The impact of cyber resilience and robustness on supply chain performance: Evidence from the UAE chemical industry
Bibliographic record
Abstract
This paper examines the impact of cyber resilience and supply chain (SC) robustness on supply chain performance in the UAE chemical industry. No prevailing empirical evidence makes this research unique and beneficial to the literature and future research related to cyber resilience in the chemical industry. Moreover, this research is a contemporary contribution to the research of the UAE chemical industry. The study applies a quantitative approach with causal, exploratory and analytical design. The magnitude of the industry is emphasized by choosing cluster sampling techniques. Data is collected from chemical manufacturing companies located in Abu Dhabi, UAE. A valid sample of 303 participants is used for data analysis. A positive direct impact with a significant level of cyber resilience and SC robustness on supply chain performance is found. Current hypothetical model assessment in one industry limits the research findings. It is recommended that other industries be investigated through longitudinal research. A system of diverse detection and defense mechanisms is required. For the chemical industry, an effective cyber security plan would strengthen resilience against cyberattacks and improve SC performance.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".