Exploratory and Exploitative Innovation Performance in the Artificial Intelligence Industry in China from the Perspective of a Collaboration Network: A Data-Driven Analysis
- Liping Zhang 1, Hailin Li 1, Wenhao Zhou 1, Hanhui Qiu 1, Yenchun Jim Wu 2,3
- Liping Zhang 1, Hailin Li 1, Wenhao Zhou 1
- 1College of Business Administration, Huaqiao University, Quanzhou 362021, Fujian, China.
- 2Graduate Institute of Global Business and Strategy, National Taiwan Normal University, Taipei 106, Taiwan.
- 3Hospitality Management, Ming Chuan University, Taipei 111, Taiwan.
- 0College of Business Administration, Huaqiao University, Quanzhou 362021, Fujian, China.
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View abstract on PubMed
Summary
This summary is machine-generated.Collaboration network structures significantly impact firms' innovation performance. Specific network characteristics and firm clusters offer tailored strategies for enhancing both exploratory and exploitative innovation in the artificial intelligence sector.
Area Of Science
- Innovation Management
- Network Science
- Artificial Intelligence Industry
Background
- Firms' competitiveness relies on innovation performance.
- Understanding collaboration network structures is key to enhancing innovation output.
- The artificial intelligence (AI) industry in China presents a dynamic landscape for studying inter-organizational collaboration.
Purpose Of The Study
- To investigate the complex relationship between inter-organizational collaboration network structures and firms' exploratory and exploitative innovation performance.
- To identify key structural characteristics influencing innovation performance.
- To provide strategic insights for firms within the AI industry.
Main Methods
- Analysis of 14,790 patents from 281 AI firms in China.
- Application of clustering algorithms.
- Utilizing information entropy and Gini index for classification.
- Examining network characteristics: degree centrality, closeness centrality, local clustering coefficient, and structural holes.
Main Results
- Four structural characteristics (degree centrality, closeness centrality, local clustering coefficient, structural holes) significantly affect innovation performance.
- Distinct firm clusters exhibit unique characteristic combinations that inform development strategies.
- Firms can adopt varied strategic paths to improve innovation performance based on their goals.
Conclusions
- Network structure is a critical determinant of innovation performance.
- Tailored strategies based on firm clusters and network characteristics are essential for AI firms.
- Firms should align innovation strategies with their specific development objectives for optimal outcomes.
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