Brand responses to influencer scandals: An action plan for managers
Bibliographic record
Abstract
Influencers are an increasingly popular and highly effective way to expand brand reach and generate consumer interest. Yet, influencers are often involved in scandals that can negatively impact the brand or products they endorse. This research explores influencer scandals and offers managerial guidance on how brands should respond to them. Using examples, we identify four key factors that brand managers should consider when faced with influencer transgressions. We develop an action plan that suggests that brand managers assess culpability, the gravity of the transgression, the nature of the endorsement, and the influencers’ responses to the incident. This paper culminates with a decision tree to guide managers in the difficult process of handling influencer transgressions and determining whether to stick with or cut ties with an influencer.
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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 | 0.042 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".