IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v12y2023i1p177-d1026012.html
   My bibliography  Save this article

Impact of Industrial Synergy on the Efficiency of Innovation Resource Allocation: Evidence from Chinese Metropolitan Areas

Author

Listed:
  • Yi Ji

    (College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China)

  • Hechang Cai

    (College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China)

  • Zilong Wang

    (College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China)

Abstract

Chinese metropolitan areas suffer from isolated industrial development, obstructed factor flows, and imperfect cooperation mechanisms. Promoting inter-city industrial complementarity and the rational allocation of regional innovation factors is necessary for sustainable regional development. First, this paper uses a network data envelopment analysis model based on resource sharing and two-stage additional input to measure the efficiency of innovation resource allocation in 31 metropolitan areas in China between 2010 and 2019. Second, the Tobit model is used to explore the impact of industrial synergy in metropolitan areas on the efficiency of innovation resource allocation at different stages and to analyze regional heterogeneity. The results indicate that the efficiency of innovation resource allocation in China’s metropolitan areas shows a slowly increasing trend. The efficiency of the innovation resource development stage is lower than that of the economic transformation stage. Disparity in the efficiency of innovation resource allocation among metropolitan areas is significant, with those on the southeast coast being the most efficient. Industrial synergy in metropolitan areas has a significantly positive impact on the efficiency of innovation resource allocation. The positive impact is greater in the economic transformation phase than in the innovation resource development phase and has significant regional heterogeneity.

Suggested Citation

  • Yi Ji & Hechang Cai & Zilong Wang, 2023. "Impact of Industrial Synergy on the Efficiency of Innovation Resource Allocation: Evidence from Chinese Metropolitan Areas," Land, MDPI, vol. 12(1), pages 1-16, January.
    • Handle: RePEc:gam:jlands:v:12:y:2023:i:1:p:177-:d:1026012
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/12/1/177/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2073-445X/12/1/177/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sergey Anokhin & Joakim Wincent & Vinit Parida & Natalya Chistyakova & Pejvak Oghazi, 2019. "Industrial clusters, flagship enterprises and regional innovation," Entrepreneurship & Regional Development, Taylor & Francis Journals, vol. 31(1-2), pages 104-118, January.
    2. Tsionas, Mike G., 2021. "Optimal combinations of stochastic frontier and data envelopment analysis models," European Journal of Operational Research, Elsevier, vol. 294(2), pages 790-800.
    3. Ning, Lutao & Wang, Fan & Li, Jian, 2016. "Urban innovation, regional externalities of foreign direct investment and industrial agglomeration: Evidence from Chinese cities," Research Policy, Elsevier, vol. 45(4), pages 830-843.
    4. Liu, Hui-hui & Yang, Guo-liang & Liu, Xiao-xiao & Song, Yao-yao, 2020. "R&D performance assessment of industrial enterprises in China: A two-stage DEA approach," Socio-Economic Planning Sciences, Elsevier, vol. 71(C).
    5. Chiang Kao, 2017. "General Two-Stage Systems," International Series in Operations Research & Management Science, in: Network Data Envelopment Analysis, chapter 0, pages 237-273, Springer.
    6. Griliches, Zvi, 1980. "R & D and the Productivity Slowdown," American Economic Review, American Economic Association, vol. 70(2), pages 343-348, May.
    7. Kao, Chiang, 2014. "Network data envelopment analysis: A review," European Journal of Operational Research, Elsevier, vol. 239(1), pages 1-16.
    8. Juying Zeng & Domingo Ribeiro-Soriano & Jun Ren, 2021. "Innovation efficiency: a bibliometric review and future research agenda," Asia Pacific Business Review, Taylor & Francis Journals, vol. 27(2), pages 209-228, March.
    9. Shao, Shuai & Yang, Lili, 2014. "Natural resource dependence, human capital accumulation, and economic growth: A combined explanation for the resource curse and the resource blessing," Energy Policy, Elsevier, vol. 74(C), pages 632-642.
    10. Chen, Xiafei & Liu, Zhiying & Zhu, Qingyuan, 2018. "Performance evaluation of China's high-tech innovation process: Analysis based on the innovation value chain," Technovation, Elsevier, vol. 74, pages 42-53.
    11. Jiao, Hao & Zhou, Jianghua & Gao, Taishan & Liu, Xielin, 2016. "The more interactions the better? The moderating effect of the interaction between local producers and users of knowledge on the relationship between R&D investment and regional innovation systems," Technological Forecasting and Social Change, Elsevier, vol. 110(C), pages 13-20.
    12. Cook, Wade D. & Liang, Liang & Zhu, Joe, 2010. "Measuring performance of two-stage network structures by DEA: A review and future perspective," Omega, Elsevier, vol. 38(6), pages 423-430, December.
    13. Kao, Chiang, 2017. "Efficiency measurement and frontier projection identification for general two-stage systems in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 261(2), pages 679-689.
    14. Moaniba, Igam M. & Su, Hsin-Ning & Lee, Pei-Chun, 2019. "On the drivers of innovation: Does the co-evolution of technological diversification and international collaboration matter?," Technological Forecasting and Social Change, Elsevier, vol. 148(C).
    15. Zhu, Lin & Luo, Jian & Dong, Qingli & Zhao, Yang & Wang, Yunyue & Wang, Yong, 2021. "Green technology innovation efficiency of energy-intensive industries in China from the perspective of shared resources: Dynamic change and improvement path," Technological Forecasting and Social Change, Elsevier, vol. 170(C).
    16. Huangxin Chen & Hang Lin & Wenjie Zou, 2020. "Research on the Regional Differences and Influencing Factors of the Innovation Efficiency of China’s High-Tech Industries: Based on a Shared Inputs Two-Stage Network DEA," Sustainability, MDPI, vol. 12(8), pages 1-15, April.
    17. Jing Li & Jialong Xing, 2020. "Why Is Collaborative Agglomeration of Innovation so Important for Improving Regional Innovation Capabilities? A Perspective Based on Collaborative Agglomeration of Industry-University-Research Institu," Complexity, Hindawi, vol. 2020, pages 1-21, September.
    18. Yongqi Feng & Haolin Zhang & Yung-ho Chiu & Tzu-Han Chang, 2021. "Innovation efficiency and the impact of the institutional quality: a cross-country analysis using the two-stage meta-frontier dynamic network DEA model," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(4), pages 3091-3129, April.
    19. Chiang Kao, 2014. "Efficiency Decomposition in Network Data Envelopment Analysis," International Series in Operations Research & Management Science, in: Wade D. Cook & Joe Zhu (ed.), Data Envelopment Analysis, edition 127, chapter 0, pages 55-77, Springer.
    20. Anna D’Ambrosio & Roberto Gabriele & Francesco Schiavone & Manuel Villasalero, 2017. "The role of openness in explaining innovation performance in a regional context," The Journal of Technology Transfer, Springer, vol. 42(2), pages 389-408, April.
    21. Miguel Gómez-Antonio & Stuart Sweeney, 2021. "Testing the role of intra-metropolitan local factors on knowledge-intensive industries’ location choices," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 66(3), pages 699-728, June.
    22. Hong, Jin & Feng, Bing & Wu, Yanrui & Wang, Liangbing, 2016. "Do government grants promote innovation efficiency in China's high-tech industries?," Technovation, Elsevier, vol. 57, pages 4-13.
    23. Kao, Chiang, 2018. "Multiplicative aggregation of division efficiencies in network data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 270(1), pages 328-336.
    24. Yanwen Sheng & Jinli Zhao & Xuebo Zhang & Jinping Song & Yi Miao, 2019. "Innovation efficiency and spatial spillover in urban agglomerations: A case of the Beijing‐Tianjin‐Hebei, the Yangtze River Delta, and the Pearl River Delta," Growth and Change, Wiley Blackwell, vol. 50(4), pages 1280-1310, 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. Yudong Zhang & Pushpita Chatterjee & Amrit Mukherjee, 2023. "Trust, Privacy and Security for Smart Cities," Sustainability, MDPI, vol. 15(6), pages 1-3, March.

    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. Jiawei Yang & Lei Fang, 2022. "Average lexicographic efficiency decomposition in two-stage data envelopment analysis: an application to China’s regional high-tech innovation systems," Annals of Operations Research, Springer, vol. 312(2), pages 1051-1093, May.
    2. Wan, Qunchao & Chen, Jin & Yao, Zhu & Yuan, Ling, 2022. "Preferential tax policy and R&D personnel flow for technological innovation efficiency of China's high-tech industry in an emerging economy," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    3. Kao, Chiang, 2018. "Multiplicative aggregation of division efficiencies in network data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 270(1), pages 328-336.
    4. Kao, Chiang, 2019. "Inefficiency identification for closed series production systems," European Journal of Operational Research, Elsevier, vol. 275(2), pages 599-607.
    5. Lim, Dong-Joon & Kim, Moon-Su, 2022. "Measuring dynamic efficiency with variable time lag effects," Omega, Elsevier, vol. 108(C).
    6. Jiawei Yang, 2023. "Disentangling the sources of bank inefficiency: a two-stage network multi-directional efficiency analysis approach," Annals of Operations Research, Springer, vol. 326(1), pages 369-410, July.
    7. Qingyou Yan & Fei Zhao & Xu Wang & Tomas Balezentis, 2021. "The Environmental Efficiency Analysis Based on the Three-Step Method for Two-Stage Data Envelopment Analysis," Energies, MDPI, vol. 14(21), pages 1-14, October.
    8. Xiao, Huijuan & Wang, Daoping & Qi, Yu & Shao, Shuai & Zhou, Ya & Shan, Yuli, 2021. "The governance-production nexus of eco-efficiency in Chinese resource-based cities: A two-stage network DEA approach," Energy Economics, Elsevier, vol. 101(C).
    9. Meng, Fanyong & Xiong, Beibei, 2021. "Logical efficiency decomposition for general two-stage systems in view of cross efficiency," European Journal of Operational Research, Elsevier, vol. 294(2), pages 622-632.
    10. Ming-Fu Hsu & Ying-Shao Hsin & Fu-Jiing Shiue, 2022. "Business analytics for corporate risk management and performance improvement," Annals of Operations Research, Springer, vol. 315(2), pages 629-669, August.
    11. Kremantzis, Marios Dominikos & Beullens, Patrick & Kyrgiakos, Leonidas Sotirios & Klein, Jonathan, 2022. "Measurement and evaluation of multi-function parallel network hierarchical DEA systems," Socio-Economic Planning Sciences, Elsevier, vol. 84(C).
    12. Koronakos, Gregory & Sotiros, Dimitris & Despotis, Dimitris K. & Kritikos, Manolis N., 2022. "Fair efficiency decomposition in network DEA: A compromise programming approach," Socio-Economic Planning Sciences, Elsevier, vol. 79(C).
    13. Ibrahim Alnafrah, 2021. "Efficiency evaluation of BRICS’s national innovation systems based on bias-corrected network data envelopment analysis," Journal of Innovation and Entrepreneurship, Springer, vol. 10(1), pages 1-28, December.
    14. Zhang, Linyan & Chen, Kun, 2019. "Hierarchical network systems: An application to high-technology industry in China," Omega, Elsevier, vol. 82(C), pages 118-131.
    15. Xueling Guan & Lijiang Chen & Qing Xia & Zhaohui Qin, 2022. "Innovation Efficiency of Chinese Pharmaceutical Manufacturing Industry from the Perspective of Innovation Ecosystem," Sustainability, MDPI, vol. 14(20), pages 1-16, October.
    16. Jin, Baoling & Han, Ying & Kou, Po, 2023. "Dynamically evaluating the comprehensive efficiency of technological innovation and low-carbon economy in China's industrial sectors," Socio-Economic Planning Sciences, Elsevier, vol. 86(C).
    17. Junhee Bae & Yanghon Chung & Hyesoo Ko, 2021. "Analysis of efficiency in public research activities in terms of knowledge spillover: focusing on earthquake R&D accomplishments," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 108(2), pages 2249-2264, September.
    18. Chen, Ya & Li, Yongjun & Liang, Liang & Salo, Ahti & Wu, Huaqing, 2016. "Frontier projection and efficiency decomposition in two-stage processes with slacks-based measures," European Journal of Operational Research, Elsevier, vol. 250(2), pages 543-554.
    19. Joe Zhu, 2022. "DEA under big data: data enabled analytics and network data envelopment analysis," Annals of Operations Research, Springer, vol. 309(2), pages 761-783, February.
    20. Tatiana Bencova & Andrea Bohacikova, 2022. "DEA in Performance Measurement of Two-Stage Processes: Comparative Overview of the Literature," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 5, pages 111-129.

    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:jlands:v:12:y:2023:i:1:p:177-:d:1026012. 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.