Browsing by Subject "Soziale Netzwerkanalyse"

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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.