The impact of brand evangelism on oppositional referrals towards a rival brand
Bibliographic record
Abstract
Purpose This study aims to build on the notion of brand evangelism developed by Becerra and Badrinarayanan (2013) by examining how brand relationship variables regarding one brand (i.e. brand loyalty, brand community identification and self-brand connection) influence oppositional referrals to a rival brand (i.e. desire to harm and trash-talking) in the high definition (HD) videogame console industry. Design/methodology/approach A survey of online communities devoted to video gaming was conducted using a sample of 809 respondents, all owners of either a PlayStation or an Xbox. Findings The results show that the desire to harm the rival brand is strongly and positively associated to participation in trash-talking. Brand loyalty is connected to both dimensions of oppositional brand referrals. Consumers’ connection with the brand affects trash-talking only indirectly through the desire to harm. No association is found between identification with the brand community and oppositional brand referrals. Originality/value This study is the first to demonstrate the mechanism linking brand relationship variables regarding a focal brand with consumers’ disparagement of a rival brand, showing that a desire to harm plays a central role. Just as the desire for retaliation drives negative word-of-mouth in the context of an unsatisfactory experience with a brand (Grégoire and Fisher, 2006), the desire to harm drives trash-talking against a rival brand by brand evangelists. This study improves our understanding of the relationships consumers build with their preferred brands and how this relationship may influence their rejection of competing brands with which they do not have direct experience.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".