Browsing by Person "Mueller, Matthias"

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The role of consumers in business model innovations for a sustainable circular bioeconomy
(2023) Lang, Stephanie; Minnucci, Giulia; Mueller, Matthias; Schlaile, Michael P.
Over the last decade, various governments and supranational bodies have promoted the development of a circular bioeconomy (CBE) as a response to sustainability challenges. The transition towards a CBE requires the collaboration of different actors in the innovation (eco)system. With this conceptual paper, we apply a circular business model lens to address the research question: “What are the archetypical roles of consumers in business model innovations for a sustainable CBE?” We use a combination of complementary theories from the circular economy and bioeconomy literature, evolutionary innovation economics, sustainability transitions research, the business model literature, and the work on active consumers. Considering consumers’ agency as a continuum between the manufacturer-active paradigm and the consumer-active paradigm, we propose: (i) consumers in the manufacturer-active paradigm can actively influence circular business models with their purchase decision; (ii) consumers can act as lobbyists and influencers for circular business model innovation; (iii) in their different roles as customer, user, repairer, and reseller, consumers can incentivize organizations to adapt their business models to their needs; (iv) consumers can become key partners in the process of defining the normative orientation of the innovation paradigm for a CBE; (v) consumers can actively co-create value by means of co-ownership (e.g., through platform cooperatives).
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.