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

Abstract

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.