Studies on Time-Lag Effect of Corporate Performance Influenced by Competitive Strategy
Bibliographic record
Abstract
Corporate performance has been analyzed by using quarterly economic data between 1st Quarter of 2003 and 1st Quarter of 2011 shown in the listing companies of the Chinese medical biologicals after implementing the formulated strategy. Studies show competitive strategy influenced corporate performance to some extent brings about time-lag effect and differentiation strategy influenced corporate performance related Duration of Lag inferior to that of Lowcost strategy; at the same time, competitive strategy has a long-term effect on corporate performance, with differentiation strategy influenced Duration of Lag in corporate performance which is more longer than that of Lowcost strategy. Finally, from test results we can also conclude that it is more difficult for implementing differentiation strategy than for Lowcost strategy, even bringing about highly risks and there is little possibility to succeed after being implemented. Key words: Differentiation strategy; Lowcost strategy; Coporate performance; Time-Lag effect
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".