Optimisation analysis of resource allocation for China’s high-tech industry based on an extended inverse DEA with frontier changes

ABSTRACT

Developing China’s high-tech industry is important to make the country innovation-oriented. This requires optimising the allocation of innovation resources and improving innovation efficiency. However, few studies have investigated this topic and the realisation path for the high-tech industry. This study develops an input-oriented inverse data envelopment analysis (DEA) model with frontier changes to analyse the optimisation of resource allocation in China’s high-tech industry during 2019–2025. With this method, decision makers can scientifically analyse the specific amount of resource investment. We also construct an analysis framework from short- and long-term perspectives. The results show that the excessive input of research and development (R&D) personnel and unbalanced allocation of capital resources are the main barriers to the development of high-tech industries in the short term, and in the mid- and long terms, the demands for investment in talent and capital will continue to increase. Improvement directions for promoting the development of China’s high-tech industry are discussed. Finally, we present valuable information for policymaking to promote progress in high-tech industries in different regions.

KEYWORDS:

• Innovation resource allocation
• high-tech industries
• inverse data envelopment analysis
• frontier changes

Acknowledgements

The authors thank the editors (Professor James Fleck and Ms. Priya Rajan) and two anonymous reviewers of this paper. Their constructive and insightful comments have improved the quality of this paper significantly.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 Eastern: Fujian, Guangdong, Hainan, Hebei, Jiangsu, Liaoning, Shandong, Shanghai, Beijing, Tianjin, and Zhejiang; Central: Anhui, Henan, Heilongjiang, Hubei, Hunan, Jilin, Jiangxi, and Shanxi; Western: Gansu, Guangxi, Guizhou, Inner Mongolia, Ningxia, Qinghai, Shaanxi, Sichuan, Xinjiang, Yunnan, and Chongqing

Additional information

Funding

This work was supported by National Natural Science Foundation of China [grant number 71872047].

Notes on contributors

Jin-cheng Lu

Jin-cheng Lu is currently a PhD candidate at the School of Economics and Management, Fuzhou University. His research interests are science and technology management, decision theory and methods.

Mei-juan Li

Mei-juan Li is a professor at the School of Economics and Management, Fuzhou University. Her research interests are evaluation theory and methods, science and technology management, decision theory and methods.