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The present paper analyzes development patterns in research networks and technology as well as their co-evolution by the example of the currently highly transformative German automotive industry. We therefore introduce a consistent network-based approach for measuring change in research networks and the technological composition of patents. Our results show that the applied method is particularly useful for identifying structural similarities and main structural changes in research and patent networks. Further, the investigation of the co-evolutionary processes indicates that, regarding the German automotive industry, changes in technology lead to structural changes in the research network.

Network dynamics, economic transformation, and policy design are closely related phenomena that influence the performance of economic systems in a variety of ways. In this introductory paper, we set the stage for a series of excellent contributions addressing some still largely unexplored questions in this research field. At the core of our introduction, we provide a contextual structuration and classification of the contributions to this special issue. Finally, we address some contemporary issues that deserve some attention since they open up highly interesting opportunities for future research.

ZusammenfassungDieses Kapitel zieht Patente als Indikator für den FuE-Output heran. Sie messen den Erfolg des FuE-Prozesses und sind daher sowohl quantitativ als auch qualitativ interpretierbar. Allerdings sind sie aufgrund der zwingenden wirtschaftlichen Verwertbarkeit eher für den Unternehmenssektor relevant. Hochschulen und Forschungsinstitute melden zwar in Einzelfällen auch Patente an, allerdings ist hier der Anteil der noch nicht ökonomisch verwertbaren Grundlagenforschung sehr viel höher als bei den Unternehmen. In Form von Steckbriefen werden die wichtigsten Eigenschaften und Anwendungsgebiete der Indikatoren beschrieben.

ZusammenfassungIn diesem Kapitel wird die Wirkung, die eine Publikation in der wissenschaftlichen Community erzielt (Impact), als Indikator genutzt. Ein Anhaltspunkt dafür ist die Anzahl der Publikationen, in denen die betroffene Veröffentlichung zitiert wird. Dabei ist ein Zitat nicht nur ein Indikator dafür, dass eine Publikation wahrgenommen wurde, sondern auch dafür, dass ein anderer Wissenschaftler oder eine andere Wissenschaftlerin sie für wichtig genug erachtet hat, noch einmal darauf einzugehen. Prinzipiell stellt ein Zitat also erst einmal ein Qualitätsmerkmal dar. In Form von Steckbriefen werden die wichtigsten Eigenschaften und Anwendungsgebiete der Indikatoren beschrieben.

ZusammenfassungIn diesem Kapitel werden die FuE-Ausgaben bzw. FuE-Aufwendungen als Indikatoren der FuE-Erhebung und der Innovationserhebung genutzt. Vorteile monetärer Indikatoren sind (1) Anwendbarkeit in allen Sektoren, (2) vollständige und überschneidungsfreie Erfassung aller FuE-Aktivitäten einschließlich der erfolglosen Forschung, (3) Aggregierbarkeit und (4) Kompatibilität mit anderen Erhebungen. In Form von Steckbriefen werden die wichtigsten Eigenschaften und Anwendungsgebiete der Indikatoren beschrieben.

ZusammenfassungEine Möglichkeit, Forschung und Entwicklung sowie Innovation zu messen, stellen die offiziellen Statistiken zu FuE und Innovation dar. In diesem Kapitel werden die Vorgehensweisen beider Erhebungen kurz aufgezeigt.

ZusammenfassungDieses Kapitel vollzieht einen Perspektivwechsel. Wurden FuE- und Innovationsindikatoren bisher vor allem als Blick von außen auf die entsprechenden Einheiten verwendet, steht hier der Innenblick im Mittelpunkt. Entsprechende Indikatoren können auch für die interne Steuerung von FuE verwendet werden. Auch wenn Projektsteuerung für jeden Sektor wichtig ist, stehen in diesem Kapitel vor allem die Unternehmen im Mittelpunkt. Die Fragen, die sich Unternehmen stellen, werden durch die Indikatoren abgebildet. In Form von Steckbriefen werden die wichtigsten Eigenschaften und Anwendungsgebiete der Indikatoren beschrieben.

ZusammenfassungIn diesem Kapitel werden die Drittelmittelzuweisung und die Drittmittelherkunft als Indikatoren genutzt und es wird auf Probleme hingewiesen, die damit einhergehen können. In Form von Steckbriefen werden die wichtigsten Eigenschaften und Anwendungsgebiete der Indikatoren beschrieben.

ZusammenfassungNicht nur unterschiedliche Interessenlagen bezüglich einer Messung von FuE und Innovation geben einen kleinen Vorgeschmack auf die Komplexität des Themas. Auch die Fragestellungen können extrem unterschiedlich sein. Dieses Kapitel beschreibt Interessenlagen und Fragestellungen und führt in das Thema ein.

ZusammenfassungDieses Kapitel behandelt FuE-Personal als Indikator der FuE-Statistik und beschreibt die Vorteile gegenüber den FuE-Aufwendungen. In Form von Steckbriefen werden die wichtigsten Eigenschaften und Anwendungsgebiete der Indikatoren beschrieben.

ZusammenfassungAufgrund der Komplexität des Themas bietet es sich an, eine multidimensionale Betrachtung durchzuführen und verschiedene Kriterien zu berücksichtigen. Deshalb werden in diesem Kapitel Indikatorensets betrachtet und deren wichtigste Eigenschaften und Anwendungsgebiete in Form von Steckbriefen beschrieben.

ZusammenfassungIn diesem Kapitel wird die Frage behandelt, welche Einflussfaktoren ein Unternehmen dazu bewegen, Forschung zu betreiben oder eben auch nicht In Form von Steckbriefen werden die wichtigsten Eigenschaften beschrieben, wobei die Steckbriefe die Ansätze des Frascati- und des Oslo-Handbuchs aufgreifen.

ZusammenfassungIn diesem Kapitel wird das Zählen von Veröffentlichungen in unterschiedlichen Formen als Indikator für die Quantität wissenschaftlicher Leistungen herangezogen. In Form von Steckbriefen werden die wichtigsten Eigenschaften und Anwendungsgebiete der Indikatoren beschrieben.

Die Daten der Erhebung zu Forschung und Entwicklung sind gemas den Vorgaben des Frascati-Handbuchs und der EU-Verordnung 995/2012 nach Branchen strukturiert. Viele Masnahmen der Forschungs- und Forschungsforderpolitik orientieren sich aber nach Technologien, also nach einem Querschnitt verschiedener Branchen. Zu dieser Betrachtung liegen aber nur wenige statistische Informationen vor. Die vorliegende Arbeit versucht daher auf der Basis der FuE-Erhebung 2015 eine Neuauswertung der Daten gemas den Technologien/Forschungsfeldern der Leistungsplansystematik des Bundes vorzunehmen.

Although policy makers subsidize firms’ R&D projects to increase innovational output in an industry, region, or country, it remains unclear whether such subsidization triggers additional R&D efforts or crowds out private investments. To address this question, we assess the effectiveness of subsidization for individual and collaborative research in the German biotech industry while also examining the relevance of network embeddedness for patent output. Our results indicate that subsidies do lead to increased patent output, with additional benefits from involvement in more than one project. However, the amount of money is only significant under certain circumstances. Also, it is higher degree centrality in the firm network that significantly increases R&D success.

Although lagging behind the USA in biologics, German firms are forging ahead in the emerging field of biosimilars. This development confirms earlier explanations of the differences in these industries given by the Varieties of Capitalism approach. With the recent differentiation in biopharmaceuticals, again, the differences in the institutional environment on both sides of the Atlantic are responsible for different dynamics. However, this time German companies are in the better position: the industry refers to a tradition of engineering capabilities in chemicals and pharmaceuticals. Additionally, European regulation introduced the world’s first regulatory framework, which moreover is designed in a progressive way concerning market admission.

We apply an agent-based simulation approach to explore how and why typical network characteristics affect overall knowledge diffusion properties. To accomplish this task, we employ an agent-based simulation approach (ABM) which is based on a “barter trade” knowledge diffusion process. Our findings indicate that the overall degree distribution significantly affects a network’s knowledge diffusion performance. Nodes with a below-average number of links prove to be one of the bottlenecks for an efficient transmission of knowledge throughout the analysed networks. This indicates that diffusion-inhibiting overall network structures are the result of the myopic linking strategies of the actors at the micro level. Finally, we implement policy experiments in our simulation environment in order to analyse consequences of selected policy interventions. This complements previous research knowledge on diffusion processes in innovation networks.

Im Jahr 1982 erschien das zwischenzeitlich weltweit am haufigsten zitierte wirtschaftswissenschaftliche Werk von Richard Nelson und Sidney Winter: An Evolutionary Theory of Economic Change und bereitete das Feld fur die mittlerweile prosperierende Evolutionsokonomik bzw. Schumpeterianische Okonomik (vgl. Hanusch/Pyka, 2006a, 2007a).

In this chapter we study the diffusion of automotive knowledge created in the Region of Stuttgart. Patents leave a paper trail in the form of citations that we analyze in order to show which regions learn from e-mobility and fuel cell knowledge created in the region of Stuttgart. We show that citations of the patents of both technologies tend to localize in Germany, Japan and the US. However, in case of e-mobility, while aggregating the countries by groups, the largest number of citations is made by European, more precisely Western European countries. The fact, that domestic knowledge flows in fuel cell technology are not the most intensive can be explained by the early stage of the technology and that there is no hard fundament, on which the new knowledge can be built.

Erratum: AStA Wirtsch Sozialstat Arch 2018 https://doi.org/10.1007/s11943-018-0226-z In der Originalveroffentlichung wurde der Name der Autorin Katharina Friz versehentlich falsch geschrieben. Wir bitten, dieses Versehen zu entschuldigen. Der Originalbeitrag wurde nachtraglich …

Das Kopernikus-Projekt ENavi erarbeitet interdisziplinäres Systemwissen für die Energiewende. Im Arbeitspaket Technische Entwicklung im Systemkontext untersucht das Team innovative technische Lösungen.

Neo-Schumpeterian scholars suggest that we focus on the analysis of the knowledge-bases of firms and their role for entrepreneurial activities and managerial decisions if we want to understand patterns of industry change. Thus, the knowledge-based view of the firm paves the ground for the analysis of innovation network evolution. Evolutionary theory is very rich and offers a broad variety of concepts which have the potential for being applied in an (innovation) economic framework. Competitive pressure forces firms to continuously develop new ideas, invent new technologies and bring new products to the market in order to prevail on the field of creative destruction.

In this chapter, I first describe characteristics and challenges of German automotive suppliers and manufactures followed by an analysis of the structure of their collective knowledge-base, focusing on e-mobility technologies. Second, I introduce a model for the analysis of evolutionary change patterns of an interfirm innovation network (Pyka and Fagiolo, 2005; Pyka and Hanusch, 2006) which consists of a sample of German automotive firms. A stochastic actor-based model is applied to estimate parameters which reflect the impact of hypothesized effects.

“Classic” cross-sectional network analysis, as presented in chapter 5, captures the development state of a network at a certain point in time, i.e. it takes a snapshot of the status quo. This allows for studying the structural characteristics of the network and the positioning and embeddedness of individual actors. There is meanwhile a large body of established literature which applies this type of analysis, but there are also shortcomings with this approach. Even though we may conclude from SNA in combination with a correlation analysis that a certain network related correlation exists, the direction of causality is often very ambiguous.

Schumpeter (1911) puts innovation in the center of his theory of economic development. In the beginning, he linked innovative success purely to the entrepreneurial success of outstanding individuals in an economy. Thirty years later, Schumpeter (1942) – inspired by the development of US industries – identified a significant change in the organization of R&D processes in specialized R&D laboratories of large firms (routinized innovation). And another forty years later, again a significant change had taken place in the organization of R&D.

Evolutionary thinking in a network context emerged only recently on the research agenda. For instance, Gluckler (2007) addresses the question how tie selection constitutes an evolutionary process in networks. More precisely, he argues that network tie selection processes cause retention and variation within network structures. Hite (2008) presents an evolutionary multi-dimensional model of network change that explicitly considers micro level network change processes. Witt (2006) argues that selection processes are, in line with Neo-Schumpeterian approaches, constitutive for evolutionary economics. Studies on the evolution of network structures have typically focused on external factors (e.g. competitive pressure) as drivers for change processes.

Differing positions in a network are linked to operational opportunities or limitations, especially with regard to access to knowledge. It is not only advantageous to be a member of a network but it is also important to find the right place in the network, most of the time preferably a central position that allows for absorbing the latest developments. Furthermore, evolutionary dynamics is not a phenomenon which is limited to firms or technologies, but also networks change their structure over time. Consequently, the position of actors in the network changes.

We focus on the evolution of interfirm innovation networks which are composed of and driven by heterogeneous actors that follow a number of well-defined cooperation partner selection strategies. For this purpose, we simulate micro level firm behaviour which shapes the macro level network patterns. In order to analyse the micro-macro link we build an agent-based simulation model (ABSM) which allows us to test causal relationships between firm strategies and the emerging network structures. With this model we analyse the structural consequences of homophily, reputation and cohesion mechanisms in a situation of information scarcity. We start with a simple model which is in a next step extended by adding a market mechanism which links the knowledge base of a firm with the reward a firm receives and with the incentives to cooperate. We show that (a) a transitive closure mechanism combined with a tendency for preferential attachment produces networks that show both, small world characteristics as well as a power law degree distribution; (b) diversity in the selection of cooperation partners is an important determinant of innovative performance if we consider an evolving network.

We engineer policies to stimulate technology development in a manufacturing industry using an agent-based model of value networks within a multi-region industry. Generally, industrial production is organized in value networks, where firms in upstream tiers supply components to firms in downstream tiers. We postulate that ties are increasingly intraregional with incremental innovation typical for the mature stage of the industry life-cycle, while ties are increasingly interregional to accommodate experimental recombination and discontinuous innovation in the decline and inception stages of the industry life-cycle. Policy instruments should create conditions favorable for regional performance in either of the two distinct industry life-cycle phases. We present an agent-based model based on Pyka, Gilbert & Ahrweiler (2007) in which agents decentrally form multi-tier production networks that produce products for the end-consumer market. Production knowledge is modeled as a capability taxonomy with an expandable set of abilities per capability class. These abilities are the production recipes to transform input products acquired at upstream tiers into output products sold at downstream tiers. Agents continuously experiment with their abilities to improve the product they produce, but the increase in performance of products gradually tapers off. In case agents repeatedly discover inferior products, they look for new combinations of capabilities, both within and outside the set of their current value network partners. This radical capability experimentation may yield new capabilities, thereby new possible abilities, hence possibly new (end-)products that can be produced. Firms either collaborate with other firms, collaborate with a research institute, or vertically integrate to access capabilities that may or may not 'unlock' new capabilities. We introduce a spatial limit to both the production network partnership (and thereby ability hill-climbing) and the innovation network partnership (and thereby capability discovery search). To understand which technology search heuristics are to be stimulated and what research infrastructure is to be put in place, we study technological progress under different distributions of capabilities (over regions, over public pools, and over firms in the value networks) and different (spatial characteristics of) search heuristics used by the agents. Finally, we initialize our agent-based model to reflect the particular cases of the biotechnology industry in Vienna and automotive industry in the Stuttgart region. Using the empirical distribution of capabilities over agents and regions, we study technological progress in both industries for the different scenarios for the (spatial characteristics of) search heuristics and for different research infrastructures for capability pooling.