How to Enhance Enterprises’ Radical Innovation Performance Through Multiple Pathways—A Machine Learning Analysis of SRDI Enterprises in China
Specialized, Refined, Differentiated, and Innovative (SRDI) enterprises are crucial to China’s economic development. It is important to examine how various factors’ combinations impact the radical innovation performance of SRDI enterprises in order to promote high-quality regional economic developme...
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| Format: | Article |
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MDPI AG
2025-03-01
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| Series: | Systems |
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| Online Access: | https://www.mdpi.com/2079-8954/13/3/198 |
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| author | Liping Zhang Hanhui Qiu Jinyi Chen Hailin Li Xiaoji Wan |
| author_facet | Liping Zhang Hanhui Qiu Jinyi Chen Hailin Li Xiaoji Wan |
| author_sort | Liping Zhang |
| collection | DOAJ |
| description | Specialized, Refined, Differentiated, and Innovative (SRDI) enterprises are crucial to China’s economic development. It is important to examine how various factors’ combinations impact the radical innovation performance of SRDI enterprises in order to promote high-quality regional economic development. Based on the Technology–Organization–Environment (TOE) framework, this study selected SRDI enterprises as research samples, used a hierarchical clustering algorithm to divide the enterprises into groups according to the characteristics of SRDI enterprises, and employed a classification and regression tree (CART) algorithm to reveal the complex nonlinear relationships between the combinations of multiple key influencing factors and radical innovation performance from multi-source big data. The findings indicate that (1) there are significant variations in the factors affecting the radical innovation performance of different types of SRDI enterprises; (2) the radical innovation performance of SRDI enterprises stems from the synergistic interaction among various factors; and (3) the impact of R&D investment on radical innovation is not simply linear. This study effectively captures the complex nonlinear relationships between combinations of multiple influencing factors and radical innovation performance. It is of great practical significance for revealing SRDI enterprises’ radical innovation performance improvement pathways and enhancing their innovation capability. |
| format | Article |
| id | doaj-art-03512c0c7bdc4745b9b8d742401ff3c2 |
| institution | OA Journals |
| issn | 2079-8954 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Systems |
| spelling | doaj-art-03512c0c7bdc4745b9b8d742401ff3c22025-08-20T01:48:58ZengMDPI AGSystems2079-89542025-03-0113319810.3390/systems13030198How to Enhance Enterprises’ Radical Innovation Performance Through Multiple Pathways—A Machine Learning Analysis of SRDI Enterprises in ChinaLiping Zhang0Hanhui Qiu1Jinyi Chen2Hailin Li3Xiaoji Wan4College of Business Administration, Huaqiao University, Quanzhou 362021, ChinaCollege of Business Administration, Huaqiao University, Quanzhou 362021, ChinaCollege of Business Administration, Huaqiao University, Quanzhou 362021, ChinaCollege of Business Administration, Huaqiao University, Quanzhou 362021, ChinaCollege of Business Administration, Huaqiao University, Quanzhou 362021, ChinaSpecialized, Refined, Differentiated, and Innovative (SRDI) enterprises are crucial to China’s economic development. It is important to examine how various factors’ combinations impact the radical innovation performance of SRDI enterprises in order to promote high-quality regional economic development. Based on the Technology–Organization–Environment (TOE) framework, this study selected SRDI enterprises as research samples, used a hierarchical clustering algorithm to divide the enterprises into groups according to the characteristics of SRDI enterprises, and employed a classification and regression tree (CART) algorithm to reveal the complex nonlinear relationships between the combinations of multiple key influencing factors and radical innovation performance from multi-source big data. The findings indicate that (1) there are significant variations in the factors affecting the radical innovation performance of different types of SRDI enterprises; (2) the radical innovation performance of SRDI enterprises stems from the synergistic interaction among various factors; and (3) the impact of R&D investment on radical innovation is not simply linear. This study effectively captures the complex nonlinear relationships between combinations of multiple influencing factors and radical innovation performance. It is of great practical significance for revealing SRDI enterprises’ radical innovation performance improvement pathways and enhancing their innovation capability.https://www.mdpi.com/2079-8954/13/3/198SRDI enterprisesradical innovation performanceTOE frameworkmachine learning |
| spellingShingle | Liping Zhang Hanhui Qiu Jinyi Chen Hailin Li Xiaoji Wan How to Enhance Enterprises’ Radical Innovation Performance Through Multiple Pathways—A Machine Learning Analysis of SRDI Enterprises in China Systems SRDI enterprises radical innovation performance TOE framework machine learning |
| title | How to Enhance Enterprises’ Radical Innovation Performance Through Multiple Pathways—A Machine Learning Analysis of SRDI Enterprises in China |
| title_full | How to Enhance Enterprises’ Radical Innovation Performance Through Multiple Pathways—A Machine Learning Analysis of SRDI Enterprises in China |
| title_fullStr | How to Enhance Enterprises’ Radical Innovation Performance Through Multiple Pathways—A Machine Learning Analysis of SRDI Enterprises in China |
| title_full_unstemmed | How to Enhance Enterprises’ Radical Innovation Performance Through Multiple Pathways—A Machine Learning Analysis of SRDI Enterprises in China |
| title_short | How to Enhance Enterprises’ Radical Innovation Performance Through Multiple Pathways—A Machine Learning Analysis of SRDI Enterprises in China |
| title_sort | how to enhance enterprises radical innovation performance through multiple pathways a machine learning analysis of srdi enterprises in china |
| topic | SRDI enterprises radical innovation performance TOE framework machine learning |
| url | https://www.mdpi.com/2079-8954/13/3/198 |
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