IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i8p3284-d347069.html
   My bibliography  Save this article

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

Author

Listed:
  • Huangxin Chen

    (School of Economy, Fujian Normal University, Fuzhou 350108, China)

  • Hang Lin

    (School of Economy, Fujian Normal University, Fuzhou 350108, China)

  • Wenjie Zou

    (School of Economy, Fujian Normal University, Fuzhou 350108, China)

Abstract

Innovation ability has become one of the core elements in the pursuit of China’s green growth, and high-tech industries are playing a leading role in technological innovation in China. With the rapid development of China’s high-tech industries, their innovation efficiency has attracted widespread attention. This article aims to illustrate a shared inputs two-stage network Data Envelopment Analysis (DEA), to measure the innovation efficiency of high-tech industries in China’s 29 provinces from 1999 to 2018. The results indicate that there are obvious differences in the innovation efficiency of the provinces. The technology development efficiency, the technical transformation efficiency, and the overall innovation efficiency of the developed east coast provinces are generally higher than those of the backward central and western provinces. This article further applies the spatial econometrics model to analyze the factors influencing the innovation efficiency of high-tech industries. We have found that government support, R&D input intensity, industries aggregation, economic extroversion, and the level of development of the modern service industries cause varying degrees of impact on innovation efficiency.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:8:p:3284-:d:347069
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/8/3284/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/12/8/3284/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fare, Rolf & Grosskopf, Shawna, 1996. "Productivity and intermediate products: A frontier approach," Economics Letters, Elsevier, vol. 50(1), pages 65-70, January.
    2. Wen-Min Lu & Shiu-Wan Hung & Qian Long Kweh & Wei-Kang Wang & En-Tzu Lu, 2014. "Production and Marketing Efficiencies of the U.S. Airline Industry: A Two-Stage Network DEA Approach," International Series in Operations Research & Management Science, in: Wade D. Cook & Joe Zhu (ed.), Data Envelopment Analysis, edition 127, chapter 0, pages 537-568, Springer.
    3. Jian-li Chen & Ling-jie Meng, 2014. "Research on Technological Innovation Efficiency of China’s High-Tech Industry Based on Network SBM Model and DEA Window Analysis," Springer Books, in: Ershi Qi & Jiang Shen & Runliang Dou (ed.), Proceedings of 2013 4th International Asia Conference on Industrial Engineering and Management Innovation (IEMI2013), edition 127, pages 897-905, Springer.
    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. Kao, Chiang & Hwang, Shiuh-Nan, 2008. "Efficiency decomposition in two-stage data envelopment analysis: An application to non-life insurance companies in Taiwan," European Journal of Operational Research, Elsevier, vol. 185(1), pages 418-429, February.
    6. Lorenzo Castelli & Raffaele Pesenti & Walter Ukovich, 2010. "A classification of DEA models when the internal structure of the Decision Making Units is considered," Annals of Operations Research, Springer, vol. 173(1), pages 207-235, January.
    7. Maryann Feldman, 1999. "The New Economics Of Innovation, Spillovers And Agglomeration: Areview Of Empirical Studies," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 8(1-2), pages 5-25.
    8. Wade Cook & Moez Hababou & Hans Tuenter, 2000. "Multicomponent Efficiency Measurement and Shared Inputs in Data Envelopment Analysis: An Application to Sales and Service Performance in Bank Branches," Journal of Productivity Analysis, Springer, vol. 14(3), pages 209-224, November.
    9. William W. Cooper & Lawrence M. Seiford & Kaoru Tone, 2006. "Introduction to Data Envelopment Analysis and Its Uses," Springer Books, Springer, number 978-0-387-29122-2, September.
    10. Yanhong Liu & Xinjian Huang & Weiliang Chen, 2019. "Threshold Effect of High-Tech Industrial Scale on Green Development—Evidence from Yangtze River Economic Belt," Sustainability, MDPI, vol. 11(5), pages 1-21, March.
    11. Liu, Xiaohui & Buck, Trevor, 2007. "Innovation performance and channels for international technology spillovers: Evidence from Chinese high-tech industries," Research Policy, Elsevier, vol. 36(3), pages 355-366, April.
    12. Liping Fu & Xiaodi Jiang, 2019. "Does the Multiple-Participant Innovation Improve Regional Innovation Efficiency? A Study of China’s Regional Innovation Systems," Sustainability, MDPI, vol. 11(17), pages 1-16, August.
    13. Seiford, Lawrence M. & Zhu, Joe, 2002. "Modeling undesirable factors in efficiency evaluation," European Journal of Operational Research, Elsevier, vol. 142(1), pages 16-20, October.
    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. 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.
    2. Bresciani, Stefano & Puertas, Rosa & Ferraris, Alberto & Santoro, Gabriele, 2021. "Innovation, environmental sustainability and economic development: DEA-Bootstrap and multilevel analysis to compare two regions," Technological Forecasting and Social Change, Elsevier, vol. 172(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. Yipeng Zhang, 2023. "The Sustainability of Regional Innovation in China: Insights from Regional Innovation Values and Their Spatial Distribution," Sustainability, MDPI, vol. 15(13), pages 1-42, June.
    5. Wang, Zhe & Chen, Huangxin & Teng, Yin-Pei, 2023. "Role of greener energies, high tech-industries and financial expansion for ecological footprints: Implications from sustainable development perspective," Renewable Energy, Elsevier, vol. 202(C), pages 1424-1435.
    6. Ying Song & Lu Yang & Stavros Sindakis & Sakshi Aggarwal & Charles Chen, 2023. "Analyzing the Role of High-Tech Industrial Agglomeration in Green Transformation and Upgrading of Manufacturing Industry: the Case of China," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 14(4), pages 3847-3877, December.
    7. 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).
    8. Lin, Renzao & Wang, Zhe & Gao, Chunjiao, 2023. "Re-examining resources taxes and sustainable financial expansion: An empirical evidence of novel panel methods for China's provincial data," Resources Policy, Elsevier, vol. 80(C).
    9. Fang Song & Xuerong Xu, 2023. "How Operation Scale Improve the Production Technical Efficiency of Grape Growers? An Empirical Evidence of Novel Panel Methods for China’s Survey Data," Sustainability, MDPI, vol. 15(4), pages 1-19, February.

    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. Wu, Huaqing & Lv, Kui & Liang, Liang & Hu, Hanhui, 2017. "Measuring performance of sustainable manufacturing with recyclable wastes: A case from China’s iron and steel industry," Omega, Elsevier, vol. 66(PA), pages 38-47.
    2. Kao, Chiang, 2014. "Network data envelopment analysis: A review," European Journal of Operational Research, Elsevier, vol. 239(1), pages 1-16.
    3. Liu, John S. & Lu, Louis Y.Y. & Lu, Wen-Min, 2016. "Research fronts in data envelopment analysis," Omega, Elsevier, vol. 58(C), pages 33-45.
    4. Jolly Puri & Shiv Prasad Yadav & Harish Garg, 2017. "A new multi-component DEA approach using common set of weights methodology and imprecise data: an application to public sector banks in India with undesirable and shared resources," Annals of Operations Research, Springer, vol. 259(1), pages 351-388, December.
    5. AGRELL, Per & HATAMI-MARBINI, Adel, 2011. "Frontier-based performance analysis models for supply chain management; state of the art and research directions," LIDAM Discussion Papers CORE 2011069, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    6. 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.
    7. Fukuyama, Hirofumi & Weber, William L., 2010. "A slacks-based inefficiency measure for a two-stage system with bad outputs," Omega, Elsevier, vol. 38(5), pages 398-409, October.
    8. Despotis, Dimitris K. & Koronakos, Gregory & Sotiros, Dimitris, 2016. "The “weak-link” approach to network DEA for two-stage processes," European Journal of Operational Research, Elsevier, vol. 254(2), pages 481-492.
    9. Jorge Antunes & Peter Wanke & Thiago Fonseca & Yong Tan, 2023. "Do ESG Risk Scores Influence Financial Distress? Evidence from a Dynamic NDEA Approach," Sustainability, MDPI, vol. 15(9), pages 1-32, May.
    10. Simona Cohen-Kadosh & Zilla Sinuany-Stern, 2020. "Hip fracture surgery efficiency in Israeli hospitals via a network data envelopment analysis," 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. 28(1), pages 251-277, March.
    11. Kao, Chiang, 2016. "Efficiency decomposition and aggregation in network data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 255(3), pages 778-786.
    12. Khoveyni, Mohammad & Fukuyama, Hirofumi & Eslami, Robabeh & Yang, Guo-liang, 2019. "Variations effect of intermediate products on the second stage in two-stage processes," Omega, Elsevier, vol. 85(C), pages 35-48.
    13. Qu, Jingjing & Wang, Baohui & Liu, Xiaohong, 2022. "A modified super-efficiency network data envelopment analysis: Assessing regional sustainability performance in China," Socio-Economic Planning Sciences, Elsevier, vol. 82(PB).
    14. 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.
    15. Suvvari Anandarao & S. Raja Sethu Durai & Phanindra Goyari, 2019. "Efficiency Decomposition in two-stage Data Envelopment Analysis: An application to Life Insurance companies in India," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 17(2), pages 271-285, June.
    16. 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.
    17. Xiyang Lei & Yongjun Li & Qiwei Xie & Liang Liang, 2015. "Measuring Olympics achievements based on a parallel DEA approach," Annals of Operations Research, Springer, vol. 226(1), pages 379-396, March.
    18. Despotis, Dimitris K. & Sotiros, Dimitris & Koronakos, Gregory, 2016. "A network DEA approach for series multi-stage processes," Omega, Elsevier, vol. 61(C), pages 35-48.
    19. Cook, Wade D. & Seiford, Larry M., 2009. "Data envelopment analysis (DEA) - Thirty years on," European Journal of Operational Research, Elsevier, vol. 192(1), pages 1-17, January.
    20. Chiu, Yung-ho & Huang, Chin-wei & Ma, Chun-Mei, 2011. "Assessment of China transit and economic efficiencies in a modified value-chains DEA model," European Journal of Operational Research, Elsevier, vol. 209(2), pages 95-103, 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:12:y:2020:i:8:p:3284-:d:347069. 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.