How does cultural tightness-looseness affect attitudes toward a local vs foreign brand transgression?
Bibliographic record
Abstract
Purpose This study aims to examine how cultural tightness at the national level and individual level influences consumer attitudes toward a brand's wrongdoing depending on the brand's country of origin and severity of the transgression. Design/methodology/approach Employing data from two tight-culture countries (China and South Korea) and a loose-culture country (the USA), two experiments were conducted to examine the proposed hypotheses. Findings The authors found that although consumers across cultures universally punish strong (vs weak) transgressions more severely, consumers in a tight-culture country, China, are more forgiving of a local (vs foreign) brand in both strong and weak transgression conditions, and forgiveness is higher for the strong transgression. Moreover, this buffering effect observed for Chinese consumers is stronger for those with high personal cultural tightness in the strong transgression condition. However, it emerges only in the weak transgression condition for South Korea, another tight-culture country. As hypothesized, no buffering effect for a local brand was found in a loose-culture country, the USA. Consumers from a loose culture assess transgression severity independently, and the punishment is harsher for strong transgressions than for weak transgressions. Originality/value This study fills a research gap by revealing that consumers from tight (vs loose) cultures would react differently to brands following a transgression depending on the brand's country of origin. It provides implications by examining how national-level and individual-level cultural tightness jointly affect post-transgression attitudes. It also presents a more nuanced perspective that the local brand's buffering effect is contingent on the degree of tightness and severity of transgression, even in similar culturally tight countries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".