What kind of capacity is needed to improve the performance of start-ups? How effective are government support policies in improving start-up performance? Start-ups are critical firm group for ensuring the prospective and sustainable growth of an economy, and thus many countries’ governments have established support policies and they are likely to engage more widely in forward-looking political support activities to ensure further growth and expansion. In this paper, the effect of innovation capabilities and government support policies on start-up performance is examined. We used an unbalanced panel data analysis with a random effect generalized least squares. We investigated the effect of government support policies on 4368 Korean start-ups. The findings indicated that technology and knowledge capabilities had positive effects on the sales performance of start-ups, and government financial support positively affected the relationship between knowledge capability and firm performance. However, when government financial support increased, marketing capability was negatively associated with firm performance. These results demonstrate the significant role of government financial support, including its crowding in but also its crowding out effect. Practical implications: To be more effective, governments should employ innovation-driven entrepreneurship policy approaches to support start-ups. To improve their performance, start-ups need to increase their technology and knowledge capabilities. This study extends recent efforts to understand more fully the effect of government support policies on start-ups differing in their technology, knowledge, and marketing capabilities.

How do the organizational attributes of public R&D organizations affect their performance? Recent researchers have focused on the relationship between R&D investments and performance or efficiency, and others suggest that performance may be affected by organizational factors. However, we focus on the attributes of R&D workforce excellence and diversity of R&D workforce, which can affect the performance of public R&D organizations. Therefore, we conduct a panel negative binomial regression and a focus group interview after empirical analysis to derive interpretations and implications. The results show that the effects of the diversity and excellence of an R&D workforce can vary depending on the type of performance: scientific, technological, or social. Overall, we call for a human resources and resource-based approach to consider the importance of R&D workforce composition, specifically diversity and excellence, in evaluating the performance of public R&D organizations.