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

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.