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Global innovation index: Moving beyond the absolute value of ranking with a fuzzy-set analysis

机译：全球创新指数：通过模糊集分析超越排名的绝对值

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This study applies a fuzzy-set qualitative comparative analysis to data from the Global Innovation Index (GII). Building on the National Innovation System's approach, this study posits that a country can achieve high innovation performance via several combinations of causal conditions. These conditions are the five input enablers of GII: institutions, human capital and research, infrastructure, market sophistication, and business sophistication. By defining two subsamples of countries (high-income and low-income), this study finds that several causal combinations of conditions lead to high innovation performance in both groups. In order to obtain better innovation performance, the low-income countries show more multifaceted solutions. These results indicate that none of the conditions is necessary for predicting high innovation performance in both samples. Additionally, in the low-income group, none of the conditions, individually, is sufficient to predict higher innovation performance, while in the high-income group the infrastructure and human capital and research conditions, on their own, are sufficient to obtain better innovation performance. These results indicate that the political decision making processes required for improving the level of innovation need to be different for each group of countries. (C) 2016 Elsevier Inc. All rights reserved.
机译：这项研究对全球创新指数（GII）的数据进行了模糊集定性比较分析。本研究基于国家创新体系的方法，假设一个国家可以通过多种因果条件组合来实现较高的创新绩效。这些条件是GII的五个投入推动因素：机构，人力资本和研究，基础设施，市场成熟度和业务成熟度。通过定义国家的两个子样本（高收入和低收入），本研究发现条件的几种因果组合导致两组中的高创新绩效。为了获得更好的创新绩效，低收入国家展示了更多层面的解决方案。这些结果表明，在两个样本中都没有必要条件来预测高创新绩效。此外，在低收入群体中，没有任何一个条件足以单独预测更高的创新绩效，而在高收入人群中，基础设施，人力资本和研究条件本身不足以获得更好的创新性能。这些结果表明，提高创新水平所需的政治决策过程对于每个国家组都应有所不同。 （C）2016 Elsevier Inc.保留所有权利。

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