There is a wide variety of channels through which knowledge and technology is being transferred between universities and industry. This paper aims to explain the relative importance of these ...different channels in different contexts. For this purpose, responses from two questionnaires were analysed, addressing Dutch industrial and university researchers, respectively. A reassuring result is that the perceived importance of the 23 distinct transfer channels we distinguished hardly differs between industry and university: we did not observe a major mismatch. Overall, our results suggest that the industrial activities of firms do not significantly explain differences in importance of a wide variety of channels through which knowledge between university and industry might be transferred. Instead, this variety is better explained by the disciplinary origin, the characteristics of the underlying knowledge, the characteristics of researchers involved in producing and using this knowledge (individual characteristics), and the environment in which knowledge is produced and used (institutional characteristics). Based on our findings, we offer policy recommendations.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
This paper estimates the effect of heterogeneous university funding programs within the German Excellence Initiative on a regional firm's probability to innovate by using a multi-valued two-way fixed ...effects difference-in-differences model. The estimations show that funding an additional Excellence Cluster focused on internationally competitive research within a labor market region increases a regional firm's probability to innovate. This effect is driven by firms within labor market regions receiving a high number of Excellence Clusters. There is no statistically significant effect for receiving a low number of Excellence Clusters. Moreover, we find no consistent statistically significant effect of funding Graduate Schools concentrating on training scientists nor of funding University Strategies promoting the overall long-term plan of a university.
•Funding Excellence Clusters within labor market regions increases the innovativeness of firms•The positive effect of funding Excellence Clusters is driven by regions receiving a high dose of Excellence Cluster funding•The positive effect of funding a high dose of Excellence Cluster is economically relevant for individual labor market regions and firms•Funding Graduate Schools or University Strategies does not increase the innovativeness of firms
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
A multifactorial evolutionary algorithm (MFEA) is a recently proposed algorithm for evolutionary multitasking, which optimizes multiple optimization tasks simultaneously. With the design of knowledge ...transfer among different tasks, MFEA has demonstrated the capability to outperform its single-task counterpart in terms of both convergence speed and solution quality. In MFEA, the knowledge transfer across tasks is realized via the crossover between solutions that possess different skill factors . This crossover is thus essential to the performance of MFEA. However, we note that the present MFEA and most of its existing variants only employ a single crossover for knowledge transfer, and fix it throughout the evolutionary search process. As different crossover operators have a unique bias in generating offspring, the appropriate configuration of crossover for knowledge transfer in MFEA is necessary toward robust search performance, for solving different problems. Nevertheless, to the best of our knowledge, there is no effort being conducted on the adaptive configuration of crossovers in MFEA for knowledge transfer, and this article thus presents an attempt to fill this gap. In particular, here, we first investigate how different types of crossover affect the knowledge transfer in MFEA on both single-objective (SO) and multiobjective (MO) continuous optimization problems. Furthermore, toward robust and efficient multitask optimization performance, we propose a new MFEA with adaptive knowledge transfer (MFEA-AKT), in which the crossover operator employed for knowledge transfer is self-adapted based on the information collected along the evolutionary search process. To verify the effectiveness of the proposed method, comprehensive empirical studies on both SO and MO multitask benchmarks have been conducted. The experimental results show that the proposed MFEA-AKT is able to identify the appropriate knowledge transfer crossover for different optimization problems and even at different optimization stages along the search, which thus leads to superior or competitive performances when compared to the MFEAs with fixed knowledge transfer crossover operators.
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