Consumer Boycotts and Fast-Food Chains: Economic Consequences and Reputational Damage
Bibliographic record
Abstract
The increasing avoidance of international fast-food chains is a widespread phenomenon influenced by economic, social, and political factors. This study examines the risks and implications of restaurant boycotts, focusing on their role in social justice movements and economic shifts. The authors employed the qualitative approach; using an exploratory case study and a critical discourse analysis, we investigated consumer motivations for avoidance, the financial and reputational risks businesses face, and how corporate responses shape brand perception. By integrating political consumerism and social justice theory, we provide a comprehensive framework for understanding the psychological, ethical, and economic drivers of boycotts. The findings highlight that boycott behavior significantly impacted declining sales and profits for McDonald’s and Starbucks and forced the closure of outlets, as well as leading to the loss of consumer trust and long-term brand loyalty. Thus, it forced companies to create strategies for protecting their reputation. Consumer activism, which draws from social justice and ethical consumerism, demonstrates its capability to affect corporate policy choices and business practices in sensitive political situations to fight injustices. This research offers valuable insights for business leaders navigating consumer activism, emphasizing the need for proactive corporate responsibility strategies to mitigate the risks of reputational damage and declining consumer trust.
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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 | 0.004 | 0.007 |
| 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".