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首页> 外文期刊>Annals of Operations Research >Measuring efficiency of innovation using combined Data Envelopment Analysis and Structural Equation Modeling: empirical study in EU regions
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Measuring efficiency of innovation using combined Data Envelopment Analysis and Structural Equation Modeling: empirical study in EU regions

机译:利用组合数据包络分析和结构方程模型测量创新效率:欧盟地区实证研究

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The main aim of this paper is to investigate the impact of patent applications, development level, employment level and degree of technological diversity on innovation efficiency. Innovation efficiency is derived by relating innovation inputs and innovation outputs. Expenditures in Research and Development and Human Capital stand for innovation inputs. Technological knowledge diffusion that comes from spatial and technological neighborhood stands for innovation output. We derive innovation efficiency using Data Envelopment Analysis for 192 European regions for a 12-year period (1995-2006). We also examine the impact of patents production, development and employment level and the level of technological diversity on innovation efficiency using Structural Equation Modeling. This paper contributes a method of innovation efficiency estimation in terms of regional knowledge spillovers and causal relationship of efficiency measurement criteria. The study reveals that the regions presenting high innovation activities through patents production have higher innovation efficiency. Additionally, our findings show that the regions characterized by high levels of employment achieve innovation sources exploitation efficiently. Moreover, we find that the level of regional development has both a direct and indirect effect on innovation efficiency. More accurately, transition and less developed regions in terms of per capita GDP present high levels of efficiency if they innovate in specific and limited technological fields. On the other hand, the more developed regions can achieve high innovation efficiency if they follow a more decentralized innovation policy.
机译:本文的主要目的是探讨专利申请,发展水平,就业水平和技术多样程度对创新效率的影响。通过在创新投入和创新产出中获得创新效率。研发的支出和人力资本代表创新投入。来自空间和技术街区的技术知识扩散代表着创新产出。我们使用192年欧洲地区的数据包络分析来获得创新效率(1995-2006)。我们还使用结构方程式建模研究专利生产,开发和就业水平的影响和技术多样性对创新效率的影响。本文在区域知识溢出率和效率测量标准的因果关系方面提供了一种创新效率估算方法。该研究表明,通过专利生产提出了高创新活动的地区具有更高的创新效率。此外,我们的研究结果表明,该地区以高水平的就业为特色,实现了创新源的利用有效利用。此外,我们发现区域发展水平对创新效率具有直接和间接的影响。更准确地说,如果在特定和有限的技术领域创新,则在人均GDP方面的过渡和较少发达的地区呈现出高水平的效率。另一方面,如果他们遵循更分散的创新政策,越发达的地区就越发达的地区可以实现高创新效率。

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