Innovation, Breakthroughs & Disruptive Knowledge: Evidence from Science & Scientists
Bibliographic record
Abstract
Organizations crucially rely on knowledge and innovation. It represents the commercial potential of firms’ research and development (R&D) activities (Katila and Shane 2005) and is thus a source of competitive advantage and profits (Utterback 1994). Further, scientific methods can be applied to processes of creative search within organizations to create new business opportunities (Li et al. 2013; Rosenberg and Nelson 1994). Innovative products may open new markets and drive long-run economic growth (Hasan and Tucci 2010), and novel research may open new paradigms and fields and lead to scientific breakthroughs (Kuhn 1962). Breakthrough inventions create “Schumpeterian rents” (Schumpeter 1939), on which the entry, growth and survival of firms hinge. Yet, innovation is invariably unpredictable (Katila and Chen 2008). Novel products, processes and theories are developed through an inherently complex and ambiguous process. The path to an innovation is a tortuous one, ripe with dead- ends and pitfalls, and the scientific, technical, and commercial promise of an innovation is rarely understood in advance. At the heart of these search paths lies a tension: knowledge is built cumulatively (Merton 1973) and the search for innovation inherently relies on this wealth, yet innovative knowledge breaks with prior work (Hargadon and Sutton 1997; Uzzi et al. 2013a). What is the cartography of those search paths? Does divergence from mainstream knowledge implies low reliance on prior work? How can external audiences evaluating innovation influence these search paths, at times in biased ways? How can innovators find breakthroughs in well-defined but understudied technological spaces? These questions are important to craft strategy processes for managers and resources allocation for policymakers. To answer them, we turn to science (and scientists) as one of the prime search spaces for innovation and breakthroughs. This presenter symposium will assemble four papers on search, innovation, and breakthroughs to further our understanding of these topics. Data-Driven Search and Innovation in Well-Defined Technological Spaces Author: Matteo Tranchero; Haas School of Business, UC Berkeley Greenlighting Innovative Projects: How Evaluation Format Shapes the Perceived Feasibility Author: Jacqueline Lane; Harvard U. If All is Lost: How Negative Social Evaluations May Shape Innovation Author: Paul Bliot; HEC Paris Author: Michael Park; INSEAD Gender Inequality and the Technological Impact of Scientific Ideas Author: Michael A. Bikard; INSEAD Author: Isabel Fernandez-Mateo; London Business School
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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 | 0.012 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".