Citizenship Pressure as a Predictor of Daily Enactment of Autonomous and Controlled Organizational Citizenship Behavior: Differential Spillover Effects on the Home Domain
Bibliographic record
Abstract
This study questions the exclusive discretionary nature of organizational citizenship behavior (OCB) by differentiating between autonomous OCB (performed spontaneously) and controlled OCB (performed in response to a request from others). We examined whether citizenship pressure evokes the performance of autonomous and controlled OCB, and whether both OCB types have different effects on employees' experience of work-home conflict and work-home enrichment at the within- and between-person level of analysis. A total of 87 employees completed two questionnaires per day during ten consecutive workdays (715 observations). The results of the multilevel path analyses revealed a positive relationship between citizenship pressure and controlled OCB. At the within-person level, engaging in autonomous OCB resulted in an increase of experienced work-home conflict and work-home enrichment. At the between-person level, enactment of autonomous OCB predicted an increase in experienced work-home enrichment, whereas engaging in controlled OCB resulted in increased work-home conflict. The divergent spillover effects of autonomous and controlled OCB on the home domain provide empirical support for the autonomous versus controlled OCB differentiation. The time-dependent results open up areas for future research.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".