Browsing by Subject "Wissen"

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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.
Standard-setting and knowledge dynamics in innovation clusters
(2009) Christ, Julian P.; Slowak, André P.
Extensive research has been conducted on how firms and regions take advantage of spatially concentrated assets, and also why history matters to regional specialisation patterns. In brief, it seems that innovation clusters as a distinctive regional entity in international business and the geography of innovation are of increasing importance in STI policy, innovation systems and competitiveness studies. Recently, more and more research has contributed to an evolutionary perspective on collaboration in clusters. Nonetheless, the field of cluster or regional innovation systems remains a multidisciplinary field where the sate of the art is determined by the individual perspective (key concepts could, for example, be industrial districts, innovative clusters with reference to OECD, regional knowledge production, milieus & sticky knowledge, regional lock-ins & path dependencies, learning regions or sectoral innovation systems). According to our analysis, the research gap lies in both quantitative. comparative surveys and in-depth concepts of knowledge dynamics and cluster evolution. Therefore this paper emphasises the unchallenged in-depth characteristics of knowledge utilisation within a cluster's collaborative innovation activities. More precisely, it deals with knowledge dynamics in terms of matching different agents' knowledge stocks via knowledge flows, common technology specification (standard-setting), and knowledge spillovers. The means of open innovation and system boundaries for spatially concentrated agents in terms of knowledge opportunities and the capabilities of each agent await clarification. Therefore, our study conceptualises the interplay between firm- and cluster-level activities and externalities for knowledge accumulation but also for the specification of technology. It remains particularly unclear how, why and by whom knowledge is aligned and ascribed to a specific sectoral innovation system. Empirically, this study contributes with several descriptive calculations of indices, e.g. knowledge stocks, GINI coefficients, Herfindahl indices, and Revealed Patent Advantage (RPA). which clearly underline a high spatial concentration of both mechanical engineering and biotechnology within a European NUTS2 sample for the last two decades. Conceptually, our paper matches the geography of innovation literature, innovation system theory, and new ideas related to the economics of standards. Therefore. it sheds light on the interplay between knowledge flows and externalities of cluster-specific populations and the agents' use of such knowledge, which is concentrated in space. We find that knowledge creation and standard-setting are cross-fertilising each other: although the spatial concentration of assets and high-skilled labour provides new opportunities to the firm, each firm's knowledge stocks need to be contextualised. The context in terms of 'use case' and 'knowledge biography' makes technologies (as represented in knowledge stocks) available for collaboration, but also clarifies relevance and ownership, in particular intellectual property concerns. Owing to this approach we propose a conceptualisation which contains both areas with inter- and intra-cluster focus. This proposal additionally concludes that spatial and technological proximity benefits standard-setting in high-tech and low-tech industries in very different ways. More precisely, the versatile tension between knowledge stocks, their evolution. and technical specification & implementation requires the conceptualisation and analysis of a non-linear process of standard-setting. Particularly, the use case of technologies is essential. Related to this approach, clusters strongly support the establishment of technology use cases in embryonic high-tech industries. Low-tech industries in contrast rather depend on approved knowledge stocks, whose dynamics provide better and fast accessible knowledge inputs within low-tech clusters.
United we stand, divided we fall : essays on knowledge and its diffusion in innovation networks
(2019) Bogner, Kristina; Pyka, Andreas
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.