Bibliographic record
Abstract
Purpose This study aims to examine the emotional components of brand hate and the variation of emotions across different levels of brand hate. Design/methodology/approach Study 1 uses in-depth interviews and data triangulation. Studies 2-5 make use of quantitative methods to test and validate the multidimensional structure of brand hate and the variation of its composing emotions. Findings Study 1 suggests that brand hate is a multidimensional construct comprised of anger-, sadness- and fear-related emotions; possible antecedents and consequences are discussed. The quantitative results from Studies 2-5 confirm the findings in Study 1. A three-factor scale consisting of nine items is developed. The proposed model is tested among different samples and is compared with the currently available brand hate models. In addition, the findings show that emotions weigh differently for different brand hate levels. Research limitations/implications This study contributes to the brand hate literature and provides a structure to understand brand hate more thoroughly. Practical implications Companies can benefit from the research through a better knowledge of brand hate. Managers can use the multidimensional measurement to detect brand hate and better cope with it. Originality/value This study is among the first few attempts to examine the multidimensionality of brand hate and to investigate the variation of emotions in different brand hate levels. This study contributes to a more precise description of the brand hate construct and improves understanding of consumer-brand relationships.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".