Helpful Today, But Not Tomorrow? Feeling Grateful as a Predictor of Daily Organizational Citizenship Behaviors
Bibliographic record
Abstract
This research extends the existing theoretical understanding of what predicts organizational citizenship behavior (OCB). Using experience sampling techniques, we examine the within‐person relation between OCB and a novel, theoretically relevant predictor: state gratitude. Using 4 independent samples with a total of 210 working adults and 173 undergraduate students, we developed a reliable and valid measure of state gratitude. Drawing upon the moral affect model of gratitude and affective events theory, we conducted 2 experience sampling studies with data collected from 67 (Study 2) and 104 (Study 3) working adults to test the effects of state gratitude on OCB, beyond the effects of several relevant constructs (i.e., state positive affect, dispositional gratitude, and social exchange). Our results advance OCB research and explanations of OCB by modeling OCB as a dynamic, time‐variant construct and by demonstrating that feelings of gratitude, a discrete positive emotion, can be an effective predictor of OCB.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".