Supply chain resilience after the Covid-19 pandemic in Thai industry
Bibliographic record
Abstract
The epidemic of the COVID-19 has spread rapidly worldwide. This phenomenon has changed people's lifestyles as well as business activities. Many businesses are unable to operate normally, which is caused /or affected by supply chain disruption. Therefore, supply chain resilience after the COVID-19 pandemic is essential to maintaining the liquidity of businesses and increasing supply chain efficiency. This research aimed to examine factors influencing supply chain resilience and construct a structural equation model for supply chain resilience after the COVID-19 pandemic in Thailand's industries. A research framework was developed according to previous literature in the context of supply chain resilience. Five constructs, namely Technology, Flexibility, Collaboration, Agility, and Supply chain resilience, with seven hypotheses were established. A questionnaire survey was developed from the research framework and previous literature. Then, the validity and reliability test of the questionnaire were performed with the Index of Item Objective Congruence (IOC) technique and Cronbach’s alpha, respectively. The data was obtained from 426 business organizations in both the industrial and service industry in Thailand. The structural equation model (SEM) technique was conducted to examine the relationship between constructs. The results revealed that agility was only a factor that directly influenced supply chain resilience, while technology had an indirect effect on it via agility. However, technology has had direct effects on flexibility, agility, and collaboration.
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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 | 0.002 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".