Niche width revisited: organizational scope, behavior and performance
Bibliographic record
Abstract
Abstract Although strategy research typically regards firm scope as a positional characteristic associated with performance differences, we propose that broad contemporary scope also provides insight into the routines that govern firm behavior. To attain broad scope, firms must repeatedly explore outside the boundaries of their current niche. Firms with broad niches therefore operate under a set of routines that repeatedly propel them into new market segments, expanding their niche. These niche expansions, however, involve risky organizational changes, behavior that disadvantages generalists relative to specialists, despite the positional value of broad scope. Empirical analyses of machine tool manufacturers and computer workstation manufacturers support this conjecture: (i) generalists introduce new products at a higher than optimal rate, thereby increasing their exit rates; and (ii) generalists also more frequently launch new models with novel features or targeted at new consumer segments rather than improving only incrementally on existing products, further accelerating their odds of failure. After adjusting for these behavioral differences, broad niche widths reduce exit rates, suggesting that they provide positional advantages. The paper discusses how this phenomenon may help to explain the diversification and multi‐nationality discounts. Copyright © 2006 John Wiley & Sons, Ltd.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".