Spiritual leadership, job Satisfaction, and its effect on organizational commitment and organizational citizenship behavior
Bibliographic record
Abstract
Organizational citizenship behavior (OCB) has received a great deal of attention among researchers recently given the practical importance and its implication for the organizations. Building on a theoretical framework that links characteristics of leader and perception of individuals and their work settings to organizational commitment (OC) and citizenship behavior, the objective of this study was to investigate the relationship between spiritual leadership, job attitudes, and organizational citizenship behavior among lecturers at Islamic University. A cross-sectional design was used to meet the objectives set. Data were collected through the administration of questionnaires to 170 lecturers from Islamic University in Malang. Data were collected through 5 questionnaires in which participants completed measures of spiritual leadership, job satisfaction, organizational commitment, and OCBs. Results showed that one of the seven hypotheses proposed was rejected in this study. The results of structural equation modeling indicated a direct effect between spiritual leadership and OCB, and an indirect effect of organizational commitment on the relationship between spiritual leadership and job satisfaction toward OCB. The implications of these findings, as well as directions for future research, are addressed.
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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 | 0.000 |
| 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) | 0.003 | 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".