Brands in the eye of the storm: navigating political consumerism and boycott calls on social media
Bibliographic record
Abstract
Purpose Given the division between conservative and liberal ideologies on many issues, brands navigate social media minefields whenever they take a social or political stance. This study aims to explore real-time social media consumer responses to eight US boycott threats, including both conservative-based and liberal-based calls for boycott. Design/methodology/approach A grounded theory analysis of approximately 800 tweets collected in the 24 h following each brand’s trigger event led to a framework of motivations for using social media to engage in boycott discussions over a brand’s political stance. Findings Eleven pro-boycott and 11 anti-boycott consumer profiles emerged across cases. Overarching motivations for pro- and anti-boycotters include a desire to cause/prevent change, seeking justice/fairness, self-enhancement and expression of hostility. Findings suggest that political consumerism occurs with differing motivations and varying levels of emotion, that brand defenders may lessen boycott effectiveness and that threats to boycott may not always translate to actual boycotts. Originality/value This paper explores actual consumer boycott calls from various industries as they unfolded in real-time, as opposed to other research that explores hypothetical boycotts or a single case study. Additionally, to the best of the authors’ knowledge, this work is among the first to explore how consumers enter the boycott conversation in defense of the brand and attempt to diffuse the call for a boycott.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".