Supply Chain Collaboration, Innovation, and Sustainability Performance: Evidence from Manufacturing Firms in Jordan
Bibliographic record
Abstract
This study examined the impact of supply chain collaboration (SCC) on supply chain innovation (SCI) and sustainability performance in the context of manufacturing firms in Jordan. The study also investigated the mediating role of SCI in the relationship between different types of SCC and sustainability performance. SCC was represented by three types namely, customer collaboration, supplier collaboration, and internal collaboration. Data were collected using a structured questionnaire that was distributed to employees from numerous management levels in firms located in Jordan as a developing country. A total of 314 valid responses were obtained between December 2024 and March 2025. The data were analyzed using partial least squares structural equation modeling with using the SmartPLS software package. The results of the study revealed that customer, supplier, and internal collaboration significantly enhanced SCI. These three forms of SCC also improved sustainability performance. SCI was found to directly influence sustainability performance, confirming its role as a driver of sustainable outcomes. Moreover, SCI mediated the relationship between internal collaboration and sustainability performance. However, no mediating effects were found between customer or supplier collaboration and sustainability performance. The findings contribute to the resource-based view and dynamic capabilities theory by highlighting collaboration and innovation as critical pathways for achieving sustainable performance. The study offers managerial insights for manufacturing firms in Jordan, emphasizing the importance of strengthening collaboration with customers and suppliers, while also fostering internal innovation to embed sustainability into organizational practices.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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".