Competitive positioning within and across a strategic group structure: the performance of core, secondary, and solitary firms
Bibliographic record
Abstract
Abstract Drawing from economic and cognitive theories, researchers have argued that firms within an industry tend to cluster together, following similar strategies. Their positioning in strategic groups, in turn, is argued to influence firm actions and firm performance. We extend this research to examine performance implications of competitive positioning not just among but also within groups. We find that performance differences within groups are significantly larger than across groups, suggesting that some firms within groups develop better resource or competitive positions. We also find that secondary firms within a group outperform both core firms within the group and solitary firms, the latter being those not belonging to any multifirm strategic group. This suggests that secondary firms may be able to effectively balance the benefits of strategic distinctiveness with institutional pressures for similarity. We conclude that the primary implication of strategic groups does not relate to the ability of firms to create stable, advantageous market segments through collusion. Instead, strategic groups represent a range of viable strategic positions firms may stake out and use as reference points. Moreover, our results concerning secondary firms indicate that firm positioning within a group structure can have performance implications. Copyright © 2002 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 | 0.001 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".