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Cloud-native-based flexible value generation mechanism of public health platform using machine learning
Ming Jiang1,2,3, Lingzhi Wu1, Liming Lin1
1School of Internet Economics and Business, Fujian University of Technology, Fuzhou, China.
Cloud-native architecture flexibility enhances public health platform value. Absorptive capacity and supply chain agility mediate this, with R&D subsidies further boosting these effects for greater cloud provider value.
Area of Science:
- Information Systems
- Cloud Computing
- Public Health
Background:
- Public health platforms increasingly leverage machine learning and cloud-native technologies.
- Understanding the value generation of these flexible systems is crucial for intelligent public health services.
Purpose of the Study:
- To investigate the relationship between cloud-native architecture flexibility and cloud provider value.
- To explore the mediating roles of absorptive capacity and supply chain agility.
- To examine the moderating effect of R&D subsidies.
Main Methods:
- Survey of 509 respondents in China related to public health machine learning platforms.
- Analysis based on innovation theory and dynamic capability theory.
- Development of a flexible value generation mechanism model.
Main Results:
- Cloud-native architecture flexibility positively impacts cloud provider value.
- Absorptive capacity and supply chain agility significantly mediate this relationship.
- R&D subsidies strengthen the positive effects of absorptive capacity and supply chain agility on cloud provider value.
Conclusions:
- Cloud-native architecture flexibility is a key driver of value in public health platforms.
- Developing absorptive capacity and supply chain agility are essential for maximizing this value.
- R&D subsidies can enhance the effectiveness of these mediating factors, offering strategic insights for enterprises.
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