Year: 2004
Type: dissertation
Abstract: This dissertation examines the impact of coupling that exists between the knowledge \nelements of a firm (i.e., the structure of the firm’s knowledge base) on the firm’s \ntechnological search activity. I define coupling as the decision made on how the search \nacross two knowledge elements should be combined and distinguish it from \ninterdependence, which is the inherent relationship between these two elements. I ask \ntwo questions: 1) How does the structure of a firm’s knowledge base impact the \nusefulness of a firm’s inventions? 2) How does the prior structure of a firm’s knowledge \nbase affect the malleability of the knowledge base? Malleability is defined as the \ncapacity for adaptive change. \nInventions are generated when existing knowledge elements are combined in novel \nways. Given the large number of potential combinations of knowledge elements, the \nproblem of searching for technological inventions is computationally intractable. I use \nthe NK model to study this computationally complex problem and argue that the \nstructure of a knowledge base can mitigate the negative effects of complexity. In the \nfirst part of the dissertation, I show through computer simulations that a structure that is \nnearly decomposable (i.e. high coupling within a cluster of elements along with low \ncoupling across clusters) increases the effectiveness of search on an NK landscape. In \nthe second part of the dissertation, I test the relationship between near decomposability \nin the structure of a knowledge base and technology search in the context of the \nworldwide semiconductor industry. I find support for the hypothesis that a nearly \ndecomposable structure improves the search for technological inventions. Further, I also \nfind support for the hypothesis that firms with a nearly decomposable structure are \nlikely to undergo a larger change in their knowledge base over time.
Author: Sai Yayavaram
Institution: National University of Singapore
Language: English
Subfield: Strategy and Management
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W2124563547