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Cloud-native-based flexible value generation mechanism of public health platform using machine learning
Public health machinery learning platform based on cloud-native is a system platform that combines machine learning frameworks and cloud-native technology for public health services. The problem of how its flexible value is realized has been widely concerned by all public health network intelligent...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer London
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9039606/ https://www.ncbi.nlm.nih.gov/pubmed/35496654 http://dx.doi.org/10.1007/s00521-022-07221-5 |
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author | Jiang, Ming Wu, Lingzhi Lin, Liming Xu, Qiaozhi Zhang, Weiguo Wu, Zeyan |
author_facet | Jiang, Ming Wu, Lingzhi Lin, Liming Xu, Qiaozhi Zhang, Weiguo Wu, Zeyan |
author_sort | Jiang, Ming |
collection | PubMed |
description | Public health machinery learning platform based on cloud-native is a system platform that combines machine learning frameworks and cloud-native technology for public health services. The problem of how its flexible value is realized has been widely concerned by all public health network intelligent researchers. Thus, this article examines the relationship between cloud-native architecture flexibility and cloud provider value and the processes and the boundary condition by which cloud-native architecture flexibility affects cloud provider value based on innovation theory and dynamic capability theory. The results of a survey of 509 platform-related respondents in China show that cloud-native architecture flexibility is positively related to cloud provider value, and both absorptive capacity and supply chain agility mediate the above-mentioned effect. Moreover, R&D subsidies strengthen both the positive relationship between absorptive capacity and cloud provider value and the relationship between supply chain agility and cloud provider value. In this study, cloud-native architecture flexibility, unit absorptive capacity, supply chain agility and R&D subsidies are considered into a flexible value generation mechanism model that extend the relevant research on the value generation mechanism of information system under the background of network intelligence, and to provide relevant enterprises with suggestions on upgrade strategies. |
format | Online Article Text |
id | pubmed-9039606 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer London |
record_format | MEDLINE/PubMed |
spelling | pubmed-90396062022-04-26 Cloud-native-based flexible value generation mechanism of public health platform using machine learning Jiang, Ming Wu, Lingzhi Lin, Liming Xu, Qiaozhi Zhang, Weiguo Wu, Zeyan Neural Comput Appl S.I.: Machine Learning and Big Data Analytics for IoT Security and Privacy (SPIOT 2021) Public health machinery learning platform based on cloud-native is a system platform that combines machine learning frameworks and cloud-native technology for public health services. The problem of how its flexible value is realized has been widely concerned by all public health network intelligent researchers. Thus, this article examines the relationship between cloud-native architecture flexibility and cloud provider value and the processes and the boundary condition by which cloud-native architecture flexibility affects cloud provider value based on innovation theory and dynamic capability theory. The results of a survey of 509 platform-related respondents in China show that cloud-native architecture flexibility is positively related to cloud provider value, and both absorptive capacity and supply chain agility mediate the above-mentioned effect. Moreover, R&D subsidies strengthen both the positive relationship between absorptive capacity and cloud provider value and the relationship between supply chain agility and cloud provider value. In this study, cloud-native architecture flexibility, unit absorptive capacity, supply chain agility and R&D subsidies are considered into a flexible value generation mechanism model that extend the relevant research on the value generation mechanism of information system under the background of network intelligence, and to provide relevant enterprises with suggestions on upgrade strategies. Springer London 2022-04-26 2023 /pmc/articles/PMC9039606/ /pubmed/35496654 http://dx.doi.org/10.1007/s00521-022-07221-5 Text en © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | S.I.: Machine Learning and Big Data Analytics for IoT Security and Privacy (SPIOT 2021) Jiang, Ming Wu, Lingzhi Lin, Liming Xu, Qiaozhi Zhang, Weiguo Wu, Zeyan Cloud-native-based flexible value generation mechanism of public health platform using machine learning |
title | Cloud-native-based flexible value generation mechanism of public health platform using machine learning |
title_full | Cloud-native-based flexible value generation mechanism of public health platform using machine learning |
title_fullStr | Cloud-native-based flexible value generation mechanism of public health platform using machine learning |
title_full_unstemmed | Cloud-native-based flexible value generation mechanism of public health platform using machine learning |
title_short | Cloud-native-based flexible value generation mechanism of public health platform using machine learning |
title_sort | cloud-native-based flexible value generation mechanism of public health platform using machine learning |
topic | S.I.: Machine Learning and Big Data Analytics for IoT Security and Privacy (SPIOT 2021) |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9039606/ https://www.ncbi.nlm.nih.gov/pubmed/35496654 http://dx.doi.org/10.1007/s00521-022-07221-5 |
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