Browsing by Person "Buchmann, Tobias"

Now showing 1 - 6 of 6

Editorial: Responsible research and innovation as a toolkit: Indicators, application, and context
(2023) Buchmann, Tobias; Dreyer, Marion; Müller, Matthias; Pyka, Andreas
Responsibly shaping technology innovation for the energy transition: an RRI indicator system as a tool
(2023) Buchmann, Tobias; Wolf, Patrick; Müller, Matthias; Dreyer, Marion; Dratsdrummer, Frank; Witzel, Bianca
Efforts to reduce global greenhouse gas emissions have had limited success. For many, the hopes rest on new energy innovations to advance the energy transition process. In this paper, we develop a Responsible Research and Innovation (RRI) base indicator system to steer the design of innovations in the field of energy transition innovations and, thus, improve social acceptance of these innovations. We propose a guideline for its application to assist R&D performing organizations and funding organizations in the design, selection, and communication of research proposals. The indicator system is intended to promote early integration of environmental and social aspects, support the formation of teams aware of the different responsibility aspects of innovation, and monitor progress in regard to relevant RRI dimensions.
Simulating knowledge diffusion in four structurally distinct networks : an agent-based simulation model
(2015) Kudic, Muhamed; Mueller, Matthias; Bogner, Kristina; Buchmann, Tobias
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 network’s 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.
The co-evolution of innovation networks : collaboration between West and East Germany from 1972 to 2014
(2016) Mueller, Matthias; Buchmann, Tobias; Yi, Seung-Kyu; Jun, Bogang
This paper describes the co-evolution of East and West German innovation networks after the German reunification in 1990 by analyzing publication data from 1972 to 2014. This study uses the following four benchmark models to interpret and classify German innovation networks: the random graph model, the small-world model, the Barabási–Albert model, and the evolutionary model. By comparing the network characteristics of empirical networks with the characteristics of these four benchmark models, we can increase our understanding of the particularities of German innovation networks, such as development over time as well as structural changes (i.e., new nodes or increasing/decreasing network density). We first confirm that a structural change in East–West networks occurred in the early 2000s in terms of the number of link between the two. Second, we show that regions with few collaborators dominated the properties of German innovation networks. Lastly, the change in network cliquishness, which reflects the tendency to build cohesive subgroups, and path length, which is a strong indicator of the speed of knowledge transfer in a network, compared with the four benchmark models show that East and West German regions tended to connect to new regions located in their surroundings, instead of entering distant regions. Our findings support the German federal government’s continuous efforts to build networks between East and West German regions.
The evolution of innovation networks : an automotive case study
(2014) Buchmann, Tobias; Pyka, Andreas
Competitive pressure forces firms to continuously develop new ideas, invent new technologies and bring new products to the market in order to survive the destructive part of Schumpeterian innovation competition. This holds particularly for the automotive industry in Germany, challenged by firms from emerging markets which are able to offer their products for lower prices. In the competition for new technological solutions, competences and cutting-edge knowledge are success factors. New knowledge stimulates the emergence of new ideas that can be transformed into innovation. Such knowledge can partly be generated internally in the companies’ R&D laboratories. However, relying on internal knowledge generation is no longer sufficient. Participation in innovation networks which allow for access to external knowledge, and applying innovation cooperation as a strategic tool to acquire necessary knowledge which cannot be developed in-house opens up rich opportunities to complement and recombine the own knowledge-base. Thus, knowledge becomes the most important source of competitive advantage. In this dissertation, I analyze the drivers of innovation networks evolution among a sample of German automotive firms.
The evolution of innovation networks : the case of a German automotive network
(2013) Pyka, Andreas; Buchmann, Tobias
In this paper we outline a conceptual framework for depicting network development patterns of interfirm innovation networks and for analyzing the dynamic evolution of an R&D network in the German automotive industry. We test the drivers of evolutionary change processes of a network which is based on subsidised R&D projects in the 10 year period between 1998 and 2007. For this purpose a stochastic actor-based model is applied to estimate the impact of various drivers of network change. We test hypotheses in the innovation and evolutionary economics framework and show that structural positions of firms as well as actor covariates and dyadic covariates are influential determinants of network evolution.