Bibliographic record
Abstract
Professor Ghemawat argues that commitment is key to gaining a competitive advantage, but can leave a firm vulnerable to rapid and disruptive changes. He and other leading strategy scholars explore the intricacies of developing and sustaining a dynamic competitive advantage. Yet, economists argues that most firms eventually fail to maintain competitiveness. Functionally efficient financial markets, by capitalizing innovative entrants and culling uncompetitive firms, sustain economy-level prosperity. Ghemawat (1991) highlights this tension: firm-level competitiveness can give investors high returns for years, while returns regress towards the mean. Applying this insight to well-documented historical episodes of rapid innovation in various industries, we show that leading U.S. firms in 1920s acquired durable competitive advantages, as did many in the 1960s, but that later entrants often felled early leaders in the 1990s IT boom, consistent with intensified creative destruction. Still, even these shorter-term winners paid well above average cumulative returns. Strategy research that could predict the durability of leading firms’ competitive advantages through an era of rapid innovation would have tremendous value to practitioners in finance.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.032 | 0.010 |
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".