Bibliographic record
Abstract
Purpose Consumer–brand relationship literature has seen a recent surge of work on the concept of brand hate. Considering that hate is not easily acknowledged, it is challenging to uncover the psychological mechanisms that underpin its development. Using the concept of “self” as over-arching theory, this study aims to uncover consumers’ psychological triggers for hating a brand by providing contextualized perspectives from the informants. Design/methodology/approach The authors use an interpretive approach focused on exploring the role of self in explaining the development of brand hate. Data is collected through 25 in-depth interviews and inductively analysed using the NVivo 12. Findings The findings of the study align with the motivational perspective of hate discussed in psychology literature. Six psychological strategies (coping, moral consciousness, ego defense, self-esteem protection, power reinstatement, and self-concept strengthening) cater to three motives of the self (self-preservation, self-defense, and self-enhancement). Originality/value The current study uses an interdisciplinary approach and draws perspectives from psychology, sociology and interpersonal relationship theories to study consumer brand hate. It uncovers the subconscious mechanisms that lead to the germination of brand hate and provides answers to unexplained and missing pieces in the existing literature. In particular, it offers a detailed perspective on how self-related motives can explain the psychology of brand hate.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".