Study on the Regional Differences and Promotion Models of Green Technology Innovation Performance in China: Based on Entropy Weight Method and Fuzzy Set-Qualitative Comparative Analysis

Li, Panpan; Bi, Peng

doi:10.1109/access.2020.3043364

Cited by 9 publications

(2 citation statements)

References 64 publications

(76 reference statements)

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“…According to the research of Zhang [6], scholars discussed capital factors [18]- [20], technical factors [3], [21], environmental factors [18], [23] and human factors [20], [25] on the software industry TFP and used the interpretive structure model method, the quantile regression method and the Tobit model for testing [15], [19], [23]. However, previous studies mainly focused on the "net effect" of a single factor on TFP, while ignoring the matching effect between multiple factors [10].…”

Section: B Influencing Factors Of Tfpmentioning

confidence: 99%

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

Zhao

Wang

2021

IEEE Access

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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.

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Section: B Influencing Factors Of Tfpmentioning

confidence: 99%

“…The purpose of this paper is to determine whether there are regional differences in TFP of China's software industry and the configuration of variable factors that lead to regional differences [10]. We discusses and solves the following questions: What is the development status of TFP in China's software industry?…”

Section: Introductionmentioning

confidence: 99%