Does the direction of offshoring matter? Comparison of downward and upward offshoring strategies in changing consumers’ brand perception by brand tiers
Bibliographic record
Abstract
Offshoring strategies raise a critical question to defining the country images of brands, as offshoring results in discrepancies between the country-of-brand origin (COB) and the country-of-manufacture (COM) by relocating COM from the home to another country. Based on cognitive dissonance theory and social exchange theory, this study assesses the outcomes of apparel brands’ differing types of offshoring strategies by tracing consumers’ brand perception as these change after the revelation of offshoring information, particularly by comparing those outcomes between an upscale brand and a midmarket brand. Results from an experimental study revealed that a brand originating from a developed country’s offshoring from a developing country can significantly decrease brand credibility and prestige, while a brand originating from an emerging country’s offshoring from a developed country does not significantly improve those brand assets in either the upscale or the midmarket brand contexts. The paper concludes with a discussion of theoretical and managerial implications along with suggestions for future research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 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".