Früherkennung wissenschaftlicher Konvergenz im Hochschulmanagement

It is crucial for universities to recognize early signals of scientific convergence. Scientific convergence describes a dynamic pattern where the distance between different fields of knowledge shrinks over time. This knowledge space is beneficial to radical innovations and new promising research topics. Research in converging areas of knowledge can therefore allow universities to establish a leading position in the science community. The Q-AKTIV project develops a new approach on the basis of machine learning to identify scientific convergence at an early stage. In this work, we briefly present this approach and the first results of empirical validation. We discuss the benefits of an instrument building on our approach for the strategic management of universities and other research institutes

Made in academia: the effect of institutional origin on inventors' attention to science

Inventors cannot exploit new scientific discoveries if they do not pay attention to them. However, allocating attention to science is difficult because the scientific literature is vast, fast-changing, and often unreliable. Inventors are therefore likely to rely on informational cues when screening new publications. I posit that inventors pay significantly less attention to discoveries “made in academia” than to those “made in industry” because they believe that the work of academic scientists will be less useful to them. I test this proposition by examining inventors’ patent references to the scientific literature in the case of simultaneous discoveries made by at least one team based in academia and another based in industry. I find that inventors are 23% less likely to cite the academic paper than its twin from industry. My results highlight the importance of inventors’ attention as a hitherto underexplored bottleneck shaping the translation of science into new technologies

Creativity at the Knowledge Frontier: The Impact of Specialization in Fast- and Slow-paced Domains

Using the impact of the Soviet Union’s collapse on the performance of theoretical mathematicians as a natural experiment, we attempt to resolve the controversy in prior research on whether specialists or generalists have superior creative performance. While many have highlighted generalists’ advantage due to access to a wider set of knowledge components, others have underlined the benefits that specialists can derive from their deep expertise. We argue that this disagreement might be partly driven by the fact that the pace of change in a knowledge domain shapes the relative return from being a specialist or a generalist. We show that generalist scientists performed best when the pace of change was slower and their ability to draw from diverse knowledge domains was an advantage in the field, but specialists gained advantage when the pace of change increased and their deeper expertise allowed them to use new knowledge created at the knowledge frontier. We discuss and test the roles of cognitive mechanisms and of competition for scarce resources. Specifically, we show that specialists became more desirable collaborators when the pace of change was faster, but when the pace of change was slower, generalists were more sought after as collaborators. Overall, our results highlight trade-offs associated with specialization for creative performance

Creativity:the interplay of structural and individual characteristics

This dissertation explores the interplay of individuals’ specialization, expertise, and position within the collaboration network in determining creativity. In this regard, each of the three chapters that constitute this dissertation provides a key finding. The first chapter reveals that the same network position can enhance or hamper an individual’s creativity, depending on that individual’s specialization and expertise. The second chapter provides different configurations of specialization, expertise, and network positions that enable the processes of importing new ideas from another field, rather than recombining ideas existing within the same professional field. The third chapter demonstrates that the exchange of ideas through social interaction, especially in the form of collaborators’ creative influence on each other, remains to be a fundamental driver of creativity even after accounting for factors that shape the collaboration network, such as similar reputation or past creative success among collaborators