Unpacking the Relationship Between Organizational Citizenship Behavior and Counterproductive Work Behavior: Moral Licensing and Temporal Focus
Bibliographic record
Abstract
Traditionally, scientific- and practitioner-oriented publications tend to categorize employees in groups of either “good” or “bad” employees, thereby omitting that one category of employees might engage in organizational citizenship behavior (OCB-O) and counterproductive work behavior (CWB-O). In this study, we concurrently examine the mediating role of moral credits and credentials, as well as the moderating role of subjective temporal focus. Specifically, we argue that when employees enact OCB-O, they obtain moral credits and credentials, which in turn might make employees more likely to enact CWB-O. Moreover, we argue that the latter relationships depend on an employee’s subjective temporal focus, resulting in an OCB-O—CWB-O relationship that is (1) positive for a past temporal focus, (2) negative for a future temporal focus, and (3) non-significant for a present temporal focus. We examined these hypotheses by means of a multilevel weekly survey study and largely found support for our hypotheses, especially with regard to the role of moral credentials as the mediating mechanism and the aggravating versus attenuating effect of past versus future temporal focus, respectively. We end with a discussion on implications, suggestions for future research, and recommendations for practice.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".