Perfect Timing? Dominant Category, Dominant Design, and the Window of Opportunity for Firm Entry
Bibliographic record
Abstract
Determining the optimal time for firms to enter emerging industries has long been a key concern in strategy, yet scholars still struggle to create a theoretical foundation that can fully integrate the empirical findings within this area. We incorporate categorical dynamics into the theory of the industry life cycle to complement and enhance the predictive power of the existing theories of entry timing. In addition, we introduce the concept of a dominant category — the conceptual schema that most stakeholders adhere to when referring to products that address similar needs and compete for the same market space — and link this concept to the emergence of the dominant technological design. From this framework, we develop a set of propositions that relate the emergence of the dominant category to entry-timing advantages and firm performance. In particular, we propose the existence of a window of opportunity (i.e., an optimal time for firm entry) that starts with the emergence of the dominant category and ends with the emergence of the dominant design. We discuss the implications of categorical dynamics for our understanding of entry-timing advantages and explore the influence of industry contexts in the emergence of the dominant category.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".