From purchase to regret: deterring counterfeit consumption through moral emotions and multicultural identity
Bibliographic record
Abstract
Purpose This research examines whether conflicting multicultural identities may influence counterfeit consumption behavior by examining how monocultural and multicultural consumers process shame and guilt differently depending on their cultural identities. It explores how consumers’ moral emotions after buying counterfeit goods can lead to regret and reduce their likelihood of buying such goods in the future. Design/methodology/approach This research employs a multimethod, multi-sample approach with 1,694 respondents across multiple cultures to test our hypotheses. Study 1 is survey research with overseas Chinese consumers and monocultural Chinese consumers, and Study 2 is a randomized block experiment with a European multicultural sample. This design allowed us to test both mediation and moderation hypotheses, validating the effects of shame and guilt on post-purchase regret across diverse multicultural settings. Findings Study 1 shows that cultural identity conflict (CIC) weakens the main effects of shame and guilt on counterfeit post-purchase regret. Study 2 shows that under artificially high levels of shame and guilt, CIC no longer weakens the effects of either shame or guilt on post-purchase regret, further confirming these main effects on counterfeit post-purchase regret. Furthermore, Study 2 demonstrates that, in a natural setting without manipulation, CIC weakens the main effects of shame and guilt on post-purchase regret, further supporting CIC’s moderating effect. Originality/value This study develops a model to examine counterfeit purchasing, going beyond the point of purchase to also consider post-purchase regret and repurchase intentions. It also explores moral emotions and cultural identity factors that can discourage future counterfeit purchases by increasing post-purchase regret. Finally, it investigates how this process may vary between multicultural and monocultural consumers, given their different cultural identities.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".