Values as incremental predictors of organizational citizenship behaviour
Bibliographic record
Abstract
This field study (n=107) examined ethnic differences in organizational citizenship behaviour (OCB), between co-workers and more specifically examined Schwartz's (1992) values as incremental predictors of OCB. Both self and peer reports of OCB were collected. The correlation between them was low. Significant differences in OCB between different ethnic groups were found for peer assessments. In addition, significant differences in values were found between ethnic groups, adding further support that ethnicity can be captured by measuring cultural values at the individual level. Multiple regression analyses revealed that values were not significant predictors of OCB and did not add incremental validity over other predictors of OCB, namely conscientiousness, job satisfaction and affective commitment. However, when values were moderated by job autonomy, the amount of additional variance accounted for was significant. In addition, a person's value for power was found to be significantly moderated by job autonomy, such that individuals that value power and are in jobs with a high level of autonomy are less likely to exhibit OCB. Understanding cultural values is of increasing interest given the rise in the ethnically diverse workforce in heterogeneous nations such as Canada and the U.S. The implications of these findings as well as the differences in self versus peer ratings of OCB 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 | 0.002 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".