Evaluation of Innovation Efficiency of Listed Baijiu Enterprises Based on Three-stage DEA Model

Xiyao Li; Yijun Chen

doi:10.62051/ijgem.v3n3.11

Authors

Xiyao Li
Yijun Chen

DOI:

https://doi.org/10.62051/ijgem.v3n3.11

Keywords:

Baijiu Listed Companies, Innovation efficiency, Three-stage DEA model

Abstract

Baijiu industry, as a pillar industry of hundreds of billions of dollars, is an important part of the national economy, and has been strongly supported by the national and local governments at all levels. However, as China's consumption upgrading speeds up, the process of the liquor industry is accelerating, and the market competition led by homogenised products is intensifying, liquor enterprises need to seek an innovation path suitable for their own enterprises in order to form a sustainable competitive advantage. Using a three-stage DEA model, this paper measures the innovation efficiency of 18 listed liquor enterprises in 2022, and analyses the relationship between environmental variables and the innovation efficiency of listed liquor enterprises. The results show that environmental factors such as government subsidies, GDP per capita, and equity concentration all have a significant impact on the slack variables of the firms' R&D expenditure investment and the slack variables of R&D personnel investment. From a general point of view, the innovation efficiency of China's listed liquor enterprises still needs to be improved, and the gap between enterprises is large.

Downloads

Download data is not yet available.

References

Charnes A, Cooper W W, Rhodes E. Measuring the efficiency of decision making units [J]. European Journal of Operational Research, 1978, 2(6): 429-444.

Zehua Ling. Production efficiency analysis of listed liquor enterprises in China based on DEA [J]. Brewing Science and Technology, 2013(7): 1-4.

Liping Chu, Fang Shu. Comparative DEA analysis of financing efficiency of property insurance companies in China [J]. Insurance Research, 2013, 2013(4).

Yangkun Wng. Equity structure and technological innovation efficiency of Chinese new energy automobile companies - A DEA measure based on 24 listed companies [J]. Science, Technology and Industry, 2022, 22(8): 68-78.

Jindan Chen. Research on the effectiveness of R&D in China's provinces and cities based on the SFA method [J]. Business Economy, 2008, 2008(10): 26-27,65.

Junlin Li, Zhiqiang Zhou. A study on the investment efficiency of China's textile industry based on the SFA method [J]. Journal of Wuhan Textile University, 2023, 36(6): 79-86.

Zhimin Ni, Hai Lin. Evaluation of Science and Technology Innovation Efficiency in Guangdong Province Based on SE-DEA Model [J]. Research on Science and Technology Management, 2021, 41(5): 15-20.

Haihua Wang, Ying Wang, Yajie Li, et al. Evaluation of the efficiency of science and technology innovation in high-tech industries in the Yangtze River Delta region--A three-stage network DEA model based on shared inputs [J]. East China Economic Management, 2022, 36(8): 26-33.

Yupei Xu, Jiaxin Li, Jian Yu. Innovation Efficiency and Influencing Factors in China's Animation Industry - An Analysis Based on the Super-Efficiency DEA Model [J]. Research on Science and Technology Management, 2020, 40(8): 83-90.

Zhiguo Lu, Fei Meng. Measurement of technological innovation efficiency of strategic emerging industries based on three-stage DEA model [J]. Statistics and Decision Making, 2022, 38(8): 158-162.

Yuegang Song, Xin Zhang. Measuring the innovation efficiency of China's high-tech industries [J]. Statistics and Decision Making, 2022, 38(10): 86-91.

Fangyu Ye, Yi Li, Ziran Tang, et al. Measurement and difference analysis of research and innovation efficiency of Chinese universities based on DEA model [J]. Contemporary Education Forum, 2024(1): 52-60.

Lijuan Zhou, Min Xu, Hedong Liu. Measurement of regional innovation efficiency - an empirical analysis based on a three-stage DEA model [J]. Enterprise Economy, 2013(12): 160-164.

Wencheng Wang, Yuan Sui. Spatial effects of synergistic agglomeration of productive service industry and high-tech industry on regional innovation efficiency [J]. Journal of Management, 2022, 19(5): 696-704.

Zhengchu He, Weihua Pan, Hongyu Pan. Research on innovation efficiency measurement and influencing factors of manufacturing enterprises - based on the perspective of digital transformation [J]. Science Decision Making, 2023, 2023(2).

Yangkun Wang. Equity structure and technological innovation efficiency of Chinese new energy automobile enterprises - A DEA measure based on 24 listed companies [J]. Science and Industry, 2022, 22(8): 68-78.

Fried H O, Lovell C A K, Schmidt S S, et al. Accounting for Environmental Effects and Statistical Noise in Data Envelopment Analysis [J]. Journal of Productivity Analysis, 2002, 17(1/2): 157-174.

Xinhong Wang, Zerong Xue, Xing Zhang. A study on innovation efficiency measurement of mixed-ownership enterprises based on two-stage DEA model - based on empirical data of listed manufacturing enterprises [J]. Research on Science and Technology Management, 2018, 38(14): 61-67.

Chenyi Deng. Evaluation of Innovation Efficiency and Influencing Factors of Biomedical Listed Enterprises--An Empirical Analysis Based on Three-Stage DEA [J]. China Price, 2023, 2023(10): 116-120.

Jianqing Zhang, Mengxuan Sun, Fei Fan. Evaluation of operational efficiency of Hubei science and technology business incubators based on DEA method [J]. Research on Science and Technology Management, 2017, 37(4): 82-88.

Xiaomeng Jie, Yongsheng Wang, Wentao Wang. Evaluation of scientific and technological innovation efficiency of Chinese sea-related enterprises based on three-stage DEA model [J]. Journal of Ocean University of China (Social Science Edition), 2020(2): 80-90.