A deontic perspective on organizational citizenship behavior toward the environment: The contribution of anticipated guilt
Bibliographic record
Abstract
Abstract This study draws on deontic justice theory to examine an unexplored socioemotional micro‐foundation of corporate social responsibility (CSR), namely anticipated guilt, in an effort to improve our understanding of employees’ moral reactions to their organization’s CSR. We empirically investigate whether environmental CSR induces anticipated guilt (i.e., concerns about future guilt for not contributing to organizational CSR) leading to organizational environmental citizenship behavior. We also consider two boundary conditions related to the social nature of anticipated guilt: line manager support for the environment and negative environmental group norms. To test our hypotheses, we analyzed data from a convenience sample of 503 managers working in Mexican organizations, using Latent Moderated Structural equation modeling. Overall, our results support the deontic argument that employees care about CSR because CSR embodies moral concerns. Specifically, our findings show that efforts to avoid a guilty conscience increase when the line manager provides increased resources and control to act for the environment, and when group members do not care for the environment, suggesting that employees feel they have to compensate for their group’s moral failure.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".