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Staged efficiency and its determinants of regional innovation systems: a two-step analytical procedure

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  • Chen Kaihua

    ()

  • Kou Mingting

    ()

Abstract

An efficiency-oriented analysis of regional innovation systems will enhance the public’s understanding of their operational ‘quality’, help policymakers in benchmarking innovation performance, and thereby improve policymaking. This study proposes a coherent two-step analytical procedure for modelling the efficiency performance of regional innovation systems and its determinants. The first step of the procedure involves measuring efficiency associated with a sophisticated network data envelopment analysis model, which accounts for the linkages between the two disaggregated sub-processes, namely an upstream technological creation process (TCrP) and a downstream technological commercialisation process (TCoP), during a technological innovation process (TIP). The procedure simultaneously deals with overall TIP efficiency as well as the two component TCrP and TCoP efficiencies in a united framework. In the second step of the procedure, the paper examines the effects of policy-oriented environmental factors on the respective efficiency scores of the three processes (TIP, TCrP, and TCoP) associated with a flexible partial least squares regression. We apply the two-step hybrid analytical procedure to China’s province-level regional innovation systems. Our empirical study shows that China’s regional innovation systems perform poorly in both technological creation efficiency and technological commercialisation efficiency at the provincial average level. This awkward situation is attributed to unfavourable environmental factors. Our findings indicate that the embedded and contextualised policy-oriented environment does not effectively suit the TIP within China’s regional innovation systems. Copyright Springer-Verlag Berlin Heidelberg 2014

Suggested Citation

  • Chen Kaihua & Kou Mingting, 2014. "Staged efficiency and its determinants of regional innovation systems: a two-step analytical procedure," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 52(2), pages 627-657, March.
  • Handle: RePEc:spr:anresc:v:52:y:2014:i:2:p:627-657
    DOI: 10.1007/s00168-014-0604-6
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    References listed on IDEAS

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    Cited by:

    1. Kwon, He-Boong, 2017. "Exploring the predictive potential of artificial neural networks in conjunction with DEA in railroad performance modeling," International Journal of Production Economics, Elsevier, vol. 183(PA), pages 159-170.
    2. Bi, Kexin & Huang, Ping & Wang, Xiangxiang, 2016. "Innovation performance and influencing factors of low-carbon technological innovation under the global value chain: A case of Chinese manufacturing industry," Technological Forecasting and Social Change, Elsevier, vol. 111(C), pages 275-284.
    3. Areti Gkypali & Vasileios Kokkinos & Christos Bouras & Kostas Tsekouras, 2016. "Science parks and regional innovation performance in fiscal austerity era: Less is more?," Small Business Economics, Springer, vol. 47(2), pages 313-330, August.

    More about this item

    Keywords

    O11; P51; C61;

    JEL classification:

    • O11 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Macroeconomic Analyses of Economic Development
    • P51 - Economic Systems - - Comparative Economic Systems - - - Comparative Analysis of Economic Systems
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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