Study on Information Security Industry Efficiency Measurement Based on DEA-GRA

Songye Wu; Huixin Zheng; Caihong Lv; Jialing Jiang; Shenghong Li

doi:10.62051/cnbqs759

Authors

Songye Wu
Huixin Zheng
Caihong Lv
Jialing Jiang
Shenghong Li

DOI:

https://doi.org/10.62051/cnbqs759

Keywords:

Information Security Industry; Data Envelopment Analysis; Grey Correlation Analysis; Operational Efficiency.

Abstract

The operational efficiency of information security enterprises is of great significance to China's information security and even economic security. This paper constructs a DEA-GRA model to calculate the BCC efficiency decomposition of 30 listed companies and gives the amount of input redundancy and output insufficiency of non-DEA effective enterprises. This study also explores the grey correlation between the efficiency of super-efficient technologies and the environmental factors of where firms produce and sell. The results of the study show that the vast majority of non-DEA effective decision-making units in 2022 are in a state of decreasing size. The grey correlation analysis shows that the industrial cluster effect and scientific and technological investment in the production place, and the degree of development of the information security industry in the operation place have an important impact on the technical efficiency of information security enterprises.

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References

[1] Zhang, Z. (2022). Research on the development trends and countermeasures of China's cybersecurity industry. Journal of Beijing University of Technology (Social Science Edition), 22(3), 75-85.

[2] Yin, T., & Cao, Z. (2022). The clustering of information technology industry, upgrading of industrial structure, and high-quality economic development. Statistics & Decision, 38(4), 129-134.

[3] Li, X., Zhao, H., & Cheng, G. (2021). Competitiveness evaluation and regional differences in the next-generation information technology industry in the three major economic circles. Soft Science, 35(8), 106-112.

[4] Yu, C., Wang, C., Zhuang, W., et al. (2018). Evaluation of the next-generation information technology industry from the perspective of dynamic evolution. Information Science, 36(5), 110-113.

[5] Qu, G., Song, L., & Guo, Y. (2018). Research on the technological innovation efficiency of listed companies in China: Based on the three-stage DEA method. Macroeconomic Research, 2018(6), 97-106.

[6] Fu, N., Su, Y., & Guo, X. (2023). Evaluation of innovation efficiency of smart manufacturing enterprises based on two-stage super-efficiency DEA. Science & Technology Progress and Policy, 2023, 1-11.

[7] Chen, J., & Duan, J. (2020). Comparative study of the operational efficiency of China's fintech companies based on the three-stage DEA. Finance Theory & Practice, 2020(6), 20-27.

[8] Du, B., & Chen, L. (2022). Research on the factors influencing and optimizing paths for the high-quality development of China's technology service industry: A comprehensive analysis based on the GRA model and CRITIC weighting method. Science & Technology Management Research, 42(9), 91-98.

[9] Ma, Q., Liu, Y., & Zhou, Y. (2023). Analysis of factors influencing urban unemployment scale in China based on the GRA model. China Business Review, 2023(3), 80-82.

[10] Mao, Y., & Li, Y. (2017). Comparative study on the merger performance of high-tech enterprises and traditional enterprises based on DEA after the financial crisis: Taking listed manufacturing companies on China's A-share market as an example. Journal of Southeast University (Philosophy and Social Science Edition), 19(4), 38-51+146.

[11] Wu, R. (2018). Efficiency evaluation of listed publishing companies based on the Malmquist index. Science & Technology and Publishing, 2018(12), 114-118.