The Total Factor Productivity of China’s Software Industry and its Promotion Path

To reasonably guide and promote the high-quality development of China’s software industry through policies, and to improve the total factor productivity (TFP) of China’s software industry are the inevitable requirements of the conjunctive development. Previous research mainly u...

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Main Authors: Lixiang Zhao, Xiaoxiang Wang, Songling Wu
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/9471797/
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spelling doaj-1fabf57cb6bf4e90b51b0e504d5f9c812021-07-13T23:01:34ZengIEEEIEEE Access2169-35362021-01-019960399605510.1109/ACCESS.2021.30942679471797The Total Factor Productivity of China’s Software Industry and its Promotion PathLixiang Zhao0Xiaoxiang Wang1https://orcid.org/0000-0001-7894-9134Songling Wu2College of Economics and Management, Beijing University of Technology, Beijing, ChinaCollege of Economics and Management, Beijing University of Technology, Beijing, ChinaSchool of Economics, Henan University of Science and Technology, Henan, ChinaTo reasonably guide and promote the high-quality development of China’s software industry through policies, and to improve the total factor productivity (TFP) of China’s software industry are the inevitable requirements of the conjunctive development. Previous research mainly used econometric methods to explore the impact of specific variables or factors in different regions on the TFP of the software industry. Here we provide a solution for the path selection to improve the TFP of the software industry. The DEA-Malmquist index analysis method to measure the TFP of the software industry and the fuzzy set qualitative comparative analysis method (fsQCA) are adopted to explore the different promotion paths of provinces based on the relevant data of 29 provinces in China. The results show that 5 path configurations achieve high TFP. Specifically, regions with high TFP in the software industry tend to be those with high enterprise scale, high R&D investment, and high R&D personnel investment. When the scale of enterprises is high, the region should fully consider the degree of R&D investment and the degree of higher education. When the investment of R&D personnel is high, the high education level of human factors and the investment intensity of fixed assets of capital factors should be brought into full play. When the investment in fixed assets and R&D investment are high, the region should fully consider the investment of R&D personnel and the scale of the enterprise.https://ieeexplore.ieee.org/document/9471797/Software industryDEA-Malmquist index analysis methodtotal factor productivityfuzzy set qualitative comparative analysispromotion path
collection DOAJ
language English
format Article
sources DOAJ
author Lixiang Zhao
Xiaoxiang Wang
Songling Wu
spellingShingle Lixiang Zhao
Xiaoxiang Wang
Songling Wu
The Total Factor Productivity of China’s Software Industry and its Promotion Path
IEEE Access
Software industry
DEA-Malmquist index analysis method
total factor productivity
fuzzy set qualitative comparative analysis
promotion path
author_facet Lixiang Zhao
Xiaoxiang Wang
Songling Wu
author_sort Lixiang Zhao
title The Total Factor Productivity of China’s Software Industry and its Promotion Path
title_short The Total Factor Productivity of China’s Software Industry and its Promotion Path
title_full The Total Factor Productivity of China’s Software Industry and its Promotion Path
title_fullStr The Total Factor Productivity of China’s Software Industry and its Promotion Path
title_full_unstemmed The Total Factor Productivity of China’s Software Industry and its Promotion Path
title_sort total factor productivity of china’s software industry and its promotion path
publisher IEEE
series IEEE Access
issn 2169-3536
publishDate 2021-01-01
description To reasonably guide and promote the high-quality development of China’s software industry through policies, and to improve the total factor productivity (TFP) of China’s software industry are the inevitable requirements of the conjunctive development. Previous research mainly used econometric methods to explore the impact of specific variables or factors in different regions on the TFP of the software industry. Here we provide a solution for the path selection to improve the TFP of the software industry. The DEA-Malmquist index analysis method to measure the TFP of the software industry and the fuzzy set qualitative comparative analysis method (fsQCA) are adopted to explore the different promotion paths of provinces based on the relevant data of 29 provinces in China. The results show that 5 path configurations achieve high TFP. Specifically, regions with high TFP in the software industry tend to be those with high enterprise scale, high R&D investment, and high R&D personnel investment. When the scale of enterprises is high, the region should fully consider the degree of R&D investment and the degree of higher education. When the investment of R&D personnel is high, the high education level of human factors and the investment intensity of fixed assets of capital factors should be brought into full play. When the investment in fixed assets and R&D investment are high, the region should fully consider the investment of R&D personnel and the scale of the enterprise.
topic Software industry
DEA-Malmquist index analysis method
total factor productivity
fuzzy set qualitative comparative analysis
promotion path
url https://ieeexplore.ieee.org/document/9471797/
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