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Article type: Research Article
Authors: Zhang, Qianshaa | Zhang, Huiqib; * | Lo, Weia
Affiliations: [a] School of Business Administration, Guangxi University of Finance and Economics, Nanning, China | [b] School of Economics and Management, Beijing Jiaotong University, Beijing, China
Correspondence: [*] Corresponding author: Huiqi Zhang, School of Economics and Management, Beijing Jiaotong University, Beijing, China. E-mail: [email protected].
Abstract: In the face of increasingly severe global environmental problems, green transformation of the manufacturing industry has become a key issue. This study aims to explore the interaction between green innovation, intelligent decision-making and risk management in manufacturing industry. The purpose of this study is to explore the relationship between intelligent decision-making, risk management and green innovation, as well as the role and impact of risk management in the process of green innovation in manufacturing companies by building a statistical model. In this study, we investigated the intelligent decision-making problem and the risk management problem in the green transformation process through the literature survey method and statistical analysis. The results of the study show that intelligent decision-making positively affects the green innovation capability of manufacturing enterprises, there is a negative correlation between the complexity of risk management and the green innovation capability of manufacturing enterprises, and when manufacturing enterprises rely more on intelligent decision-making, the impact of risk management (positively or negatively) on green innovation is amplified, indicating an interactive effect between intelligent decision-making and risk management. This study provides valuable insights for the manufacturing industry to enhance smart decision-making in the face of environmental challenges.
Keywords: Manufacturing, green innovation, intelligent decision-making, risk management, sustainable transformation
DOI: 10.3233/JCM-247450
Journal: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 4-5, pp. 2317-2332, 2024
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