When standing out pays off: institutional asymmetry between startups and incumbents
Bibliographic record
Abstract
Purpose Differentiation is a widely adopted strategy by both startups and established firms, but its impact on performance varies greatly. This study aims to explore why the outcomes differ by looking at how institutional disciplines treat established incumbents and new ventures differently when they try to stand out. Design/methodology/approach This study argues that new ventures are subject to less discipline and in turn can derive greater performance benefits from differentiation than do established incumbents. Such asymmetry would be more pronounced in a highly contested market or a mature industry, wherein institutional disciplines are more potent. Findings Empirical evidence of this study generally supports the asymmetrical view on the institutional disciplines toward differentiation. New ventures benefit significantly more from the differentiation strategy than established incumbents. And this divergence is contingent upon market competitive intensity and industry maturity. Originality/value This study introduces a novel approach to explain why differentiation works better for some businesses than others by showing how institutions treat new and established ventures differently. It helps managers understand when standing out pays off – being new can actually protect a firm from some of the pressures that typically discourage firms from standing out. The findings challenge the common belief that new ventures are always at a disadvantage. From this insight, practical guidelines are offered for startups on how to turn newness into a strategic advantage when trying to differentiate.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".