Exploring the interplay of transformational leadership, job satisfaction, and organizational commitment in enhancing organizational citizenship behavior
Bibliographic record
Abstract
This study investigates the intricate relationships between transformational leadership behaviours (TLB), job satisfaction (JS), organizational commitment (OC), and organizational citizenship behaviours (OCB) within the context of county governments in the United States. Drawing on a theoretical model that hypothesizes both direct and indirect effects of TLB on OCB, mediated by JS and OC, this research employs structural equation modelling to analyze survey data collected from 372 matched leader-follower pairs across various county governments. The findings substantiate the hypothesized model, revealing that TLB indirectly influences OCB through the mediating effects of JS and OC. Specifically, the study underscores the pivotal role of job satisfaction and organizational commitment as conduits through which transformational leadership exerts its positive impact on organizational citizenship behaviours. This research contributes to the literature by empirically validating a comprehensive model within the public sector context, thereby extending our understanding of the mechanisms through which transformational leadership fosters a conducive work environment that promotes OCB. The study's implications for leadership development and organizational policy within public sector organizations are discussed, alongside limitations and directions for future research.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".