How do foreign customers' perceptions of product-harm crises affect their transfer of capability- and character-based stigma?
Bibliographic record
Abstract
Purpose Customers often trace a product-harm crisis to the deviant firm's capability- or character-relevant issues. This study examines how capability- and character-based stigma associated with product-harm crises influence foreign customers' product preferences (i.e. brand affect and purchase intention) for other firms from the same country of origin. Design/methodology/approach Qualitative survey data are used to test hypotheses with a structural equation model. Findings The authors find that negative capability judgment significantly affects foreign customers' product preferences for other firms from the same country of origin, whereas negative character judgment does not. However, customers' national animosity and product knowledge moderate the stigma spillover effects. Specifically, national animosity and product knowledge weaken the spillover effects of capability-based stigma but strengthen those of character-based stigma. Research limitations/implications Future research could examine strategies for uninvolved firms to avoid the stigma-by-association effect. Moreover, due to the lack of resources to collect data, this study does not investigate how customers' generalized favorability and familiarity with crisis-stricken firms and uninvolved firms moderate the stigma-by-association effect. Originality/value The findings of this study advance our knowledge on product-harm crises and the stigma-by-association effect.
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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 | 0.007 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".