Bibliographic record
Abstract
Because of rapid technology and market changes, high tech firms often think it isn’t a good idea to be attuned to markets, but rather they need only to keep their eye on the technology itself. This survey of telecommunications firms reveals the importance of a market orientation extends to these firms and is a consistent predictor of performance. Technology intensive firms have consistently argued that managing these firms is different from managing in other business sectors. Because of rapidly changing technologies and markets, management must focus on the technologies and their development over attention to market needs. In fact, too much market responsiveness will doom firms to losing their technology lead. While many engineers-cum managers who are at the helm of technology intensive firms and are often the inventor-entrepreneur would like to believe that it is their inventive genius that will win markets (“If I build it, they will come”), there is lots of evidence that even technologically superior ideas can lose out to better marketed ones (the classic –beta vs. VCR). On the other hand, Christensen and Raynor (2003) point out that focusing on current markets can relegate market winners to market losers because they stake their claim on current customers while competitors find new technologies and new markets as a breeding ground for
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.772 | 0.505 |
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".