Research on the Innovation Capability Evaluation of Science and Technology-Based Large and Medium-Sized Enterprises Based on the Artificial Neural Network Under Coupling Weights

DOI

10.2991/aebmr.k.220502.064How to use a DOI?

Keywords

AHP; CRITIC; ANN; Technology-based Enterprises; Innovation Capability; Evaluation Study

Abstract

Science and technology innovation capability is the core competitiveness for the long-term development of science and technology-based enterprises. In this paper, we construct an evaluation system of innovation capability of science and technology-based large and medium-sized enterprises by taking listed science and technology-based enterprises in Jiangsu Province in China’s Shanghai and Shenzhen A-shares. The coupling method of AHP subjective assignment and CRITIC objective assignment is used to assign weights to the indicators, and the data are subjected to ANN training to find the best neural network structure, so as to achieve the evaluation of innovation capability of science and technology-based large and medium-sized enterprises. The results show that the growth rate of R&D expenses and the number of invention patent applications are the most important. R&D investment and innovation output are more important. This study uses quantitative methods to evaluate the innovation ability of large and medium-sized scientific and technological enterprises, providing enterprises with a reality evaluation and decision-making basis, with certain guiding significance.

Copyright

© 2022 The Authors. Published by Atlantis Press International B.V.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license.

Download article (PDF)

TY  - CONF
AU  - Ziqing Ye
AU  - Xiangcheng Qin
PY  - 2022
DA  - 2022/05/16
TI  - Research on the Innovation Capability Evaluation of Science and Technology-Based Large and Medium-Sized Enterprises Based on the Artificial Neural Network Under Coupling Weights
BT  - Proceedings of the 2022 International Conference on Urban Planning and Regional Economy（UPRE 2022）
PB  - Atlantis Press
SP  - 356
EP  - 363
SN  - 2352-5428
UR  - https://doi.org/10.2991/aebmr.k.220502.064
DO  - 10.2991/aebmr.k.220502.064
ID  - Ye2022
ER  -