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

Nuno Fernandes Crespo and Cátia Fernandes Crespo

Journal of Business Research, 2016, vol. 69, issue 11, 5265-5271

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.

Keywords: National Innovation Systems; GII data; fsQCA; Country level of economic development; Country innovation outcomes; Country input innovation enablers (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (34)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jbrese:v:69:y:2016:i:11:p:5265-5271

DOI: 10.1016/j.jbusres.2016.04.123

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