Bibliographic record
Abstract
Beginning with Bowersox and Daugherty's (1987) influential work describing three unique logistics organizational forms, researchers have generally taken a theoretical typology approach to classifying logistics strategies, and attempts to validate the numerous proposed typologies have produced inconsistent and somewhat conflicting results. In an attempt to add clarity to this stream of research, the current article partially replicates and extends the previous studies using a more rigorous and data‐driven methodology, by developing an empirical taxonomy with firmlevel logistics activities used as clustering criteria. The results identify two primary logistics strategy types used by contemporary firms. The revealed strategies are somewhat parallel to two of the three strategic orientations proposed within the original Bowersox and Daugherty (1987) typology, but also elements suggested by other researchers, as well as new concepts introduced since the original work was published. Based on the results, implications of the revealed logistics strategy taxonomy are provided for managers, and foundations are laid for researchers seeking to undertake further inquiry in the area.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".