Impact of Financial R&D Resource Allocation Efficiency Based on VR Technology and Machine Learning in Complex Systems on Total Factor Productivity
With the development of the globalization of science and technology, innovation has become an important driving force for regional economic development. As a core element of regional innovation, financial R&D resources have also become a key element to enhance national innovation capabilities an...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/6679846 |
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| author | Hui Sun Xiong Zhong |
| author_facet | Hui Sun Xiong Zhong |
| author_sort | Hui Sun |
| collection | DOAJ |
| description | With the development of the globalization of science and technology, innovation has become an important driving force for regional economic development. As a core element of regional innovation, financial R&D resources have also become a key element to enhance national innovation capabilities and national economic competitiveness. National and regional innovation capabilities have a direct impact. There are also many deep-seated problems behind the world-renowned achievements, such as irrational industrial structure, insufficient independent innovation capabilities, low resource utilization efficiency, and the service quality and efficiency of financial institutions for the transformation of total factor productivity. These problems extremely restrict the efficiency upgrade and further development of our country’s total factor productivity. This study uses the DEA-Malmquist index model to measure the efficiency of fiscal R&D resource allocation in 28 provinces and regions in China in the past 10 years and uses Mapinfo12.0 software to analyze regional differences in the efficiency of fiscal R&D resource allocation in China from a spatial perspective. During the year, the overall R&D resource allocation efficiency of 28 provinces and autonomous regions in China has shown an upward trend. The efficiency of fiscal R&D resource allocation and the concentration of financial factors have had a positive impact on total factor productivity, transform and upgrade factors, increase total factor productivity, and provide empirical evidence for building a strong country. |
| format | Article |
| id | doaj-art-0aa80d3a77dc4d35ba35d7d68badbaf6 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-0aa80d3a77dc4d35ba35d7d68badbaf62025-02-03T01:28:18ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/66798466679846Impact of Financial R&D Resource Allocation Efficiency Based on VR Technology and Machine Learning in Complex Systems on Total Factor ProductivityHui Sun0Xiong Zhong1School of Economics and Statistics, Guangzhou University, Guangzhou 510006, Guangdong, ChinaInstitute of Finance, Guangzhou University (Guangzhou Institute of International Finance), Guangzhou 510006, Guangdong, ChinaWith the development of the globalization of science and technology, innovation has become an important driving force for regional economic development. As a core element of regional innovation, financial R&D resources have also become a key element to enhance national innovation capabilities and national economic competitiveness. National and regional innovation capabilities have a direct impact. There are also many deep-seated problems behind the world-renowned achievements, such as irrational industrial structure, insufficient independent innovation capabilities, low resource utilization efficiency, and the service quality and efficiency of financial institutions for the transformation of total factor productivity. These problems extremely restrict the efficiency upgrade and further development of our country’s total factor productivity. This study uses the DEA-Malmquist index model to measure the efficiency of fiscal R&D resource allocation in 28 provinces and regions in China in the past 10 years and uses Mapinfo12.0 software to analyze regional differences in the efficiency of fiscal R&D resource allocation in China from a spatial perspective. During the year, the overall R&D resource allocation efficiency of 28 provinces and autonomous regions in China has shown an upward trend. The efficiency of fiscal R&D resource allocation and the concentration of financial factors have had a positive impact on total factor productivity, transform and upgrade factors, increase total factor productivity, and provide empirical evidence for building a strong country.http://dx.doi.org/10.1155/2020/6679846 |
| spellingShingle | Hui Sun Xiong Zhong Impact of Financial R&D Resource Allocation Efficiency Based on VR Technology and Machine Learning in Complex Systems on Total Factor Productivity Complexity |
| title | Impact of Financial R&D Resource Allocation Efficiency Based on VR Technology and Machine Learning in Complex Systems on Total Factor Productivity |
| title_full | Impact of Financial R&D Resource Allocation Efficiency Based on VR Technology and Machine Learning in Complex Systems on Total Factor Productivity |
| title_fullStr | Impact of Financial R&D Resource Allocation Efficiency Based on VR Technology and Machine Learning in Complex Systems on Total Factor Productivity |
| title_full_unstemmed | Impact of Financial R&D Resource Allocation Efficiency Based on VR Technology and Machine Learning in Complex Systems on Total Factor Productivity |
| title_short | Impact of Financial R&D Resource Allocation Efficiency Based on VR Technology and Machine Learning in Complex Systems on Total Factor Productivity |
| title_sort | impact of financial r d resource allocation efficiency based on vr technology and machine learning in complex systems on total factor productivity |
| url | http://dx.doi.org/10.1155/2020/6679846 |
| work_keys_str_mv | AT huisun impactoffinancialrdresourceallocationefficiencybasedonvrtechnologyandmachinelearningincomplexsystemsontotalfactorproductivity AT xiongzhong impactoffinancialrdresourceallocationefficiencybasedonvrtechnologyandmachinelearningincomplexsystemsontotalfactorproductivity |