A Coal in the Heart: Self-Relevance as a Post-Exit Predictor of Consumer Anti-Brand Actions
Bibliographic record
Abstract
This article extends theory around consumer-brand relationship quality by exploring conditions under which such relationships may be transformed into exceptionally negative dispositions toward once-coveted brands. Survey and experimental results indicate that the more self-relevant a consumer-brand relationship, the more likely are anti-brand retaliatory behaviors after the relationship ends. These anti-brand behaviors are diverse: from complaining to third parties, to negative word of mouth, to illegal actions such as theft, threats, and vandalism. In contrast, post-exit consumer-brand relationships that were low in self-relevance but were high in trust, commitment, and satisfaction are less likely to result in anti-brand actions. The role of a discrete product or service failure is also explored, and results suggest that self-relevance may motivate retaliation even in the absence of a so-called critical incident. Ultimately, this research illuminates previously unexplored mechanisms—including self-conscious emotional reactions—that motivate consumer hostility and retaliation.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".