Bibliographic record
Abstract
Purpose – The author identified a set of companies that have figured out how to cope, even to thrive, within the new transient advantage economy and explains how they did it. Design/methodology/approach – Her research team analyzed nearly 5,000 companies. Of that whole population, only ten companies were able to grow their net income by at least 5 percent a year for ten years in a row. These ten “outliers” have out-performed competitors while adapting to rapidly changing market forces. Findings – Organizations that have mastered transient-advantage environments have learned to continually free up resources from old advantages in order to fund the development of new ones. Additionally, innovation is continuous, mainstream and part of everyone's job. Research limitations/implications – The 5,000 companies analyzed included every publicly traded firm on any stock exchange with a market capitalization of over $1 billion. The article studies the practices of the ten most successful over ten years. Practical implications – The most successful firms, over the entire study period, had no dramatic downsizings, restructurings or sell-offs. Originality/value – The author found that the key leadership and management challenge is maintaining an organizational system that can manage the complementary forces of innovation and stability.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".