IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i9p5269-d803394.html
   My bibliography  Save this article

Dynamic Transition and Convergence Trend of the Innovation Efficiency among Companies Listed on the Growth Enterprise Market in the Yangtze River Economic Belt—Empirical Analysis Based on DEA—Malmquist Model

Author

Listed:
  • Yanqi Han

    (School of Business, Hubei University, Wuhan 430062, China)

  • Minghui Hua

    (School of Business, Hubei University, Wuhan 430062, China)

  • Malan Huang

    (School of Business, Hubei University, Wuhan 430062, China)

  • Jin Li

    (School of Business, Hubei University, Wuhan 430062, China)

  • Shirui Wang

    (School of Business, Hubei University, Wuhan 430062, China)

Abstract

Background: The Yangtze River Economic Belt (YREB) occupies an important economic position in China and has great research value. Methods: Based on the panel data of 142 GEM-listed companies in the YREB from 2015 to 2019, using the DEA Malmquist index, σ -convergence and β -convergence models, this study empirically analyzes the dynamic change and convergence trend of the innovation efficiency of these companies. Results: The number of these companies increased significantly but the innovation efficiency of them has not reached the optimal level. From a static point of view, companies in the middle reaches of the Yangtze River have the highest innovation efficiency, while from the dynamic point of view, the Yangtze River Delta region has the highest innovation efficiency. Moreover, most companies have an agglomeration effect, and there is a big gap in innovation efficiency. There is no σ -convergence trend in the YREB and its sub-regions, but there is an obvious β -convergence trend. Conclusions: The innovation efficiency of these companies has a lot of room for improvement. There is industry heterogeneity, and exogenous factors have different effects on the improvement of innovation efficiency in different regions owing to the differences in geographical location, economic development level, and other factors.

Suggested Citation

  • Yanqi Han & Minghui Hua & Malan Huang & Jin Li & Shirui Wang, 2022. "Dynamic Transition and Convergence Trend of the Innovation Efficiency among Companies Listed on the Growth Enterprise Market in the Yangtze River Economic Belt—Empirical Analysis Based on DEA—Malmquis," Sustainability, MDPI, vol. 14(9), pages 1-28, April.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:9:p:5269-:d:803394
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/9/5269/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/9/5269/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ying Cheng & Wei Liu & Jian Lu, 2017. "Financing Innovation in the Yangtze River Economic Belt: Rationale and Impact on Firm Growth and Foreign Trade," Canadian Public Policy, University of Toronto Press, vol. 43(s2), pages 122-135, April.
    2. Lin, Shoufu & Lin, Ruoyun & Sun, Ji & Wang, Fei & Wu, Weixiang, 2021. "Dynamically evaluating technological innovation efficiency of high-tech industry in China: Provincial, regional and industrial perspective," Socio-Economic Planning Sciences, Elsevier, vol. 74(C).
    3. Deyu Meng & Guoen Wei & Pingjun Sun, 2020. "Analyzing the Characteristics and Causes of Location Spatial Agglomeration of Listed Companies: An Empirical Study of China’s Yangtze River Economic Belt," Complexity, Hindawi, vol. 2020, pages 1-14, December.
    4. Wang, Ya & Pan, Jiao-feng & Pei, Rui-min & Yi, Bo-Wen & Yang, Guo-liang, 2020. "Assessing the technological innovation efficiency of China's high-tech industries with a two-stage network DEA approach," Socio-Economic Planning Sciences, Elsevier, vol. 71(C).
    5. Giacalone, Massimiliano & Nissi, Eugenia & Cusatelli, Carlo, 2020. "Dynamic efficiency evaluation of Italian judicial system using DEA based Malmquist productivity indexes," Socio-Economic Planning Sciences, Elsevier, vol. 72(C).
    6. Abdul Wadud & Ben White, 2000. "Farm household efficiency in Bangladesh: a comparison of stochastic frontier and DEA methods," Applied Economics, Taylor & Francis Journals, vol. 32(13), pages 1665-1673.
    7. Iglesias, Guillermo & Castellanos, Pablo & Seijas, Amparo, 2010. "Measurement of productive efficiency with frontier methods: A case study for wind farms," Energy Economics, Elsevier, vol. 32(5), pages 1199-1208, September.
    8. Fare, Rolf & Grosskopf, Shawna & Norris, Mary, 1997. "Productivity Growth, Technical Progress, and Efficiency Change in Industrialized Countries: Reply," American Economic Review, American Economic Association, vol. 87(5), pages 1040-1043, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yunyao Li & Yanji Ma, 2022. "Research on Industrial Innovation Efficiency and the Influencing Factors of the Old Industrial Base Based on the Lock-In Effect, a Case Study of Jilin Province, China," Sustainability, MDPI, vol. 14(19), pages 1-23, October.

    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. Zhong, Meirui & Huang, Gangli & He, Ruifang, 2022. "The technological innovation efficiency of China's lithium-ion battery listed enterprises: Evidence from a three-stage DEA model and micro-data," Energy, Elsevier, vol. 246(C).
    2. Chen, Xiaoqing & Liu, Xinwang & Zhu, Qingyuan, 2022. "Comparative analysis of total factor productivity in China's high-tech industries," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    3. Yaliu Yang & Yuan Wang & Cui Wang & Yingyan Zhang & Cuixia Zhang, 2022. "Temporal and Spatial Evolution of the Science and Technology Innovative Efficiency of Regional Industrial Enterprises: A Data-Driven Perspective," Sustainability, MDPI, vol. 14(17), pages 1-21, August.
    4. Na Yu & Chunfeng Zhao, 2021. "Chain Innovation Mechanism of the Manufacturing Industry in the Yangtze River Delta of China Based on Evolutionary Game," Sustainability, MDPI, vol. 13(17), pages 1-20, August.
    5. Dehua Zhang & Haiqing Wang & Sha Lou & Shen Zhong, 2021. "Research on grain production efficiency in China’s main grain producing areas from the perspective of financial support," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-16, March.
    6. Rouf, Abdur, 2015. "Conventional vs Natural Flood Control and Drainage Managements in a Tidal Coastal Zone: An Evaluation from a Productive Efficiency Perspective," 89th Annual Conference, April 13-15, 2015, Warwick University, Coventry, UK 256023, Agricultural Economics Society.
    7. Daniel Solís & Boris E. Bravo‐Ureta & Ricardo E. Quiroga, 2009. "Technical Efficiency among Peasant Farmers Participating in Natural Resource Management Programmes in Central America," Journal of Agricultural Economics, Wiley Blackwell, vol. 60(1), pages 202-219, February.
    8. Luo, Tao & Khoshnevisan, Benyamin & Huang, Ruyi & Chen, Qiu & Mei, Zili & Pan, Junting & Liu, Hongbin, 2020. "Analysis of revolution in decentralized biogas facilities caused by transition in Chinese rural areas," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    9. 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.
    10. Watkins, K. Bradley & Hristovska, Tatjana & Mazzanti, Ralph & Wilson, Charles E. Jr & Schmidt, Lance, 2014. "Measurement of Technical, Allocative, Economic, and Scale Efficiency of Rice Production in Arkansas Using Data Envelopment Analysis," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 46(1), pages 1-18, February.
    11. 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).
    12. Oguntade, Adegboyega Eyitayo & Fatunmbi, Temitope Enitan & Folayan, Joshua Adio, 2013. "Productivity of Timber Processing in Ondo State, Nigeria," Sustainable Agriculture Research, Canadian Center of Science and Education, vol. 2(1).
    13. Madau, Fabio A., 2015. "Technical and Scale Efficiency in the Italian Citrus Farming: Comparison between SFA and DEA Approaches," Agricultural Economics Review, Greek Association of Agricultural Economists, vol. 16(2), pages 1-13.
    14. Kawasaki, Kentaro, 2010. "The costs and benefits of land fragmentation of rice farms in Japan," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 54(4), pages 1-18.
    15. Ibrahim Yilmaz, 2023. "A Hybrid DEA–Fuzzy COPRAS Approach to the Evaluation of Renewable Energy: A Case of Wind Farms in Turkey," Sustainability, MDPI, vol. 15(14), pages 1-18, July.
    16. Destefanis, Sergio, 2000. "Differenziali territoriali di produttività ed efficienza e sviluppo dualistico [Territorial differences in productivity and efficiency and Italian dualism]," MPRA Paper 62065, University Library of Munich, Germany.
    17. Yang Zhong & Aiwen Lin & Zhigao Zhou & Feiyan Chen, 2018. "Spatial Pattern Evolution and Optimization of Urban System in the Yangtze River Economic Belt, China, Based on DMSP-OLS Night Light Data," Sustainability, MDPI, vol. 10(10), pages 1-14, October.
    18. ChuangLin Fang & XingLiang Guan & ShaSha Lu & Min Zhou & Yu Deng, 2013. "Input–Output Efficiency of Urban Agglomerations in China: An Application of Data Envelopment Analysis (DEA)," Urban Studies, Urban Studies Journal Limited, vol. 50(13), pages 2766-2790, October.
    19. Barnabé Walheer, 2018. "Cost Malmquist productivity index: an output-specific approach for group comparison," Journal of Productivity Analysis, Springer, vol. 49(1), pages 79-94, February.
    20. Bouali Guesmi & Teresa Serra & Amr Radwan & José María Gil, 2018. "Efficiency of Egyptian organic agriculture: A local maximum likelihood approach," Agribusiness, John Wiley & Sons, Ltd., vol. 34(2), pages 441-455, March.

    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:14:y:2022:i:9:p:5269-:d:803394. 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.