A Theory of Market Pioneers, Dynamic Capabilities, and Industry Evolution
Bibliographic record
Abstract
We analyze a model of industry evolution where the number of active submarkets is endogenously determined by pioneering innovation from incumbents and entrants. Incumbent pioneers enjoy an advantage of additional pioneering innovation via a dynamic capability that takes the form of an improved technology for innovation in young submarkets. Entrants are motivated in part by a desire to acquire the dynamic capability. We show that dynamic capabilities increase total innovation, but whether the capability confers an advantage in terms of marginal or average cost is important in determining how the impact of dynamic capabilities is distributed across incumbent and entrant innovation rates. We complement the existing literature—that focuses on exogenous arrival of submarkets or the steady state of a model with constant submarkets—by describing how competition, free entry, and the dynamic capability of incumbents drive the evolution of an industry. The shift from immature to mature submarkets can lead to a shakeout in firm numbers, and it eventually leads to a reduction in total dynamic capabilities in an industry. This paper was accepted by Bruno Cassiman, business strategy.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".