Affective commitment and citizenship behaviors across multiple foci
Bibliographic record
Abstract
Purpose This paper seeks to examine the relationships between affective commitment and organizational citizenship behaviors (OCBs) across four foci: organizations, supervisors, coworkers, and customers. Further, it aims to determine whether relationships among commitments and OCBs involve mediated linkages. Design/methodology/approach This study relies on matched employee‐supervisor data (n=216). The relative fit of different models representing relationships among commitments and OCBs was examined using structural equations modeling. Findings Results revealed that commitments to coworkers, customers and supervisors displayed positive relationships with OCBs directed at parallel foci. In addition, commitment to the global organization partially and negatively mediated the relationship of commitments to coworkers and customers to parallel OCBs dimensions. Results also revealed cross‐foci relationships between local commitments and OCBs. Finally, no commitment target was significantly associated with organization‐directed OCBs but the latter were positively related to local OCBs. Originality/value The paper demonstrates that multiple commitments and OCBs are involved in a complex net of relationships among which local foci play a critical, and positive, role.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".