Research on Evaluation and Prediction for Enhancing the Innovation Capabilities of Manufacturing Enterprises

Zhuoxun Che (), Yanxin Chen (), Yanluo He (), Yangna Yin () and Qiuquan Guo ()
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Zhuoxun Che: University of Electronic Science and Technology of China, ShenSi Lab, Shenzhen Institute for Advanced Study
Yanxin Chen: University of Electronic Science and Technology of China, ShenSi Lab, Shenzhen Institute for Advanced Study
Yanluo He: University of Electronic Science and Technology of China, ShenSi Lab, Shenzhen Institute for Advanced Study
Yangna Yin: University of Electronic Science and Technology of China, ShenSi Lab, Shenzhen Institute for Advanced Study
Qiuquan Guo: University of Electronic Science and Technology of China, ShenSi Lab, Shenzhen Institute for Advanced Study

A chapter in Proceedings of 2025 2nd International Conference on Applied Economics, Management Science and Social Development (AEMSS 2025), 2025, pp 268-275 from Springer

Abstract: Abstract To overcome the limitations of traditional evaluation methods, this study proposes a novel approach that combines TOPSIS analysis, mutual information (MI), and Bagging regression model to comprehensively assess the innovation capability of manufacturing enterprises. This innovative method not only provides quantitative indicators for enterprise innovation ability but also uncovers key factors influencing such ability through eigenvalue analysis. Moreover, it offers personalized innovation guidance based on evaluation results, aiming to help enterprises identify their unique innovation potential and develop feasible strategies. The findings from this research not only facilitate rational planning of the innovation path and enhancement of market competitiveness for enterprises but also contribute new ideas and methodologies to foster innovation in the manufacturing industry.

Keywords: TOPSIS; MI; Bagging Regression; Machine Learning (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6463-752-6_28

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DOI: 10.2991/978-94-6463-752-6_28

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