Country-of-Origin and Brand Image in Global Outsourcing Adjustment
Bibliographic record
Abstract
A global firm may need to adjust its outsourced functions when it competes in the global market place. The outsourcing adjustment will product two effects, brands image effect and the country of origin effect. The paper considers a setting of two global firms, where each has a manufacturing facility in one of the two developing countries but sell their differentiated products in a developed country. The product differentiation is solely based on differences in brand image (BI), country of origin (COO) and their interaction. We demonstrate how firms make location choices in equilibrium as driven by these effects and their inner working relations. We then look at the optimal behaviors of the firms to consider moving, when they receive outside shocks to the demand structure. We show that a firm’s moving decision is not only driven by the COO sensitivity to its own product by the COO sensitivity to its rival’s product as well. Our location choice model based on COO and BI considerations also has strong policy implications for host countries, particularly developing countries which are often times the receiving end of FDI. From the firm’s perspective, the important factors driving the decision to move are the country’s COO value and consumers’ sensitivity towards COO. In that regard, it is in the host government’s interest to maintain and strive for a higher COO value. This is because an adverse incident coming from one exporter or an entity catering to the outsourcing market that tarnishes the COO image tends to have a contagious effect that spreads to other industries.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.011 | 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".