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This is a pre-print of a paper has been accepted for publication in IEEE Transactions on Engineering Management.

The authors would like to thank Carliss Baldwin, Jeffrey Liker, Lawrence Seiford, and Jim Utterback for helpful comments on earlier versions of this paper. All errors and omissions remain our responsibility. We gratefully acknowledge financial support for this research from the National Science Foundation (SES-0620487).

Abstract

Abstract

One fundamental challenge of technology and innovation management is for firms to decide which future technologies to develop in-house vs. buying them outside. This challenge is particularly pertinent when industries emerge, typically a time of high levels of technological uncertainty. It has been long understood that technological uncertainty functions as important stimulus for firms to manage their boundaries. However, two to some extent competing strategy perspectives – governance and competence – predict that firms faced with uncertainty would either increase or decrease their scope of activities, respectively. To reconcile these conflicting positions, we propose a model in which knowledge modularity moderates the effect of technological uncertainty on firms’ R&D scope decisions. We develop new measures for R&D scope, knowledge modularity, and technological uncertainty, drawing on population ecology, network theory, and technology management. We test our model empirically using data on patenting activity and firm boundary location in the emerging automotive airbag industry. Our results generally support our model, and show that in case of knowledge-generating activities such as R&D, scope decisions under technological uncertainty are more driven by concerns about the risk of obsolescence than the risk of opportunistic behavior. We discuss implications for managerial practice and future research.

Managerial relevance

Managers responsible for managing the development of technological innovations must continuously decide on the scope of their firms’ technical development work. This task is particularly challenging in emerging industries that typically exhibit high levels of technological uncertainty. The findings of this research can help technology and innovation managers with this task in two ways, one is conceptual, the other methodological. First, our research results point R&D managers to the value of understanding knowledge modularity for the decision on their firms’ R&D scopes, and explains how and why knowledge modularity affects firms’ responses to technological uncertainty. In regimes of high technological uncertainty firms should focus initially on developing their deep expertise in areas close to what they already know, especially when the knowledge modularity is low. In general, managers can utilize the insights from our research to support their decision making when facing different levels of technological uncertainty and trying to determine a good strategy between buying and developing technological capabilities internally. Second, while knowledge modularity on the industry-level is a theoretical construct, our newly constructed measure for knowledge modularity allows practitioners to actually gauge it through the use of patent data, which are publicly accessible.

Disciplines

Operations Research, Systems Engineering and Industrial Engineering

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