Essays on Knowledge Networks, Scientific Impact and New Knowledge Adoption

Knowledge creation are the crux of economic development. However, an increase in the speed of which knowledge is created has resulted in a knowledge burden in which the time it takes to become specialized in a knowledge field is increasing at an increasing rate. Thus, creators of new knowledge employ new strategies to reduce these costs, specifically through collaborating more. Collaboration entails both opportunities and constraints. Opportunities in the form of an increased influx of new knowledge, or a better division of labor, but constraints in the form of increases in coordination costs and increasing reliance on individual network embeddedness to draw upon resources. A continuously growing stream of literature explores how network positions drive an increase in knowledge network outcomes and impact, which in turn has created an emergent literature of network antecedents. Findings in these streams of literature indicate that certain positions are more likely to be associated with increases in performance and higher likelihood of becoming central in a network.
Thus, it is of increasing importance to understand the interrelatedness of knowledge and networks, individuals and teams, to disentangle the question on how high impact new knowledge is created.
The purpose of this dissertation is therefore to contribute to the stream of literature revolving around how knowledge is created and adopted through the influence of collaboration – the result of which is knowledge networks. More specifically the dissertation explores how both individuals and their direct connections are utilizing their network positions to achieve higher impact, become more central or adopting new knowledge. Further, I explore individual level characteristics that can change the impact of such network positions. The empirical analysis is done through a bibliometric study of all scientists at a Large Scale Research Facility (LSRF) in the US – specifically the Spallation Neutron Source (SNS) at Oak Ridge Tennessee. Through the full bibliometric mapping I gain the insights of the development, production and network aspects of scientific work conducted there, which provides a unique opportunity to gaze into a somewhat isolated knowledge network. I utilize new techniques derived from simulation and machine learning to construct measures of network evolution and knowledge focus at the individual level.