Bibliographic record
Abstract
This paper explores the extent to which firms targeted by consumer boycotts strategically react to defend their public image by using prosocial claims: expressions of the organization’s commitment to socially acceptable norms, beliefs, and activities. We argue that prosocial claims operate as an impression management tactic meant to protect targeted firms by diluting the negative media attention attracted by the boycott. We test our hypotheses using a sample of 221 boycotts announced between 1990 and 2005. Results suggest that boycotted firms do significantly increase their prosocial claims activity after a boycott is announced. Firms are likely to react with a larger increase in prosocial claims when the boycott is more threatening (it receives more media attention), when the firm has a higher reputation, or when the firm engaged in more prosocial claims before the boycott. We demonstrate that firms fall back on their established impression management strategies when they face a reputational threat and will increase these previously perfected performances as the threat increases. In this way, the severity of a threat positively moderates the relationship between a firm’s prior performance repertoire and future performance repertoire, a mechanism we refer to as “threat amplification.” When an organization with high reputational standing has bolstered its position by using prosocial claims in its past performance repertoire, however, it will perceive itself to be shielded from movement attacks, decreasing the likelihood of any defensive response, a mechanism we call “buffering.” Our findings contribute to impression management by exploring the use of impression management in response to a movement attack and highlighting the important role that a firm’s pre-threat positioning plays in its response to an image threat.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.052 | 0.013 |
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".