Exploring the relationship between supply chain collaboration, risk management strategies, and supplier development on supply chain resilience: The mediating role of trust
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study investigates the relationships between Supply Chain Collaboration, Risk Management Strategies, Supplier Development, Trust, and Supply Chain Resilience in the Jordanian retail sector. This research aims to analyze the direct impacts of these Supply chain management practices on resilience, as well as mediating effect Trust in between this relation. The study used quantitative data to collect the data from 291 managers and executives in the Jordanian retail sector. The findings suggest that Supply Chain Collaboration and Risk Management Strategies have positive significant effects on Supply Chain Resilience, while Supplier Development has no direct significant effect. The results show that each of the supply chain management practices has significant positive effects on Trust and, therefore, significantly contribute to increasing Supply Chain Resilience. In addition, Trust is identified to play a mediating role in the relationships between trust on both SCMPs and resilience. This paper extends the existing literature on supply chain resilience and provides empirical insights into significant factors that shape in enhancing resilience within the retail trade industry, notably focusing on Jordan. The implications for managers, practitioners and society suggest that collaborative relationships, effective risk management strategies as well trust building initiatives are vital to increase resilience among supply chains. This research is unique as it thoroughly investigates the collaboration between SCM practices, relational variables and resilience in an understudied context providing a new understanding of the interplay of different antecedents at play on resilient supply chains creation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it