Churn, Baby, Churn: Strategic Dynamics Among Dominant and Fringe Firms in a Segmented Industry
Bibliographic record
Abstract
This paper integrates and extends the literatures on industry evolution and dominant firms to develop a dynamic theory of dominant and fringe competitive interaction in a segmented industry. It argues that a dominant firm, seeing contraction of growth in its current segment(s), enters new segments in which it can exploit its technological strengths, but that are sufficiently distant to avoid cannibalization. The dominant firm acts as a low-cost Stackelberg leader, driving down prices and triggering a sales takeoff in the new segment. We identify a “churn” effect associated with dominant firm entry: fringe firms that precede the dominant firm into the segment tend to exit the segment, while new fringe firms enter, causing a net increase in the number of firms in the segment. As the segment matures and sales decline in the segment, the process repeats itself. We examine the predictions of the theory with a study of price, quantity, entry, and exit across 24 product classes in the desktop laser printer industry from 1984 to 1996. Using descriptive statistics, hazard rate models, and panel data methods, we find empirical support for the theoretical predictions.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".