The power to voice my hate! Exploring the effect of brand hate and perceived social media power on negative eWOM
Bibliographic record
Abstract
Purpose Consumer brand relationship literature has recently seen a surge of studies on brand hate, its antecedents and outcomes. Hate alone will not drive consumers to engage in negative electronic word-of-mouth (eWOM) and indicates the interplay of other social relationship factors that can strengthen the effect of brand hate on negative eWOM. The purpose of this study is to integrate the emerging concept of brand hate and perceived social media power with the theory of planned behavior (TPB) to expand the understanding of negative eWOM. Design/methodology/approach Data is collected through a survey conducted among university students based in the National Capital Region of Delhi in India. The research model is empirically tested using structural equation modeling in AMOSv23. Findings The three TPB dimensions, including brand attitude, subjective norms and individual’s propensity to anthropomorphize, are found to influence brand to hate significantly. The other perceived control factors included in the model, perceived homophily and social media self-efficacy, were found to affect perceived social media power, which, in turn, is crucial in predicting consumers’ engagement in negative eWOM behavior, both directly and through interaction with brand hate. Originality/value The study contributes to brand hate literature and offers a novel perspective by advocating the role of consumers’ propensity to anthropomorphize in augmenting feelings 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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".