IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i11p8694-d1157499.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/11/8694/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/11/8694/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Büschken, Joachim, 2009. "When does data envelopment analysis outperform a naïve efficiency measurement model?," European Journal of Operational Research, Elsevier, vol. 192(2), pages 647-657, January.
    2. Chen, Yufeng & Ni, Liangfu & Liu, Kelong, 2021. "Does China's new energy vehicle industry innovate efficiently? A three-stage dynamic network slacks-based measure approach," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    3. Ruijing Zheng & Yu Cheng & Haimeng Liu & Wei Chen & Xiaodong Chen & Yaping Wang, 2022. "The Spatiotemporal Distribution and Drivers of Urban Carbon Emission Efficiency: The Role of Technological Innovation," IJERPH, MDPI, vol. 19(15), pages 1-22, July.
    4. Artur Wyszyński, 2017. "Sytuacja finansowa klubów Ekstraklasy w ujęciu metody DEA," Gospodarka Narodowa. The Polish Journal of Economics, Warsaw School of Economics, issue 2, pages 69-99.
    5. Mario Fortin & André Leclerc, 2011. "L’Efficience Des Cooperatives De Services Financiers: Une Analyse De La Contribution Du Milieu," Annals of Public and Cooperative Economics, Wiley Blackwell, vol. 82(1), pages 45-62, March.
    6. Shih-Heng Yu & Ying-Sin Lin & Jia-Li Zhang & Chia-Shan Hsu & Shu-Min Cheng, 2025. "Incorporating Carbon Fees into the Efficiency Evaluation of Taiwan’s Steel Industry Using Data Envelopment Analysis with Negative Data," Sustainability, MDPI, vol. 17(18), pages 1-18, September.
    7. Ningyi Liu & Yongyu Wang, 2022. "Urban Agglomeration Ecological Welfare Performance and Spatial Convergence Research in the Yellow River Basin," Land, MDPI, vol. 11(11), pages 1-18, November.
    8. Vicente J. Bolós & Rafael Benítez & Vicente Coll-Serrano, 2023. "Continuous models combining slacks-based measures of efficiency and super-efficiency," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 31(2), pages 363-391, June.
    9. Liu, Fuh-Hwa Franklin & Wang, Peng-hsiang, 2008. "DEA Malmquist productivity measure: Taiwanese semiconductor companies," International Journal of Production Economics, Elsevier, vol. 112(1), pages 367-379, March.
    10. Xiaoxue Wei & Rui Zhao & Ranran Li & Ke Liu, 2025. "High-quality development efficiency in Yangtze River Delta urban agglomeration: analysis of spatiotemporal evaluation and influencing factors," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 27(3), pages 7297-7323, March.
    11. Yung-ho Chiu & Chin-wei Huang & Chung-te Ting, 2012. "A non-radial measure of different systems for Taiwanese tourist hotels’ efficiency assessment," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 20(1), pages 45-63, March.
    12. Chuanqing Wu & Heshun Deng & Hao Zhao & Qiwei Xia, 2025. "Spatiotemporal evolution and convergence patterns of urban carbon emission efficiency in China," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 12(1), pages 1-13, December.
    13. Yan Zhang & Zihan Xin & Guoya Gan, 2024. "Evaluating the Sustainable Development Performance of China’s International Commercial Ports Based on Environmental, Social and Governance Elements," Sustainability, MDPI, vol. 16(10), pages 1-16, May.
    14. Zhen Shi & Huinan Huang & Yingju Wu & Yung-Ho Chiu & Shijiong Qin, 2020. "Climate Change Impacts on Agricultural Production and Crop Disaster Area in China," IJERPH, MDPI, vol. 17(13), pages 1-23, July.
    15. Tran, Trung Hieu & Mao, Yong & Nathanail, Paul & Siebers, Peer-Olaf & Robinson, Darren, 2019. "Integrating slacks-based measure of efficiency and super-efficiency in data envelopment analysis," Omega, Elsevier, vol. 85(C), pages 156-165.
    16. Wang, Chia-Nan & Nguyen, Xuan-Tho & Le, Thi-Dao & Hsueh, Ming-Hsien, 2018. "A partner selection approach for strategic alliance in the global aerospace and defense industry," Journal of Air Transport Management, Elsevier, vol. 69(C), pages 190-204.
    17. Hao Su & Shuo Yang, 2022. "Spatio-Temporal Urban Land Green Use Efficiency under Carbon Emission Constraints in the Yellow River Basin, China," IJERPH, MDPI, vol. 19(19), pages 1-28, October.
    18. Soushi Suzuki & Karima Kourtit & Peter Nijkamp, 2017. "The robustness of performance rankings of Asia-Pacific super cities," Asia-Pacific Journal of Regional Science, Springer, vol. 1(1), pages 219-242, April.
    19. Avkiran, Necmi K., 2007. "Stability and integrity tests in data envelopment analysis," Socio-Economic Planning Sciences, Elsevier, vol. 41(3), pages 224-234, September.
    20. Fang Wu & Mingyao Gu & Chenming Zhu & Yingna Qu, 2023. "Temporal-Spatial Evolution and Trend Prediction of the Supply Efficiency of Primary Medical Health Service—An Empirical Study Based on Central and Western Regions of China," IJERPH, MDPI, vol. 20(3), pages 1-23, January.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:11:p:8694-:d:1157499. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.