The impact of cyber resilience and robustness on supply chain performance: Evidence from the UAE chemical industry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it