Supply chain resilience and operational performance amid COVID-19 supply chain interruptions: Evidence from South Korean manufacturers
Bibliographic record
Abstract
South Korean manufacturers have dealt with supply chain interruptions from the COVID-19 pandemic as many other manufacturers have around the world; however, it seems that some Korean manufacturers are remarkably resilient. Many Korean manufacturers have withstood perilous supply chain dynamism and maintained operational performance without interruption. This study examines the antecedents of supply chain resilience and operational performance to suggest how other manufacturers can develop and maintain continuous uninterrupted operations even amid dynamic supply chains and global disruption. This is an empirical study of South Korean manufacturers utilizing PLS-SEM analysis with mediation effects. This study examines the interrelationships of six variables including, supply chain disruption orientation, management’s intention, digital infrastructure capability, innovation adoption, supply chain resilience, and operational performance. The implications are meaningful for both scholars and practitioners alike. This paper contributes to literature pertaining to both supply chain management and technology assimilation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".