Examining stakeholder reactions to corporate social irresponsibility: Evidence from social media
Bibliographic record
Abstract
What corporate behaviors are perceived as irresponsible by different stakeholders? How do such stakeholders react once they perceive irresponsibility? Using the literature on corporate social irresponsibility (CSiR), stakeholder theory and attribution theory, we examined a database of 100 000 social media posts on Twitter/X about Nestlé and H&M in the period 2015–2016. We found that the behavior of these two companies was perceived as irresponsible insofar as it caused direct harm to different stakeholder groups (stakeowners, stakeseekers, stakekeepers and stakewatchers). However, while stakeowners and stakeseekers were more likely to voice their concerns, they tended to voice their concerns only once. In contrast, stakewatchers and stakekeepers were more persistent in voicing concerns. In terms of goals, stakeowners and stakekeepers were more likely to advocate for information dissemination and community building than stakewatchers and stakeseekers, who were more likely to call for action. Our study therefore contributes to the CSiR and stakeholder engagement literature by illustrating how different stakeholder groups use social media to engage with firms perceived as irresponsible.
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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 | 0.010 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.004 |
| 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".