Assessing hatred and anti-brand activism within online brand communities: the roles of ideological imbalance and online brand avoidance
Bibliographic record
Abstract
Purpose This study aims to investigate the interplay between brand hate, online brand avoidance and anti-brand activism among online brand communities (OBCs). It also examines the moderating role of ideological imbalance between online brand communities’ hate and online brand avoidance. This study also explores the mediating effect of online brand avoidance on the relationship between hatred within OBCs and the emergence of online anti-brand activism. Design/methodology/approach An online survey was used to collect primary data from 512 members of an OBC in a developing nation. The collected data were analyzed by using PLS-SEM 3.3. Findings Using Sternberg’s duplex theory of hate, this study demonstrates that the generation of negative emotions by users leads to the subsequent emergence of negative emotions, ultimately resulting in behavioral outcomes. Interestingly, this study shows that OBCs’ negative e-word of mouth is an insignificant predictor of OBCs’ hate. Online brand hatred leads to online brand avoidance, which is ultimately associated with online anti-brand activism. This study also reveals that online brand avoidance significantly mediates the relationship between OBC hatred and online anti-brand activism. The study suggests that ideological imbalance moderates the relationship between OBCs’ hate and online brand avoidance. Research limitations/implications This study provides empirical insights into the phenomenon of hatred toward OBCs, brand aversion and anti-brand activism. It does so by applying Sternberg’s duplex theory of hate, a theory that has been rarely used in the context of online brand communities or brand hate. This study reveals a triangular relationship between OBCs’ hatred, online brand avoidance and anti-brand activism. These findings hold significant implications for managers, particularly for enterprises in emerging economies. Practical implications This study reveals a triangular relationship between OBCs’ hatred, online brand avoidance and anti-brand activism, offering valuable insights for managers, especially those overseeing enterprises in emerging economies. Consequently, understanding these dynamics aids strategic decisions to mitigate negative interactions and enhance brand resilience. Originality/value This study addresses the gaps in existing literature by providing insights into the factors contributing to brand hatred within OBCs. To the best of the authors’ knowledge, this is the first attempt at using ideological imbalance as a moderator between OBCs’ hatred and online brand avoidance, especially in a developing nation.
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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 | 0.001 | 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".