The role of strategic agility towards the firm performance of logistics service providers in Indonesia
Bibliographic record
Abstract
Competition in various companies encourages practitioners, entrepreneurs, and academics to examine the dynamization of business models. The dynamic study of this business model was driven by its dynamic environment. Dynamic moving environments require companies to be adaptive as quickly as possible. This adaptation encourages different firms to perform a certain strategy of excellence in the face of competition. Strategy Agility becomes a study of practitioners and academics due to its ability to predict and capture opportunities. One of the service industries faced with the dynamic environment within the company is the Logistic Service Provider. Fierce competition, the dynamic type of service, and rapidly changing technol-ogy encourage enterprises to always be agile in determining the direction to the business. The purpose of this research is to get a model of the relationship between strategic agility and competitive strategy as a moderator for the improvement of the firm performance. Methods used are quantitative methods. The research results describe the direct relationship between strategic agility and competitive strategy to the firm performance. However, when the competitive strategy is made as a moderator, strategic agility shows a negative effect.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".