An empirical examination of the effects of the attributes of supply chain openness on organizational performance
Bibliographic record
Abstract
Purpose The purpose of this paper is to empirically examine how different supply chain attributes as determinants of the openness of supply chain affect organizational performance (OP). Design/methodology/approach Data were collected from 259 supply chain executives in Pakistan. Structural equation modeling was used to test the hypotheses. Findings The findings indicate that organizations may take the selective view of their supply chains resulting in a varying focus on different SC attributes. The results show that though all identified supply chain attributes positively relate to OP, some attributes such as combined agility and cooperation among supply chain partners have a weak correlation coefficient. This indicates that overall the relative openness of supply chain among selected sample of Pakistani organizations is low. Practical implications Supply chain executives may not have a selective focus on some attributes; rather, they may consider to have a broader perspective drawing upon a wider range of supply chain attributes as identified in the current study. In order to remain competitive, Pakistani manufacturing organizations need to learn more about opening up their boundaries and enhance the openness of their supply chain. Originality/value The contribution of the current study is two folds. First, drawing upon the current literature, it proposes the instrument to measure the relative openness of supply chain. Second, it empirically tests the selected conceptual model which highlights the relevance of supply chain attributes and their role in the resulting relative degree of supply chain openness. The empirical examination of the selected conceptual model of supply chain openness tends to make contribution to the wider literature on supply chain management.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".