Asymmetrical Disciplines of institutions: Differentiation, Newness, and Performance
Bibliographic record
Abstract
Differentiation is one of the most widely adopted and extensively examined strategies, but its performance impact has been characterized by a fundamental polarization. While many strategy scholars have positioned differentiation as a core source of competitive advantage and superior performance, institutional theorists have argued that striving for distinctiveness may end up hampering performance. This study approaches the performance implications of differentiation by postulating an asymmetry in the institutional disciplines toward differentiation between established incumbents and new ventures. New ventures suffer from less discipline and in turn can derive greater performance benefits from differentiation than do established incumbents. And such asymmetry would be more pronounced in a highly contested market or an immature industry, wherein differentiation engenders fewer benefits to counter institutional disciplines. Empirical evidence generally supports our view on the asymmetry of institutional disciplines toward differentiation. Our study develops a novel approach to reconcile the opposing stands on differentiation and offers practical guidelines on how to maximize the benefits from this competitive strategy.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.022 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".