Building Trust with Material and Immaterial Corporate Social Responsibility: Benefits and Consequences*
Bibliographic record
Abstract
ABSTRACT We examine whether the benefits and consequences of building trust through corporate social responsibility (CSR) vary when the company engages in material or immaterial CSR, and the conditions under which these benefits hold. Our study informs companies about the relative benefits and consequences of engaging in particular types of CSR activities. Prior archival research finds that CSR performance can buffer companies against negative stock reactions caused by subsequent adverse events, such as financial restatements. However, theory suggests that there are boundary conditions for this buffering effect through the multiple dimensions of trust violations. We predict and find using Experiment 1 that positive performance in material CSR enhances competence trust, while positive performance in immaterial CSR enhances integrity trust in the company. We predict and find using Experiment 2 that positive material CSR performance alleviates investors' negative reactions to an error restatement but that this effect does not occur for a fraud restatement. In contrast, positive immaterial CSR performance results in greater negative reactions to a fraud restatement, but this effect does not occur for an error restatement. These effects can be explained through the multiple dimensions of trust and trust violation, in accordance with the schematic model of dispositional attribution. Lastly, a supplementary experiment supports the robustness of our results to the baseline of neutral CSR performance. Our study has important implications for companies and standard setters about the trust‐building effects of engagement in CSR and, more generally, of how CSR issues with different materiality levels buffer against the adverse effects of negative events.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".