Collaboration as a Driver for Supply Chain Resilience: Insights from Emerging Technology Integration
Bibliographic record
Abstract
This research investigates the critical role of collaboration as a driver of supply chain resilience, particularly in the context of emerging technology integration. In an increasingly complex global marketplace, organizations face various disruptions that challenge their operational continuity and efficiency. Through qualitative analysis, the study emphasizes the importance of establishing trust and long-term relationships among supply chain partners, which facilitates open communication and joint problem-solving. The integration of advanced technologies, such as blockchain and the Internet of Things (IoT), significantly enhances information transparency and data-sharing capabilities, enabling organizations to respond more effectively to disruptions. However, barriers to technology adoption, including high implementation costs and data security concerns, pose significant challenges, particularly for smaller firms. The research also highlights the necessity of flexibility and adaptability, with diversified supplier networks and alternative logistics strategies enhancing the capacity to manage risks. Leadership and governance emerge as pivotal factors in fostering collaboration and guiding resilience strategies, with a focus on continuous innovation and sustainability. Looking forward, the study underscores the need for ongoing digital transformation and industry-wide collaboration to build resilient supply chains that can withstand future challenges. Ultimately, the findings provide valuable insights into the multifaceted dynamics of collaboration and resilience in supply chain management, offering a framework for organizations to navigate an evolving business 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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".