Personality and contextual antecedents of organizational citizenship behavior: A study of two occupational groups
Bibliographic record
Abstract
Abstract This paper examines the impact of personality trait of dispositional affect and contextual variables of multiple commitments on organizational citizenship behaviors (OCBs) in two occupational groups. Three dimensions of OCBs were considered: helping, civic virtue and sportsmanship behaviors. We used positive and negative affectivity scale to measure dispositional affect. For commitments, we examined affective and normative organizational and occupational commitments. The data were collected from 180 engineers and 180 teachers. The findings show that affect, multiple commitments and occupation all have significant impacts on different dimensions of OCBs. Dispositional affect had the most influence on all three dimensions of OCBs. In addition, helping behavior is affected by normative organizational commitment while civic virtue behavior is influenced by affective commitments (both organizational and occupational) and occupation. Sportsmanship behavior is explained by occupation and affective organizational commitment. Occupation has been shown to make a unique contribution to understanding OCBs. The present study showed that the teachers, for example, exhibited more civic virtue and sportsmanship behaviors than the engineers. Implications of the findings for future research and practice are discussed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".