Back-shoring or re-shoring: determinants of manufacturing offshoring from emerging to least developing countries (LDCs)
Bibliographic record
Abstract
Offshore outsourcing is mainly the flow of tasks from developed to emerging country firms in search of low cost production facilities. Many of these firms are now shifting their outsourcing activities from emerging to least developing countries. This paper shed light on determinants of firms based in emerging countries’ decision on shifting their outsourcing to least developing countries and to what extent it differs from developed country firm’s offshoring to emerging countries. Survey based data collected from offshoring client firms based first in South Korea and Taiwan and then engaged in re-shoring their outsourcing activities to Bangladesh and data was analyzed by multiple regression analysis. The current study found that offshoring firms enter into re-shoring to least developing countries to avail cost advantages; to have access to supplier capabilities and to focus more on strategic activities as well as to reap advantages from the institutional policy oriented advantages available in least developed countries. The findings revealed that several production factors have effects on firm’s re-shoring decision to LDCs. Transferring offshoring to LDCs and exporting from there to the developed country markets, the offshoring creates the global production network (GPN) integrating developing, emerging and developed countries.
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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 | 0.001 | 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".