Research Report: Disruptive Technologies—Explaining Entry in Next Generation Information Technology Markets
Bibliographic record
Abstract
The most difficult challenge facing a market leader is maintaining its leading position. This is especially true in information technology and telecommunications industries, where multiple product generations and rapid technological evolution continually test the ability of the incumbent to stay ahead of potential entrants. In these industries, an incumbent often protects its position by launching prematurely to retain its leadership. Entry, however, happens relatively frequently. We identify conditions under which an entrant will launch a next generation product thereby preventing the incumbent from employing a protection strategy. We define a capabilities advantage as the ability to develop and launch a next generation product at a lower cost than a competitor, and a product with a greater market response is one with greater profit flows. Using these definitions, we find that an incumbent with a capabilities advantage in one next generation product can be overtaken by an entrant with a capabilities advantage in another next generation product only if the entrant's capabilities advantage is in a disruptive technology that yields a product with a greater market response. This can occur even though both next generation products are available to both firms. We also show that the competition may require the launching firm to lose money at the margin on the next generation product.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".