United we stand, divided we fall : essays on knowledge and its diffusion in innovation networks

Knowledge is a key resource, allowing firms to innovate and keep pace with national and international competitors. Therefore, the management of this resource within firms and innovation networks is of utmost importance. As the collection and generation of (new) knowledge gives such competitive advantage, there is a strong interest of firms and policy makers on how to foster the creation and diffusion of new knowledge. Within four studies, this doctoral thesis aims at extending the literature on knowledge diffusion performance by focussing on the effect of different network structures on diffusion performance as well as on knowledge types besides mere techno-economic knowledge. Study 1 analyses the effect of different structural disparities on knowledge diffusion by using an agent-based simulation model. It focuses on how different network structures influence knowledge diffusion performance. This study especially emphasizes the effect of an asymmetric degree distribution on knowledge diffusion performance. Study 1 complements previous research on knowledge diffusion by showing that (i) besides or even instead of the average path length and the average clustering coefficient, the (symmetry of) degree distribution influences knowledge diffusion. In addition, (ii) especially small, inadequately embedded agents seem to be a bottleneck for knowledge diffusion in this setting, and iii) the identified rather negative network structures on the macro level seem to result from the myopic linking strategies of the actors at the micro level, indicating a trade-off between optimal structures at the network and at the actor level. Study 2 uses an agent-based simulation model to analyse the effect of different network properties on knowledge diffusion performance. In contrast to study 1, this study analyses this relationship in a setting in which knowledge is diffusing freely throughout an empirical formal R&D network as well as through four benchmark networks. In addition, the concept of cognitive distance and differences in learning between agents in the network are taken into account. Study 2 complements study 1 and further previous research on knowledge diffusion by showing that (i) the (asymmetry of) degree distribution and the distribution of links between actors in the network indeed influence knowledge diffusion performance to a large extend. In addition, (ii) the extent to which a skewed degree distribution dominates other network characteristics varies depending on the respective cognitive distance between agents. Study 3 analyses how so called dedicated knowledge can contribute to the transformation towards a sustainable, knowledge-based Bioeconomy. In this study, the concept of dedicated knowledge, i.e. besides mere-techno economic knowledge also systems knowledge, normative knowledge and transformative knowledge, is first introduced. Moreover, the characteristics of dedicated knowledge which are influencing knowledge diffusion performance are analysed and evaluated according to their importance and potential role for knowledge diffusion. In addition, it is analysed if and how current Bioeconomy innovation policies actually account for dedicated knowledge. This study complements previous research by taking a strong focus on different types of knowledge besides techno-economic knowledge (often overemphasized in policy approaches). It shows, that i) different types of knowledge necessarily need to be taken into account when creating policies for knowledge creation and diffusion, and ii) that especially systems knowledge so far has been insufficiently considered by current Bioeconomy policy approaches. Study 4 analyses the effect of different structural disparities on knowledge diffusion by deducing from theoretical considerations on network structures and diffusion performance. The study tries to answer whether the artificially generated network structures seem favourable for the diffusion of both mere techno-economic knowledge as well as dedicated knowledge. Study 4 especially complements previous research on knowledge diffusion by (i) analysing an empirical network over a long period of time, and (ii) by indicating a potential trade-off between structures favourable for the diffusion of mere techno-economic knowledge and those for the diffusion of other types of dedicated knowledge. Summing up, it is impossible to make general statements that allow for valid policy recommendations on network structures optimal for knowledge diffusion. Without knowing the exact structures and context, politicians will hardly be able to influence network structures. Especially if we call for knowledge enabling transformations as the transformation towards a sustainable knowledge-based Bioeconomy, creating structures for the creation and diffusion of this knowledge is quite challenging and needs for the inclusion and close cooperation of many different actors on multiple levels.Wissen ist eine Schlüsselressource. Daher ist die Verwaltung dieser Ressource in Unternehmen und Innovationsnetzwerken von größter Bedeutung. Da das Sammeln und Generieren von (neuem) Wissen einen derartigen Wettbewerbsvorteil bietet, besteht ein starkes Interesse seitens Unternehmen und politischen Entscheidungsträgern, die Schaffung und Verbreitung von neuem Wissen zu fördern. Im Rahmen dieser Doktorarbeit soll in vier Studien die Literatur zu Wissensdiffusion erweitert werden, indem die Auswirkungen verschiedener Netzwerkstrukturen sowie die verschiedener Wissensarten in den Mittelpunkt gestellt werden. Studie 1 analysiert die Auswirkungen struktureller Unterschiede auf die Wissensdiffusion unter Verwendung eines agentenbasierten Simulationsmodells. Der Fokus liegt hierbei darauf, wie verschiedene Netzwerkstrukturen die Leistung der Wissensverbreitung beeinflussen. Diese Studie betont insbesondere den Effekt einer asymmetrischen Gradverteilung auf die Wissensdiffusionsleistung. Studie 1 ergänzt bisherige Arbeiten zu Wissensdiffusion, indem sie zeigt, dass (i) neben oder sogar anstelle der durchschnittlichen Pfadlänge und des durchschnittlichen Clustering-Koeffizienten die (Symmetrie der) Gradverteilung die Wissensdiffusion stark beeinflusst. Außerdem scheinen (ii) besonders kleine, unzureichend eingebettete Akteure ein Engpass für die Wissensverbreitung in diesem Umfeld zu sein. Studie 2 verwendet ein agentenbasiertes Simulationsmodell, um die Auswirkungen verschiedener Netzwerkeigenschaften auf die Leistung der Wissensdiffusion zu analysieren. Im Gegensatz zu Studie 1 analysiert diese Studie die freie Verbreitung von Wissen in einem empirischen, formalen FuE-Netzwerk sowie in vier Benchmark-Netzwerken. Darüber hinaus werden das Konzept der kognitiven Distanz und Unterschiede beim Lernen zwischen Agenten im Netzwerk berücksichtigt. Studie 2 ergänzt Studie 1 und weitere Forschung, indem sie zeigt, dass (i) die (Asymmetrie) der Gradverteilung und die Verteilung der Verbindungen zwischen den Akteuren des Netzwerks tatsächlich die Leistung der Wissensdiffusion stark beeinflussen. (ii) Das Ausmaß, in dem eine Verteilung der Verbindungen andere Netzwerkcharakteristiken dominiert, variiert in Abhängigkeit von der jeweiligen kognitiven Entfernung zwischen den Agenten. In Studie 3 wird analysiert, wie sogenanntes dediziertes Wissen zur Transformation hin zu einer nachhaltigen, wissensbasierten Bioökonomie beitragen kann. In dieser Studie wird zunächst das Konzept des dedizierten Wissens eingeführt, d. H. neben rein techno-ökonomischem Wissen auch Systemwissen, normatives Wissen und transformatives Wissen. Darüber hinaus werden die Merkmale des dedizierten Wissens analysiert und entsprechend ihrer Bedeutung und möglichen Rolle für die Wissensverbreitung bewertet. Darüber hinaus wird analysiert, ob und wie die aktuelle Innovationspolitik der Bioökonomie tatsächlich dediziertes Wissen berücksichtigt. Diese Studie ergänzt die bisherigen Forschungsarbeiten, indem sie neben technoökonomischem Wissen (das in politischen Ansätzen oft überbewertet wird) einen starken Fokus auf verschiedene Arten von Wissen legt. Es zeigt sich, dass i) unterschiedliche Arten von Wissen notwendigerweise bei der Erstellung von Strategien zur Schaffung und Verbreitung von Wissen berücksichtigt werden müssen, und ii) dass insbesondere Systemwissen bislang in aktuellen Bioökonomiepolitikstrategien nicht ausreichend berücksichtigt wurde. Studie 4 analysiert die Auswirkungen verschiedener struktureller Ungleichheiten auf die Wissensdiffusion, indem aus theoretischen Überlegungen die Diffusionsleistung hergeleitet wird. Die Studie versucht zu beantworten, ob die künstlich erzeugten Netzwerkstrukturen für die Verbreitung von rein technoökonomischem Wissen sowie von dediziertem Wissen günstig erscheinen. Studie 4 ergänzt insbesondere die bisherigen Forschungsarbeiten, indem sie (i) ein empirisches Netzwerk über einen langen Zeitraum analysiert und (ii) einen potenziellen Zielkonflikt aufzeigt zwischen Strukturen, die für die Verbreitung von rein technoökonomischem Wissen günstig sind, und solchen für die Verbreitung anderer Arten von dediziertem Wissen. Zusammenfassend lässt sich sagen, dass es nicht möglich ist, allgemein gültige Aussagen und politische Handlungsempfehlungen zu optimalen Netzwerkstrukturen zu treffen. Ohne die genauen Strukturen und Zusammenhänge zu kennen, kann die Politik kaum Einfluss auf Netzwerkstrukturen und Wissensverbreitung nehmen. Insbesondere wenn wir nach Wissen verlangen, das eine Transformation zu einer nachhaltigen, wissensbasierten Bioökonomie ermöglicht, stellt die Schaffung von Strukturen für die Verbreitung dieses Wissens eine große Herausforderung dar und erfordert die Einbindung und enge Zusammenarbeit vieler verschiedener Akteure auf mehreren Ebenen

The effect of project funding on innovative performance : an agent-based simulation model

Analyzing the effect of Direct Project Funding (DPF) on innovative performance of economic agents is a major challenge for innovation economists and policy makers who must give valid policy recommendations and decide on the allocation of financial resources. An approach that becomes more and more important is the use of agent-based modeling in analyzing innovative performance of market players. In this paper, an agentbased percolation model is used to investigate the effects of project funding on innovative performance in terms of the maximum technological frontier that can be reached as well as in terms of the number of innovations generated by firms. The model results show that firms which participate in subsidized projects outperform firms that do not participate in subsidized projects, especially in increasingly complex technological fields. However, the worse performance of firms that do not participate in subsidized projects can be offset by an increase in the firms financial resources. Hence, the model indicates, the effect of project funding is a purely financial one and might even have negative effects on innovative performance. This is the case if, for instance, a high number of funded research projects disturbs firms paths through the technology space. Following the results of the model, project funding is most effective and important in increasingly complex technology spaces and less effective and important in less complex technology spaces. Moreover, the model results show, other financial resources as venture capital can substitute for direct project funding

Knowledge networks in the German bioeconomy : network structure of publicly funded R&D networks

Aiming at fostering the transition towards a sustainable knowledge-based Bioeconomy (SKBBE), the German Federal Government funds joint and single research projects in predefined socially desirable fields as, for instance, in the Bioeconomy. To analyse whether this policy intervention actually fosters cooperation and knowledge transfer as intended, researchers have to evaluate the network structure of the resulting R&D network on a regular basis. Using both descriptive statistics and social network analysis, I investigate how the publicly funded R&D network in the German Bioeconomy has developed over the last 30 years and how this development can be assessed from a knowledge diffusion point of view. This study shows that the R&D network in the German Bioeconomy has grown tremendously over time and thereby completely changed its initial structure. While from a traditional perspective the development of the network characteristics in isolation seems harmful to knowledge diffusion, taking into account the reasons for these changes shows a different picture. However, this might only hold for the diffusion of mere techno-economic knowledge. It is questionable whether the artificially generated network structure also is favourable for the diffusion of other types of knowledge, e.g. dedicated knowledge necessary for the transformation towards an SKBBE

Simulating knowledge diffusion in four structurally distinct networks : an agent-based simulation model

In our work we adopt a structural perspective and apply an agent-based simulation approach to analyse knowledge diffusion processes in four structurally distinct networks. The aim of this paper is to gain an in-depth understanding of how network characteristics, such as path length, cliquishness and the distribution and asymmetry of degree centrality affect the knowledge distribution properties of the system. Our results show in line with the results of Cowan and Jonard (2007) that an asymmetric or skewed degree distribution actually can have a negative impact on a networks knowledge diffusion performance in case of a barter trade knowledge diffusion process. Their key argument is that stars rapidly acquire so much knowledge that they interrupt the trading process at an early stage, which finally disconnects the network. However, our findings reveal that stars cannot be the sole explanation for negative effects on the diffusion properties of a network. In contrast, interestingly and quite surprisingly, our simulation results led to the conclusion that in particular very small, inadequately embedded agents can be a bottleneck for the efficient diffusion of knowledge throughout the networks

Born to transform?: German bioeconomy policy and research projects for transformations towards sustainability

Aiming at fostering the transition towards a sustainable climate-neutral economy, the German Federal Government (GFG) intends to promote the transition towards a sustainable knowledge-based bioeconomy (SKBBE). Bioeconomy policies are adjusted regularly, increasingly focusing on addressing the grand challenges of our time. To analyze whether the German bioeconomy policy, in terms of strategies and publicly funded research projects, actually fosters knowledge creation for the desired transformation, these strategies and projects have to be evaluated. Using a mixed-methods approach, this paper aims at investigating in what way German bioeconomy policy is dedicated to transformations towards sustainability and whether this reflects in the publicly funded research projects. Our study shows that the strategies as well as the publicly funded projects, still have a strong techno-economic orientation, focusing on technologies as problem-solvers, lacking, e.g., normative or transformative knowledge. What is more, the artificially generated R&D network does not show the necessary structure or involvement of stakeholders, lacking, e.g., the involvement of civil society or transdisciplinary research. We argue that future innovation policy has to foster all types of knowledge relevant for transformations towards sustainability, incorporating all stakeholders. Otherwise, the bioeconomy transition might become a purely technological endeavor unable to foster strong sustainability

Human Pathologic Validation of Left Ventricular Linear Lesion Formation Guided by Noncontact Mapping

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/74679/1/j.1540-8167.2002.00079.x.pd

Why we can’t go back to normal: 5 appeals for a sustainable post-pandemic economy

The pandemic creates much hardship but also shows us how quickly societies can adapt to necessary change, write the members of the University of Hohenheim’s department of innovation economic

Navigating force conflicts: A case study on strategies of transformative research in the current academic system

Against the backdrop of the increasing calls for scholars, universities and the broader academic system to become more societally relevant and contribute to tackling various sustainability challenges, researchers across all disciplines are themselves moving toward conducting more transformative research. Work to date has focused on challenges in these transitions, obstacles to transformative research, and researchers' resistance to ‘impact strategies’; however, little is known about how those who actually do transformative research ultimately overcome these challenges. Using Lewin's field theory as a theoretical basis, we collected qualitative data and carried out 32 in-depth interviews with ‘transformative’ scholars and policy and support staff at Erasmus University Rotterdam (EUR) on the driving and conflicting forces related to transformative research, as well as strategies for dealing with them. An in-depth grounded analysis revealed transformative researchers' identity and goal conflicts and showed how they skillfully navigate those conflicts by choosing between two ideal-typical strategies, ‘transforming through research output’ and ‘transforming through research process’. The constellations of forces identified that actually influence researchers' choices on those strategies need to be taken into account in the designing of effective research policies for leveraging the potential of transformative research to tackle sustainability challenges