International Market Selection Based on Integrated MCDM Methods: A Case Study of Iron and Steel Sector
Bibliographic record
Abstract
International Market Selection (IMS) is crucial for companies seeking growth and expansion beyond their domestic boundaries. IMS is a strategic decision that requires careful evaluation of several factors to minimize risks and achieve long-term success in international markets. Accordingly, this study aims to find out optimal market alternatives for exporter companies operated in iron and steel sector. A total of twenty-three market alternatives were evaluated based on ten criteria. The alternatives and criteria were determined by literature review and expert opinions. The weight of criteria was calculated by the FUCOM and LOPCOW methods. Once the weight of criteria was determined, the alternatives were ranked using the SPOTIS, RSMVC, CoCoSo and Borda Count methods. The results showed that balance of trade and GDP were the most and least important criteria, respectively. Overall results revealed that Canada, the UAE, Germany, Japan and Malaysia were the best market alternatives, while Venezuela, Mexico, Peru, Colombia and the UK were the worst market alternatives for iron and steel exporter companies. Additionally, the sensitivity analysis was carried out to observe the robustness of the results.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".