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Cited by 215

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Digital Science and Knowledge Boundaries in Complex Innovation

Dougherty, Deborah, Dunne, Danielle D.·2012·Organization Science

Abstract

Drug discovery is a complex innovation process in which scientists need to make sense of ambiguous findings and grapple with numerous unpredictable interdependencies over many years of product development. Digitalization has combined with expanding science to address this complexity, creating new ways to measure, analyze, and model chemical compounds, diseases, and human biology. We interviewed 85 scientists and managers working on drug discovery to understand how they deal with complexity. We find a major knowledge fault line between digital scientists, who use computers as laboratories and manipulate signs, and therapy scientists, who use conventional laboratories and manipulate physical material. We build on research on epistemic cultures and knowing in practice to develop empirically grounded theory for the role of digital science in complex innovation. We propose that digitalization creates a new form of knowledge that provides essential complementary insights for complex innovation that cannot exist otherwise. However, digitalization also creates new knowledge boundaries that concern central activities of innovation. These boundaries highlight challenges of complex innovation that digital sciences can help address, but only if the innovation activities are transformed so that digital and therapy sciences can integrate their complementary knowledge.

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Metadata

Authors
Dougherty, D., & Dunne, D. D.
Affiliations
  • Department of Management and Global Business, Rutgers Business School, Rutgers University, Newark, New Jersey 07101
  • Schools of Business, Fordham University, New York, New York 10019
Venue
Organization Science
Published
2012-10
Volume
23
Issue
5
Pages
1467-1484
ISSN
  • 1047-7039
  • 1526-5455
Cited by
215
References
6
Language
en

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Dougherty, D., & Dunne, D. D. (2012). Digital Science and Knowledge Boundaries in Complex Innovation. Organization Science. https://doi.org/10.1287/orsc.1110.0700

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

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