An empirically derived framework of logistics management strategy
Bibliographic record
Abstract
The purpose of this paper is to present an empirically derived framework for Logistics Management and discuss how it integrates organization’s short-term objectives with the need to respond to the complex external environment. Organizational theory, strategic planning and logistics management literature were reviewed carefully in identifying the conceptual support for the derived framework of logistics management and organizational competitiveness. The proposed generalized framework demonstrates that Logistics Management Strategy has the strongest positive effect on Organizational Competitiveness when it is mediated by Logistics Coordination Effectiveness and Customer Service Commitment. Overall Logistics Strategy is a necessary, but not sufficient, condition for increased organizational competitiveness. If the Overall Logistics Strategy is accompanied by (a) effective logistics coordination and (b) customer service commitment then organization competitiveness is likely to be greater. This conceptual study contributes to the field by presenting a generalized framework to improve researcher and practitioner understanding of the role Logistics Management in Organizational Competitiveness. This study integrates previous research and thought domains to develop a generalized framework that guides our understanding of the role of Logistics Management and its consequences on Organizational Competitiveness.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".