Bibliographic record
Abstract
Most managers are well versed in the defensive playbook for confronting disruptive innovation. Most commonly, they either acquire the new entrants or “disrupt themselves” by setting up autonomous units charged with developing their own new technology that can be rolled into their principal operations once the disruptive innovation begins to dominate the industry. But quite often, adopting a new technology requires companies to fundamentally change their mainstream operations—the way they manufacture and distribute their products. In these cases where the organizational model changes along with customer expectations and preferences, the playbook often falls short. In this article Joshua Gans of the University of Toronto’s Rotman School of Management identifies three prescriptions for surviving “supply side” disruption: Companies must have an integrated organizational model, ownership of a product feature important to the end customer, and a broad and flexible sense of corporate identity. Though less commonly understood, supply-side disruption is arguably more dangerous than the kind described by Clayton Christensen in The Innovator’s Dilemma; indeed, disruption of a product’s architecture threatens a company’s very survival in a way that changes in customer demands do not. INSETS: How It's Made Matters.;A New Narrative.
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 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.036 | 0.007 |
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".