From a rugged to a smooth supply chain performance landscape: a complementarity perspective
Bibliographic record
Abstract
We draw on complementarity and performance landscape perspectives to reason why and how supply chains should shift from a rugged to a smooth performance landscape. We analysed the organisation of supply chain-oriented firms by conceptualising them as a set of interdependent elements whose complementarity interaction generates desirable performance outcomes. We collected perceptual data from 139 firms. After establishing the psychometric properties of the measures, we employed two econometric methods that enabled us to examine the complementary interaction using performance differences among five SCM practices. Overall, we find empirical evidence for complementarity among the SCM practices. We also find interesting results from the two econometric approaches allowing us to articulate the distinction between practice contextuality and interaction contextuality. Our study offers empirical evidence for supply chain managers to find a promising position in the rugged supply chain performance landscape. In addition, we offer noteworthy managerial insights on managing the supply chain towards a smoother supply chain performance landscape.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".