The Path of Most Persistence: An Evolutionary Perspective on Path Dependence and Dynamic Capabilities
Bibliographic record
Abstract
This paper extends the dynamic capability view and research on organizational path dependence by arguing that path dependence can be a property of capabilities when a contingently-triggered capability path is subject to self-reinforcement (i.e. a set of positive and negative mechanisms that increases the attractiveness of a path relative to others). The paper introduces an evolutionary perspective, which specifies the underlying selection mechanisms of the property of path dependence in internal and external firm environments. This theorization sheds new light on three paradoxes that currently blur the theoretical contribution of path dependence to research at the managerial, organizational, and industry levels: (1) the problematic coexistence of path irreversibility and managerial intentionality; (2) the ambivalent strategic value of lock-in with regard to competitive advantage; and (3) the relative homogeneity in observed dynamic capabilities, despite their (possible) path dependence that should lead to a wider variety of outcomes owing to the presence of contingency. We highlight the contributions of this perspective to strategic management research and evolutionary theories.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".