Does It Pay to Compete Aggressively? Contingent Roles of Internal and External Resources
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We examine, in hypercompetitive environments, why some firms fail to benefit from competitive aggressiveness while others experience superior profits. We explore the relationship between competitive aggressiveness and performance in a sample of 141 firms from three hypercompetitive industries—personal computers, computer-aided software engineering, and semiconductors—from 1995 to 2006. Contrary to the predominant view within competitive dynamics research, we find that competitive aggressiveness is not a universally effective strategy. For some firms, excessive competitive aggressiveness can escalate costs and diminish performance. Using polynomial regression analysis and response surface methodology, we identify the conditions under which competitive aggressiveness enhances firm performance. Our findings reveal that firms benefit from competitive aggressiveness when they have specialized technological resources and support from a dense network of alliance partners.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it