The contribution of balanced scorecard perspectives for improving supply chain performance: A PLS-SEM approach
Bibliographic record
Abstract
The main objective of this article is to provide a theoretical and conceptual framework encompassing concepts such as the supply chain, and supply chain performance. Furthermore, it aims to examine empirical research on the relationship between the perspectives of the Balanced Scorecard and supply chain performance. Finally, the article intends to present the findings and engage in discussions. To achieve this, a positivist epistemological approach was adopted, employing a quantitative methodology. A sample of 85 companies from the automotive industry in Morocco was selected for this study. After data collection, the structural equation method was employed using Smart PLS 3 software to test and confirm the hypotheses as well as the research model proposed. In conclusion, the results of this study highlight the positive impact of the four perspectives of the Balanced Scorecard on supply chain performance within automotive industry companies in the northern region of Morocco.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".