Faculty of Entrepreneurship, University of Tehran Journal of Entrepreneurship Development 2008-2266 7 1 2014 04 21 Ranking of Selected Countries According to National Innovation Capacity Improvement Using Data Envelopment Analysis Ranking of Selected Countries According to National Innovation Capacity Improvement Using Data Envelopment Analysis 1 16 51552 10.22059/jed.2014.51552 FA Nezamodin Faghih Professor of management, University of Shiraz, Iran Kazem Askarifar Ph.D. Candidate, Systems management, University of Shiraz, Iran Journal Article 2014 01 11 Each country could optimize the innovation process by improving infrastructures and national atmosphere. With regard to methodolory, in this paper 11 variables are selected among 20 demographic, R&D, and business variables as inputs based on experts’ opinions and statistical methods. Patent registrations data in USPTO is considered as the model output. Data of input variables gathered from published reports of some institutions and organizations such as World Bank and Heritage foundation. Then 57 countries are ranked according to input oriented CCR, output oriented BCC and slack-based data envelopment analysis model. The findings show that Slack-based model presents better results with considering input and output variables simultaneously. Developed countries with suitable infrastructures and registered patents are at top ranks, meanwhile nondeveloped and developing countries with lower ranks have weak innovation infrastructures or outcomes. Also, governmental and Nongovernmental budget and professional researchers of R&D are the most important variables for improving innovation atmosphere, which must be considered in policy-making decisions. Each country could optimize the innovation process by improving infrastructures and national atmosphere. With regard to methodolory, in this paper 11 variables are selected among 20 demographic, R&D, and business variables as inputs based on experts’ opinions and statistical methods. Patent registrations data in USPTO is considered as the model output. Data of input variables gathered from published reports of some institutions and organizations such as World Bank and Heritage foundation. Then 57 countries are ranked according to input oriented CCR, output oriented BCC and slack-based data envelopment analysis model. The findings show that Slack-based model presents better results with considering input and output variables simultaneously. Developed countries with suitable infrastructures and registered patents are at top ranks, meanwhile nondeveloped and developing countries with lower ranks have weak innovation infrastructures or outcomes. Also, governmental and Nongovernmental budget and professional researchers of R&D are the most important variables for improving innovation atmosphere, which must be considered in policy-making decisions. https://jed.ut.ac.ir/article_51552_35dc5f2fd2cedcc80ebc126df55da1af.pdf