Supply chain risks in the age of big data and artificial intelligence: The role of risk alert tools and managerial apprehensions
Bibliographic record
Abstract
As supply networks become more complex and international, the task of controlling associated risks becomes more difficult. This article investigates the usefulness of risk alert technologies in supply chain management, with a focus on Big Data Analytics (BDA) and Artificial Intelligence (AI). The study investigates the impact of BDA capabilities, solid IT infrastructure, managerial views, and AI-apprehensions on the effectiveness of risk alert tools using a questionnaire-based survey of 420 managerial personnel and Structural Equation Modeling (SEM) via SMART PLS. The work proposes the concept of AI-Apprehensions as a moderating variable, which is a relatively unexplored field. According to the findings, while BDA capabilities and IT infrastructure considerably improve the effectiveness of risk alert tools, AI-apprehensions can negate these advantages. The study provides useful insights for policymakers and practitioners, emphasizing the importance of balancing technical and human components for effective 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 | 0.019 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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 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".