Research on Spatial Differentiation of Technological Innovation Efficiency of Enterprise Based on the Super-efficiency DEA-Malmquist Model

Abstract

Through the application of the super-efficiency DEA-Malmquist method, the article measures the efficiency of enterprise technological innovation, analyzes the characteristics of time and space evolution and efficiency change of technological innovation during 2006-2016, and quantitatively adjusts the input and output factors of improving enterprise technological innovation. The research shows that: first, the technical efficiency of technological innovation of enterprises: in terms of time series changes, the potential of input of existing production factors cannot be tapped, and the input of technical elements should be increased. Moreover, in the provinces with inefficient technical efficiency, there are widespread factors such as redundant input of factors and insufficient output of benefits. Each province and region can make quantitative adjustments to the input and output factors in the development of high-tech industries according to their own conditions. Second, enterprise technology innovation Malmquist efficiency index: average technical efficiency change, average technical level change and average total factor productivity change were 1.024, 0.976 and 0.996, respectively, both close to 1. There is a significant difference in technical efficiency between regions, and it is gradually developing towards economically developed areas along the southeast coast, and there is a significant spillover effect.

Evaluation of Technological Innovation Efficiency of New Energy Enterprises in the Yangtze River Delta Region—Based on a Two-Stage DEA Optimization Model

“…The nonparametric method refers to data envelopment analysis (DEA), which solves the problem of multi-input and multi-output by constructing the traditional DEA model (Andriamasy et al, 2014;Sun, 2020). Many scholars combine the DEA model with the Malmquist index to analyze the changing trends in technological innovation efficiency from the static and dynamic perspectives, respectively (Luo et al, 2019;Wang et al, 2019a;Liu et al, 2019;Sue & Ma, 2018). They regard the innovation process as a whole and directly measure overall efficiency without considering the internal structure rate (Tseng, 2009).…”