Structure of a firm's knowledge base and the effectiveness of technological search

This dissertation examines the impact of coupling that exists between the knowledge elements of a firm (i.e., the structure of the firm’s knowledge base) on the firm’s technological search activity. I define coupling as the decision made on how the search across two knowledge elements should be combined and distinguish it from interdependence, which is the inherent relationship between these two elements. I ask two questions: 1) How does the structure of a firm’s knowledge base impact the usefulness of a firm’s inventions? 2) How does the prior structure of a firm’s knowledge base affect the malleability of the knowledge base? Malleability is defined as the capacity for adaptive change. Inventions are generated when existing knowledge elements are combined in novel ways. Given the large number of potential combinations of knowledge elements, the problem of searching for technological inventions is computationally intractable. I use the NK model to study this computationally complex problem and argue that the structure of a knowledge base can mitigate the negative effects of complexity. In the first part of the dissertation, I show through computer simulations that a structure that is nearly decomposable (i.e. high coupling within a cluster of elements along with low coupling across clusters) increases the effectiveness of search on an NK landscape. In the second part of the dissertation, I test the relationship between near decomposability in the structure of a knowledge base and technology search in the context of the worldwide semiconductor industry. I find support for the hypothesis that a nearly decomposable structure improves the search for technological inventions. Further, I also find support for the hypothesis that firms with a nearly decomposable structure are likely to undergo a larger change in their knowledge base over time.