AI adoption as a mediator in early trade defense behavior: Evidence from customs managers in an emerging economy
Bibliographic record
Abstract
Type of the article: Research ArticleAbstractThis study aims to examine the factors influencing early warning behavior in trade defense through the mediating role of the decision to adopt artificial intelligence (AI). Data were collected in the first quarter of 2025 from a survey of 328 managers working in the customs sector in Vietnam. Using partial least squares structural equation modeling (PLS-SEM), the findings reveal that the decision to adopt AI is directly influenced by six factors: perceived usefulness, perceived ease of use, perceived risk, organizational commitment to innovation, technological readiness, and external pressure. These six factors also exert indirect effects on early warning behavior through the mediating role of AI adoption decisions. In contrast, organizational support does not generate a statistically significant moderating effect on the relationship between AI adoption and early warning behavior. The results provide further evidence of the critical role of AI adoption in enhancing effectiveness and efficiency within customs authorities, particularly in strengthening behaviors that safeguard the interests of exporting firms and protect national interests. These findings offer practical implications for emerging economies with conditions similar to Vietnam, where leveraging AI can serve as a strategic tool to improve trade defense mechanisms.AcknowledgmentThe authors would like to thank the Editor-in-Chief and a reviewer for their helpful comments that in our view have helped to improve the quality of the manuscript significantly. Besides, this study is the result of collaboration between researchers from the University of Law, Hue University, and School of Business and Economics, Duy Tan University. The authors would like to thank both institutions for their support and facilitation in the publication of this research.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".