Analysis （DEA） is an important technical tool to analyze the quality and efficiency of enterprises. DEA projection analysis is the most prominent highlight and advantage of this method. The traditional DEA model takes the production frontier constructed by effective decision-making units as a reference， and the effective level that decision-making units may reach can be predicted through DEA projection， which gives the direction and degree of adjustment of each evaluation index， and has a very prominent advantage. However， the DEA projection method also has some aspects that need to be improved.First of all， DEA projection can only provide effective improvement strategies for ineffective decision-making units， but cannot provide references for the stage-by-stage improvement goals of ineffective units. For example， the annual growth targets of listed companies in China are not always DEA effective， and DEA projection cannot provide improvement strategies for such targets. Second， in many cases， decision units are not always improved to DEA effective. For example， not every ordinary person can become a world champion through training. In fact， the probability of an average person becoming a world champion is negligible. If some almost unattainable goals are used as a guide for the decision unit's behavior， this will not only seriously mislead the decision unit's strategy formulation， but also may cause significant damage to the decision unit. Finally， the improvement information given by the DEA prediction also suffers from oversimplification of the improvement information and poor operability of the improvement program. In order to further solve the shortcomings of the DEA projection method and provide enterprises with improvement strategies based on a variety of expected goals， a DEA model is proposed based on different efficiency improvement goals. Then， the DEA projection formula based on different efficiency improvement goals is given. Secondly， a quantitative method for measuring the difficulty of efficiency improvement and technological innovation of decision-making units is proposed， and furthermore， the improvement strategies of decision-making units under multiple efficiency objectives are given. Finally， the method is applied to analyze the efficiency status and growth trend of Chinese Internet listed companies from 2018 to 2022. The results show that the method of this paper not only optimizes the traditional DEA projection method， but also enriches the inverse DEA model system， and provides technical support and reference for enterprises to achieve high-quality growth.

Key words: technological innovation, efficiency improvement, technological path of high-quality growth for enterprises, inverse DEA model, panel data

中图分类号:

马占新,白洁,田雨珍. 技术创新、效率改进与企业高质量增长的技术路径[J]. 中国管理科学, 2026, 34(7): 49-60.

Zhanxin Ma,Jie Bai,Yuzhen Tian. Technological Innovation, Efficiency Improvement and the Technological Path of High-quality Growth for Enterprises[J]. Chinese Journal of Management Science, 2026, 34(7): 49-60.

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决策单元	A	B	C	D	E
投入（x）	2	4	4	8	8
产出（y）	2	6	2	6	6
效率值	1	1	0.5	0.5	0.5

决策单元	年度效率增长值			2023年预期
决策单元	最小值	平均值	最大值	效率目标值
C	0.05	0.10	0.15	0.6
D	0.02	0.05	0.10	0.6
E	-0.20	-0.10	-0.05	0.6

决策单元	效率值（θ）
企业A	1
企业B	0.80
企业C	0.75
企业D	0.32
行业平均	0.72

决策单元	效率值（θ）
企业A	1.2
企业B	1.0
企业C	1.0
企业D	0.72

企业名称	目标效率	投入投影	投入改进幅度（%）	产出投影
企业A	1.20	13.33	16.67	40
企业B	1.00	19.20	20.00	48
企业C	1.00	24.00	25.00	60
企业D	0.72	17.78	55.55	32