Incumbent Incentives in Response to Entry
Bibliographic record
Abstract
How should an incumbent respond to the arrival of an entrant? A long-standing literature documents a host of potential responses, but little work explores when each strategy will be more or less effective. This paper develops a model of incumbent-entrant competition between vertically and horizontally differentiated firms and applies that model to understand the incentives that shape an incumbent’s response to entry and ultimately, long-run profits. Analysis reveals the conditions under which an incumbent facing the full strategy space of possible exogenous entrants has incentive to attack an entrant and conditions where the incumbent has incentive to retreat. By viewing the incumbent and entrant in terms of their level of vertical and horizontal differentiation, this paper offers a unified view of prior work that generates insights about incumbent responses to entry that have been underappreciated. Further, this unified view offers insight on the effectiveness of a particular incumbent response. Supplemental Material: The online appendix is available at https://doi.org/10.1287/stsc.2022.0060 .
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".