Muster der Wissensdiffusion und Wissensrekombination
Zusammenfassung der Projektergebnisse
Although the existing literature has considerably improved our understanding of knowledge recombination and diffusion, the two concepts generally exist in parallel strands of literature and have not yet been meaningfully integrated. As a result, the boundaries between the concepts have not been (sufficiently) clarified. The integration of the two concepts is crucial because knowledge diffusion and knowledge recombination, although different concepts, are mutually dependent. Knowledge must be created (potentially through the recombination of existing knowledge components) before it can be disseminated, and knowledge recombination is impossible if individuals are not aware of the required knowledge components. However, these interdependencies cannot be studied without clear definitional boundaries. The empirical literature also lacks measures to delineate the two concepts; both are still mostly operationalized by patent or publication citation frequencies. A clear conceptual distinction and separate measurement of the two concepts are also important for the successful management of invention and possibly even innovation processes. Only if the separate effects of recombination and diffusion on the value of an invention are clearly identified can the underlying processes be understood and managed. Since existing research does not consider recombination and diffusion separately and does not address the ‘chicken-or-egg’ problem inherent in the two concepts, we can so far only assume the isolated effects that knowledge diffusion and recombination have on innovation. To close these research gaps, the project pursues four objectives: (1) distinguish the concepts of knowledge diffusion and knowledge recombination theoretically and investigate their mutual dependencies; (2) build knowledge diffusion and knowledge recombination measures using patent data and probabilistic topic modeling to trace knowledge diffusion and knowledge recombination patterns in robotics and polymers; (3) build a patent dataset that allows for the analysis of knowledge diffusion and knowledge recombination patterns in robotics and polymers; (4) analyze how knowledge diffusion and knowledge recombination patterns impact the economic value of inventions by comparing these patterns before and after an exogenous shock, i.e., the Fukushima Daiichi nuclear disaster in Japan in March 2011.
