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Enhancing Operating Efficiency in China’s High-End Equipment Manufacturing Industry: Insights from Listed Enterprises

Author

Listed:
  • Yi Zheng

    (School of Economics and Management, Shanghai Ocean University, Shanghai 201306, China)

  • Min Luo

    (School of Economics and Management, Shanghai Ocean University, Shanghai 201306, China)

Abstract

The high-end equipment manufacturing industry is a strategic sector for China’s manufacturing transformation and upgrading. However, this industry is facing a series of challenges, such as insufficient innovation capabilities and poor business operations. This paper uses the super-efficiency SBM model to calculate the operating efficiency of listed companies in this industry from a micro perspective and conducts in-depth multi-angle analysis of their operating efficiency. Furthermore, Tobit regression is utilized to identify the factors that affect operating efficiency. The aim is to provide a pathway for companies in this industry to achieve efficiency maximization and sustainable development. The research shows that the average operating efficiency of high-end equipment manufacturing companies was around 0.7 from 2016 to 2021, and nearly 70% of companies were in a non-DEA efficient state. The operating efficiency of the intelligent manufacturing equipment industry is far higher than other industries, and the western region has great development potential. In addition to government subsidies, factors such as company age, equity concentration, regional GDP, and regional openness all have a positive impact on the operational efficiency of high-end equipment manufacturing companies. This paper combines the characteristics of the equipment manufacturing industry and analyzes their operating efficiency from multiple dimensions, providing decision support and pathways for the high-quality and efficient development of this industry.

Suggested Citation

  • Yi Zheng & Min Luo, 2023. "Enhancing Operating Efficiency in China’s High-End Equipment Manufacturing Industry: Insights from Listed Enterprises," Sustainability, MDPI, vol. 15(11), pages 1-18, May.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:11:p:8694-:d:1157499
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    References listed on IDEAS

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    1. Kong, Qunxi & Peng, Dan & Ni, Yehui & Jiang, Xinyue & Wang, Ziqi, 2021. "Trade openness and economic growth quality of China: Empirical analysis using ARDL model," Finance Research Letters, Elsevier, vol. 38(C).
    2. Qingfeng Tian & Shuo Zhang & Huimin Yu & Guangming Cao, 2019. "Exploring the Factors Influencing Business Model Innovation Using Grounded Theory: The Case of a Chinese High-End Equipment Manufacturer," Sustainability, MDPI, vol. 11(5), pages 1-16, March.
    3. Shuimu Ju & Honglei Tang, 2023. "Competition and operating efficiency of manufacturing companies in E-commerce environment: empirical evidence from Chinese garment companies," Applied Economics, Taylor & Francis Journals, vol. 55(19), pages 2113-2128, April.
    4. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    5. Qiao, Lu & Fei, Junjun, 2022. "Government subsidies, enterprise operating efficiency, and “stiff but deathless” zombie firms," Economic Modelling, Elsevier, vol. 107(C).
    6. Huang, Youxing & Zhang, Yan, 2017. "How does outward foreign direct investment enhance firm productivity? A heterogeneous empirical analysis from Chinese manufacturing," China Economic Review, Elsevier, vol. 44(C), pages 1-15.
    7. Ebenezer Fiifi Emire Atta Mills & Mavis Agyapomah Baafi & Fangbiao Liu & Kailin Zeng, 2021. "Dynamic operating efficiency and its determining factors of listed real‐estate companies in China: A hierarchical slack‐based DEA‐OLS approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(3), pages 3352-3376, July.
    8. Chenghua Guan & Ye Fan, 2020. "The Impact of Social Networks on the Operating Efficiency of Chinese Technology Business Incubators," Sustainability, MDPI, vol. 12(7), pages 1-23, March.
    9. Yan Li & Haiyan Zhang & Yihui Liu & Qingbo Huang, 2020. "Impact of Embedded Global Value Chain on Technical Complexity of Industry Export—An Empirical Study Based on China’s Equipment Manufacturing Industry Panel," Sustainability, MDPI, vol. 12(7), pages 1-14, March.
    10. Tone, Kaoru, 2002. "A slacks-based measure of super-efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 143(1), pages 32-41, November.
    11. Sungmin Park & Pansoo Kim, 2021. "Operational Performance Evaluation of Korean Ship Parts Manufacturing Industry Using Dynamic Network SBM Model," Sustainability, MDPI, vol. 13(23), pages 1-20, November.
    12. Tripti Nashier & Amitabh Gupta, 2023. "Ownership Concentration and Firm Performance in India," Global Business Review, International Management Institute, vol. 24(2), pages 353-370, April.
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