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Interest of STI policies to influence the innovation behaviour of firms has been increased considerably. This gives rise to the notion of behavioural additionality, broadening traditional evaluation concepts of input and output additionality. Though there is empirical work measuring behavioural additionalities, we know little about what role distinct firm characteristics play for their occurrence. The objective is to estimate how distinct firm characteristics influence the realisation of behavioural additionalities. We use survey data on 155 firms, considering the behavioural additionalities stimulated by the Austrian R&D funding scheme in the field of intelligent transport systems in 2006. We focus on three different forms of behavioural additionality project additionality, scale additionality and cooperation additionality and employ binary regression models to address this question. Results indicate that R&D related firm characteristics significantly affect the realisation of behavioural additionality. Firms with a high level of R&D resources are less likely to substantiate behavioural additionalities, while small, young and technologically specialised firms more likely realise behavioural additionalities. From a policy perspective, this indicates that direct R&D promotion of firms with high R&D resources may be misallocated, while attention of public support should be shifted to smaller, technologically specialised firms with lower R&D experience.

Innovation efficiency is a key factor influencing the position of high-tech industries in the global value chain. Through stochastic frontier analysis, innovation efficiency of China's high-tech industry from 2000 to 2016 was estimated and analyzed. Through five random frontier analysis models, innovation efficiency of new product sales revenue and number of patent applications are analyzed. Results show that the overall level of innovation efficiency in China's high-tech industry is not high. Government subsidies for innovation have a positive impact on the R&D results of new product income from China’s high-tech industry, but have a negative impact on the number of patent applications. Scale of enterprise, degree of openness of enterprise, quality of the labor force and export delivery have a positive impact on innovation efficiency of China's high-tech industry. R&D capital stock and R&D human capital stock have a positive effect on high-tech industry innovation. In high-tech industry's transition from patents to new products, there will be a low conversion rate. Results of economic analysis can help the government to make the basis for management decisions. Conclusion of innovation performance analysis provides practical normative guidance for these high-tech industries.

Innovation policy (science & technology policy/program) aims to stimulate industrial innovation and address the gap between ideas and the market for new products/process. Hence, small and medium enterprises (SMEs) are an important target group for innovation policy. While SMEs play important economic role in Taiwan, it is more meaningful to evaluate related innovation policies, to understand the impact of polices as well as test theoretical models of interactions between the public and private sectors. This topic is significant but little studied or investigated with the chance of bias. From the perspective of program evaluation, the thesis evaluated the effect of government subsidies on SMEs¡¦ innovation including impact assessment and efficiency assessment and took the Small Business Innovation Research (SBIR) Program as an example. The target population for evaluation covered three groups: SBIR awardees, firms with rejected applications, general SME manufacturers. Questionnaires were delivered to 942 firms with SBIR Phase I or Phase II awards and 222 firms with rejected applications between 1999 and 2004; 374 and 36 valid questionnaires were returned separately. The Department of Statistics of the Ministry of Economic Affairs provided the data of general SMEs. This thesis evaluated the impact of SBIR by a quasi-experimental design and examines the efficiency by an econometric model. Main findings are as follows: A. The impact of government R&D subsidies on SMEs: 1. Innovative activity (R&D spending): Compared to other SMEs (firms with rejected applications or general SME manufacturers), the growth of SBIR awardees¡¦ R&D spending is significant. 2. Productivity (employment or sales): Compared to other SMEs (firms with rejected applications or general SME manufacturers), the growth of SBIR awardees¡¦ employment is significant. Compared to general SME manufacturers, the growth of SBIR awardees¡¦ sales is significant; but compared to firms with rejected applications, the growth of SBIR awardees¡¦ sales is not significant. B. The efficiency of government R&D subsidies on SMEs: 1. Innovative activity (R&D spending): On average, 0.28 percentage change in SBIR awardees¡¦ R&D spending is correlated with 1 percent change in subsidies (elasticity relationship). 2. Productivity (employment or sales): On average, 0.08 percentage change in SBIR awardees¡¦ employment and 0.25 percentage change in SBIR awardees sales is separately correlated with 1 percent change in subsidies (elasticity relationship).

Structural differences regarding the presence of large firms are likely to influence the performance of Regional Innovation Systems. Regions lacking large firms to act as brokers of knowledge and coordinators of regional (R&D) collaboration may have to rely on other actors to form internal and external links. We investigate whether, in this case, universities can fulfill the needs of Small- and Medium-sized Enterprises (SMEs) with regard to coordination and knowledge flows. Using a data set of subsidized R&D collaborations, we compare universities' network positions in four model regions in Germany. Applying a Temporal Exponential Random Graph Model approach, we examine link formation and network structure with a focus on university–SME ties and their development over time. Results indicate that SMEs profit from connections to the university in all regions. Nevertheless, universities take more central roles in regions where the economic surrounding does contain fewer large firms as sources for knowledge exchange.