Purpose: - Despite the dearth of research on innovation performance, the key determinants of innovation performance are still blurred. Besides, comparative research on the determinants of innovation performance among countries at different income levels: high-income, upper-middle-income, lower-middle-income, and low-income, is not common. This study is, therefore, aimed to bridge this research gap by considering the innovation performance of 63 countries. Methodology: - Participating countries were purposefully selected from the Global Innovation Index (GII) dataset. Multistage analyses were conducted: first, a linear regression was run to identify the most decisive pillars; then, stepwise regression was applied to identify the best predicting model of innovation performance; thirdly, to examine the variation in innovation performance and figure out key determinants in each country groups, the ANOVA analysis was done.Results: - Human capital and research, infrastructure, and business sophistication are the key pillars determining innovation performance in general. The best predicting variables are innovation linkage & knowledge absorption (both pertaining to business sophistication), R&D and infrastructure (inculcating both physical and digital). The human capital that promotes R&D activities is the biggest bottleneck hampering promotion of innovation in the countries and firms at lower-middle income category, whereas innovation linkage in a high-income category and both human capital that promotes R&D activities and innovation linkage in an upper-middle income category. Hence, countries and firms in these income categories should give priorities accordingly to these decisive bottlenecks hindering the innovation performance.Implication: - The result implies that country's economic growth can be defined by the level of innovation performance and the challenges of innovation vary as per the countries’ development stage. Accordingly, bottleneck factors need to be identified & addressed properly in a policy direction first at firm level and then at country level.Originality/Value: - The study claims to have extended the horizon of understanding on determinants of innovation across countries and revealed the most crucial factors in each category of countries. Further empirical comparative research can be done by stratifying firms as SMEs and Large firms in each category of countries.

The effectiveness of entrepreneurial activities is not only determined by the quality of entrepreneurs but also by the ecosystem of entrepreneurship. The entrepreneurial ecosystem (EE) that nurtures low-quality “moppets” to highly impactful “gazelles” is being widely debated and on-demand in literature. This study, therefore, is aimed to advance the discussion and make a comparative analysis of the entrepreneurial ecosystem, which has been given a little attention, of BRICS club countries with an especial focus on South Africa, Brazil, and India. Various entrepreneurship-economic growth-related measures including Global Entrepreneurship Index (GEI), Global Competitiveness Index (GCI), Index Economic Freedom (IEF), and Legatum Prosperity Index (LPI) are used to compare the countries’ entrepreneurial ecosystem. Especially, the data set (2012–2018) of GEI was utilized for the analysis. According to GEI and GCI of 2018, China is leading BRICS club in terms of growth and entrepreneurial ecosystem. On the other side, LPI, IEF, and GEI put South Africa’s entrepreneurial ecosystem in a favorable position as compared to Brazil and India. South Africa performs poorly in startup skills, while both the latter ones are better and stand at the same level. This shows that South Africa’s tertiary education, coupled with low skill perception, is less effective in equipping the population to be entrepreneurs as compared to India and Brazil. Whereas Brazil and India are at their worst in internationalizing the country’s entrepreneurs and technological absorption, respectively. South Africa is more like India in product innovation and risk acceptance. On the other side, it is more like Brazil in risk capital, technological absorption, opportunity perception, and in their sluggish economic growth. Overall, South Africa (57th/140 as of 2018) is categorized among those poorly performing countries in terms of start-up skills, networking, technology absorption, human Capital, and risk capital pillars. The government of South Africa needs to primarily work on these bottle-neck pillars to improve its EE. To increase GEI by 5%, it should invest 77% of its extra resource on start-up skills, 18% on risk capital, and 5% on technology absorption. Applying GEI set up, this paper claims to have uniquely contributed to how to make a country comparison on the EE. Further empirical research can be done including all BRICS countries to bolster their development effort and on how to promote EE by tackling the underlying bottlenecks.

Despite the dearth of research on innovation, the key determinants of innovation performance still need to be clarified. Besides, a comparative analysis of the determinants of innovation performance across countries at different income levels has yet to be found. This study, therefore, aims to bridge this research gap by considering the innovation performance of 63 countries. Participating countries were purposefully selected from the Global Innovation Index (GII) dataset. Multistage and multimodal analyses were conducted, including multiple linear regressions, hierarchical regression, and ANOVA, to examine the variation in innovation performance and pinpoint critical determinants in each category of countries. The result reveals that human capital, research, infrastructure, and business sophistication are the key pillars determining countries’ innovation performance. In a variable-level analysis, innovation linkage and knowledge absorption (both of business sophistication), research and development (R&D), and infrastructure (inculcating both physical and digital) are the best predicting variables. The shortage of human capital to promote R&D is the biggest bottleneck hampering innovation in the lower-middle-income category. Also, both human capital for R&D activities and innovation linkage equally affect the upper-middle-income, and the latter one, innovation linkage, remains the main challenge even for the high-income category. The study implies that innovation performance predicts a country’s economic growth. The level of innovation performance and the determinants of innovation vary per the countries’ income levels. Accordingly, countries and firms in various income categories should prioritize tackling their respective bottlenecks hindering innovation performance in their policy directions. The study claims to have extended the horizon of understanding determinants of innovation across countries and revealed the most crucial factors in each category of countries. Further empirical comparative research can be done by incorporating an informal institution, national culture, as an additional determinant and specifying sectors across income categories.