Determinants of technology adaptation in the supply chains: The case of SMEs in the industrial zone in Vietnam
Bibliographic record
Abstract
This article aims to analyze the different impacts that some factors may exert on the probability that an industrial zone-located firm adapts. Recently, industry policy in developing countries tends to spur both SMEs and the industrial zone in terms of adaptation, considering them as the main driver of innovation and growth. However, not all industrial zone-located firms adapt. Departing from an extensive sample of the Vietnam Technology and Competitiveness Survey in combination with the Vietnam Enterprise Survey in 2011-2013, we try to determine those factors that cause firms to become industrial zone-located adaptation SMEs (IA-SMEs, firms fewer than 250 employees, being located in the industrial zone and adapting existing technologies). The analysis results highlight the importance of direct linkages, technology transfer between FDI firms and industrial zone-located adaptation SMEs, economic obstacles, and the interactions between them that cause industrial zone-located adaptation SMEs to adapt in the supply chain (obtained through direct transfer of technology between linked firms).
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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 | 0.001 | 0.003 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".