The impact of artificial intelligence and supply chain collaboration on supply chain resilience: Mediating the effects of information sharing
Bibliographic record
Abstract
The study explores the combined influence of AI technology, supply chain collaboration, and information sharing on supply chain resilience. An integrated study model was developed and tested via smartPLS using a purposive sample of 542 respondents across different industries, to understand how information sharing mediates the collective impact of AI technology and collaborative practices on supply chain resilience. The experimental results demonstrate that AI technology paves the way for timely information and insights generation, which develops collaborative relationships among supply chain partners, facilitates trust and transparency, and thereby, information sharing to exchange pertinent data and insights across the supply chain network. The data were collected by surveying 542 managers from various industries and analyzed using SmartPLS. Relationships among technology adoption, supply chain collaboration, information sharing, and supply chain resilience were investigated. Structural equation modeling (SEM) was employed to observe the direct and mediating effects of information sharing between technology adoption, supply chain collaboration, and supply chain resilience. This study implies that practical information-sharing activities are essential for achieving supply chain resilience amid unpredictability and disturbances in solid market environments. It presents how technology adoption and supply chain collaboration are crucial to supply chain resilience. Information sharing, however, is shown to be an essential mediator, as clear communication and knowledge exchange amongst supply chain partners are fundamentally important in achieving supply chain resilience. Organizations need to invest in AI-driven technologies and develop collaborative ties with their supply chains to be resilient. This paper constitutes a valuable study of the primary drivers of supply chain resilience. It offers implications that organizations can use to persist and grow in the current ambiguous business setting.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".