Blame and culpability in explaining changes in perceptions of corporate social responsibility and credibility
Bibliographic record
Abstract
Abstract This study uses the sequential updating mechanism and draws on several theories, such as the attribution theory, the self‐perception theory and the shame theory, to explain the interplay between consumers' perceptions of corporate social responsibility (CSR) and corporate credibility. It contends that both CSR and corporate credibility undergo the sequential updating mechanism. A two‐stage model (before and after a corporate public relation [PR] crisis) is used to investigate how individuals' perceptions of CSR and credibility are determined by their blame attribution to the firm, their self‐culpability, as well as their prior perceptions of CSR and credibility. To test the research hypotheses, four samples were collected from Spain (224 and 244) and the United Kingdom (307 and 236). Respondents had to state their opinions in relation to a Spanish and a British company operating in the fashion industry. For the model estimation, the SmartPLS 3 was used. The results show that consumers' perception of a firm's liability has a significant impact on their feeling of culpability, which in turn strongly and negatively affects their perceptions of the firm's CSR and credibility. In addition, consumers' prior perceptions of CSR and credibility play a relevant role in regulating and offsetting the final effect of a corporate PR crisis.
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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 | 0.005 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".