Social relationships channel information, influence, and access to scarce resources. As a consequence, social networks—-the patterns of these relationships across the members of a community—-influence who comes up with important innovations, whether and how rapidly those innovations get adopted, and who has the ability to commercialize them. They therefore also affect the overall rate at which innovation occurs in the economy. This paper provides an introduction to and review of the research on social networks most relevant to innovation, with a particular focus on the earliest stages of the innovation process. It then discusses the likely consequences of a variety of policy interventions that could either reduce the importance of social relationships to innovation or alter the patterns of relationships in ways that might promote innovation.
]]>We propose that the failure to adopt an idea or innovation can arise from an in-group bias among employees within an organizational subunit that leads the subunit’s members to undervalue systematically ideas associated with members of the organization outside their subunit. Such biases in internal selection processes can stymie organizational adaptation and therefore depress the performance of the firm. Analyzing data on innovation proposals inside a large, multinational consumer goods firm, we find that evaluators are biased in favor of ideas submitted by individuals that work in the same division and facility as they do, particularly when they belong to small or high-status subunits.
]]>A large body of work argues that scientific research increases the rate of technological advance, and with it economic growth. The precise mechanism through which science accelerates the rate of invention, however, remains an open question. Conceptualizing invention as a combinatorial search process, this paper argues that science alters inventors’ search processes, by leading them more directly to useful combinations, eliminating fruitless paths of research, and motivating them to continue even in the face of negative feedback. These mechanisms prove most useful when inventors attempt to combine highly coupled components; therefore, the value of scientific research to invention varies systematically across applications. Empirical analyses of patent data support this thesis.
]]>Some companies are better off making incremental improvements to their products. Others that must compete on their ability to innovate focus on breakthrough inventions. Either approach requires the exploration of a specific type of ‘technology landscape’ and the right strategy for searching across the terrain.
MIT Sloan Management Review, Winter 2003: 15-23
]]>By placing a premium on predictability in their product development efforts, companies create a technology landscape that’s easier to navigate–-but one that may produce fewer true breakthroughs.
Harvard Business Review, September 2001: 2-3
]]>This paper develops a theory of invention by drawing on complex adaptive systems theory. We see invention as a process of recombinant search over technology landscapes. This framing suggests that inventors might face a ‘complexity catastrophe’ when they attempt to combine highly interdependent technologies. Our empirical analysis of patent citation rates supports this expectation. Our results also suggest, however, that the process of invention differs in important ways from biological evolution. We discuss the implications of these findings for research on technological evolution, industrial change, and technology strategy.
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