How HR practices, leader behaviors and structure influence OCB beneficiaries to team level ?
Bibliographic record
Abstract
Using data from 120 teams in a large Canadian financial institution, we examined a comprehensive unit-level model linking leadership behavior, development-enhancing practices, and organic structure to team-focused citizenship behavior via perceptions of social exchange relationships with the organization (OMX), direct superiors (LMX), and team members (TMX). In accordance with the target similarity model, we found that development-enhancing practices foster a stronger relationship and OCB directed toward the organization, leadership behavior elicits a more positive relationship and OCB directed toward supervisors, whereas an organic structure fosters a more positive relationship and OCB directed toward teammates. However, the target similarity model did not prove to be the most optimal model. We found that development-enhancing practices were the only significant driver to all relationship foci, namely to OMX, LMX and TMX, whereas TMX was the only relationship focus significantly related to all OCB beneficiaries, namely OCB-O, OCB-S and OCB-I. The implications, the limitations and future research 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 | 0.003 | 0.010 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".