The effect of information security on e-supply chain in the UAE logistics and distribution industry
Bibliographic record
Abstract
The effect of information security on the e-supply chain attracted attention to investigate its relationship with the mediating role of supply chain risk in the logistics and distribution industry in the United Arab Emirates (UAE). The proposed research explored the mediating effect of supply chain risk in the logistics and distribution industry, providing unique insights for future research, literature, and targeted sectors. A descriptive, causal and analytical design with quantitative research technique was applied to the proposed research model. A sample of 301 respondents from the managerial departments of 176 logistics and distribution companies in Dubai and Abu Dhabi was used to assess the research variables. The findings revealed that the impact of information systems was positively associated with the e-supply chain, while the indirect impact of supply chain risk significantly positively impacted the e-supply chain. The research is limited to assessing the supply chain risk as an intermediary. For future research, exploring the SC risk prevention strategies' impact on the E-supply chain is recommended. Research findings are anticipated to assist communities of practice in making better information security decisions in the context of e-supply chain by clearly implementing information security policies internally and externally to enhance e-supply chain performance and SC risk management.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.005 | 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".