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Record W1511351087

Open innovation : researching a new paradigm

2006· preprint· en· W1511351087 on OpenAlexaboutno aff
Henry Chesbrough, Wim Vanhaverbeke, Joel West

Bibliographic record

VenueRePEc: Research Papers in Economics · 2006
Typepreprint
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Knowledge Management
Canadian institutionsnot available
Fundersnot available
KeywordsOpen innovationIntellectual propertyBusinessCorporationContext (archaeology)Knowledge managementInnovation managementIndustrial organizationUser innovationValue captureMarketingValue creationComputer science
DOInot available

Abstract

fetched live from OpenAlex

Open Innovation describes an emergent model of innovation in which firms draw on research and development that may lie outside their own boundaries. In some cases, such as open source software, this research and development can take place in a non-proprietary manner. Henry Chesbrough and his collaborators investigate this phenomenon, linking the practice of innovation to the established body of innovation research, showing what's new and what's familiar in the process. Offering theoretical explanations for the use (and limits) of open innovation, the book examines the applicability of the concept, implications for the boundaries of firms, the potential of open innovation to prove successful, and implications for intellectual property policies and practices. The book will be key reading for academics, researchers, and graduate students of innovation and technology management. Contributors to this volume - Henry Chesbrough, Executive Director, Center for Open Innovation, Haas School of Business, UC Berkeley, Christensen, Jens Froslev Jens Froslev Christensen, Professor, Management of Innovation, Department of Industrial Dynamics and Strategy, Copenhagen Business School, Myriam Cloodt, post-doctoral researcher in the field of International Business and Strategy, Department of Technology Management, Eindhoven University of Technology, Kira Fabrizio, Assistant Professor, Organization and Management, Goizueta Business School, Emory University, Scott Gallagher, Assistant Professor, James Madison University, Harrisonburg, Virginia, Stuart J.H. Graham, Assistant Professor of Strategic Management, College of Management, Georgia Institute of Technology, Thomas Keil, Assistant Professor of Entrepreneurship and Policy, Schulich School of Business, York University, Toronto, Canada, Markku Maula, Professor of Venture Capital, Institute of Strategy and International Business, Helsinki University of Technology, David Mowery, William A. & Betty H. Hasler Professor of New Enterprise Development, Haas School of Business, UC Berkeley, Gina Colarelli O'Connor, Associate Professor, Lally School of Management and Technology, Rensselaer Polytechnic Institute, and Academic Director of the Radical Innovation Research Program, Jukka-Pekka Salmenkaita, Senior Business Development Manager, Nokia Multimedia, Caroline Simard, researcher, Stanford Project on the Evolution of Nonprofits, Stanford Graduate School of Business, Timothy S. Simcoe, Assistant Professor of Strategic Management, Joseph L. Rotman School of Management, University of Toronto, Wim Vanhaverbeke, Professor of Strategy and Organisation, Hasselt University, Belgium, and Research Fellow, Eindhoven Center for Innovation Studies, Technical University of Eindhoven, the Netherlands, Joel West, Associate Professor of Technology Management, College of Business, San Jose State University.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0050.059
Scholarly communication0.0170.053
Open science0.0030.007
Research integrity0.0060.010
Insufficient payload (model declined to judge)0.0050.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.078
GPT teacher head0.345
Teacher spread0.267 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3,318
Published2006
Admission routes1
Has abstractyes

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