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External Financing Efficiency of Rural Revitalization Listed Companies in China—Based on Two-Stage DEA and Grey Relational Analysis

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  • Xianhua Tan

    (School of Economics and Management, Shangrao Normal University, Shangrao 334001, China
    School of Business Administration, Wonkwang University, 460 Iksandae-ro, Iksan, Jeonbuk 54538, Korea)

  • Sanggyun Na

    (School of Business Administration, Wonkwang University, 460 Iksandae-ro, Iksan, Jeonbuk 54538, Korea)

  • Lei Guo

    (School of Business Administration, Wonkwang University, 460 Iksandae-ro, Iksan, Jeonbuk 54538, Korea)

  • Jing Chen

    (School of Business Administration, Wonkwang University, 460 Iksandae-ro, Iksan, Jeonbuk 54538, Korea)

  • Zhihua Ruan

    (School of Business Administration, Wonkwang University, 460 Iksandae-ro, Iksan, Jeonbuk 54538, Korea)

Abstract

Rural revitalization is an important strategy to promote sustainable development of rural areas in China. Rural revitalization listed companies play an important role in implementing the rural revitalization strategy and developing the agricultural industry. However, the financing problem has always been a bottleneck problem with Chinese listed companies. This study used a two-stage DEA (data envelopment analysis) method to evaluate the funds raising efficiency, funds using efficiency, and overall financing efficiency of 34 rural revitalization listed companies in 2018. The results show that the financing efficiencies of 34 sample companies were low, only six companies have overall efficient financing, and there was much room for improvement. Financing efficiency varied greatly depending on the nature of the company, the industry, and the listing board. The efficiency of funds using of state-owned enterprises was much lower than that of private companies. The average efficiency value of agricultural company funds raising was lower than that of manufacturing. The efficiency of small and middle-size enterprises (SMEs) was lower than that of main board companies, but the growth enterprise market (GEM) companies achieved higher efficiency in the funds using. Further, by using the grey relational analysis (GRA) method, we found that the key factors affecting the financing efficiency of sample companies included capital structure, debt-paying ability, governance structure, company age, and operating ability. To improve financing efficiency, the companies should not only optimize their capital structure and governance structure but also improve their management and innovation capabilities. At the same time, the state also needs to give different policies support according to the characteristics of the companies.

Suggested Citation

  • Xianhua Tan & Sanggyun Na & Lei Guo & Jing Chen & Zhihua Ruan, 2019. "External Financing Efficiency of Rural Revitalization Listed Companies in China—Based on Two-Stage DEA and Grey Relational Analysis," Sustainability, MDPI, vol. 11(16), pages 1-21, August.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:16:p:4413-:d:257825
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    2. Leonidas Sotirios Kyrgiakos & Georgios Kleftodimos & George Vlontzos & Panos M. Pardalos, 2023. "A systematic literature review of data envelopment analysis implementation in agriculture under the prism of sustainability," Operational Research, Springer, vol. 23(1), pages 1-38, March.
    3. Ahmad Bathaei & Dalia Štreimikienė, 2023. "A Systematic Review of Agricultural Sustainability Indicators," Agriculture, MDPI, vol. 13(2), pages 1-19, January.
    4. Xianhua Tan & Danting Zheng & Yuanyuan Zhu & Sanggyun Na, 2023. "The Financing Efficiency of China’s Industrial Listed Enterprises Based on the Dynamic–Network SBM Model," Sustainability, MDPI, vol. 15(6), pages 1-19, March.
    5. Bo Song & Jing Zhao & Panpan Zhang, 2022. "A Study on Factors Influencing the Efficiency of Rural Agriculture Financial Support in China," Sustainability, MDPI, vol. 14(22), pages 1-13, November.
    6. Ren, Tiantian & Wang, Na & Xiao, Helu & Zhou, Zhongbao, 2024. "Efficiency of funding to rural revitalization and regional heterogeneity of technologies in China: Dynamic network nonconvex metafrontiers," Socio-Economic Planning Sciences, Elsevier, vol. 92(C).
    7. Fayu Chen & Jinhao Liu & Xiaoyu Liu & Hua Zhang, 2023. "Static and Dynamic Evaluation of Financing Efficiency in Enterprises’ Low-Carbon Supply Chain: PCA–DEA–Malmquist Model Method," Sustainability, MDPI, vol. 15(3), pages 1-17, January.

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