The effects of fast delivery, accidental management and top management on sustainable logistics growth
Bibliographic record
Abstract
The objective of the current study is to examine the effect of fast delivery and accidental management on sustainable logistics growth. The study examined the relationship between fast delivery, accidental management, top management and sustainable logistics growth. Top management is used as a moderating variable. In this study, the Thai logistic companies are investigated, therefore, the population of the study is on logistics companies of Thailand. Data were collected from the employees of logistics companies to examine the effect of fast delivery, accidental management and top management on sustainable logistics growth. A survey was carried out and 450 questionnaires were distributed among the employees. Results of the study show that sustainable logistics growth is the most important for the logistics companies influenced by the fast delivery, accidental management and top management. Fast delivery has a positive effect on sustainable logistics growth. Increase in fast delivery increases the sustainable logistics growth. Moreover, accidental management also has a positive effect on sustainable logistics growth. Better accidental management has a positive role to enhance sustainable logistics growth. Additionally, top management also shows a positive role in sustainable logistics growth.
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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 | 0.007 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".