Loading...
Please wait, while we are loading the content...
Similar Documents
A Study on the Technological Innovation Efficiency and Influencing Factors of Iron and Steel Enterprises Based on DEA - Tobit Two-step Method
| Content Provider | Semantic Scholar |
|---|---|
| Author | Li, Baihua Yan, Junyin Wang, Gang |
| Copyright Year | 2018 |
| Abstract | The article analyzes the resource input of iron and steel enterprises in their technological innovation process by setting the input-oriented DEA effective frontier with the DEA method. On this basis, the regression analysis is carried out with respect to the influencing factors of the technological innovation efficiency in iron and steel enterprises with the Tobit regression analysis method. The study results show that 1 the averaged technological innovation efficiency of 12 enterprises is only 0.774, indicating the overall technological innovation efficiency of iron and steel enterprises is low; 2Overall, the contribution of the pure technical efficiency is greater than the scale efficiency, and the scale efficiency of iron and steel enterprises is generally low; 3There is a big difference in the technological innovation efficiency of different iron and steel enterprises;4 The industrial structure and the overall innovation environment of the region where an enterprise is located play an important promoting role in the improvement of the technological innovation efficiency of iron and steel enterprises; 5 Increasing the concentration of production capacity, improving the innovation and resource allocation in the process of technological innovation, and optimizing the regional industrial structure and regional innovation environment, is the effective way to effectively improve the technological innovation efficiency of iron and steel enterprises. 1 DEA-Tobit Two-step Method The DEA-Tobit two-step analysis method is an evaluation method derived from DEA analysis, which includes two-step extended analysis. The first step is to evaluate the technological innovation efficiency of each evaluation unit (DMU) through use of the DEA model; and the second step is to carry out the regression analysis by taking the DEA efficiency as the dependent variable, and the selected influencing factors as the independent variables, and to determine the influence degree on the efficiency value and change direction of all the influencing factors according to the measured independent variables, and identify the sensitive factors of the technological innovation efficiency, so as to provide guidance for the scientific planning of corresponding policies and recommendations. The Data Envelopment Analysis (DEA) can directly calculate the relative effective frontier of multiple decision units (DMUs) by using the evaluation data and mathematical programming model, thus achieving the effective evaluation on the relative efficiency of DMUs. After many years of research and development, the DEA model has evolved from the initial CCR model to BCC, and then extended to the super efficiency model and other models. Different models have different application conditions and assumptions, thus, can reflect different evaluation aspects of an evaluation object. When calculating the efficiency frontier with DEA, two methods may be used, i.e. the input-oriented method and the output-oriented method. Considering the production characteristics of iron and steel enterprises, we choose the input-oriented DEA model to study the innovation efficiency. 215 Copyright © 2018, the Authors. Published by Atlantis Press. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/). Advances in Intelligent Systems Research (AISR), volume 15 |
| File Format | PDF HTM / HTML |
| DOI | 10.2991/anit-17.2018.37 |
| Alternate Webpage(s) | https://download.atlantis-press.com/article/25889344.pdf |
| Alternate Webpage(s) | https://doi.org/10.2991/anit-17.2018.37 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
