Open innovation : researching a new paradigm
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.059 |
| Scholarly communication | 0.017 | 0.053 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".