Affective commitment and citizenship behaviors across multiple foci
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it