Linking Self-efficacy and Organizational Citizenship Behavior: A Moderated Mediation Model
Bibliographic record
Abstract
This study investigates the relationship between self-efficacy and Organization Citizenship Behavior (OCB), moderated by incivility and mediated by pro-social motivation. Self-efficacy is the employee's belief in him about his skills to perform tasks in different situations. The direct and indirect effects of self-efficacy of teachers towards their OCB through prosocial motivation have been observed in this study. For this purpose, data has been collected through questionnaires (N = 301) using convenience sampling in three-time phases with two weeks gaps between each phase. SPSS 22.0 and Amos 22.0 were used along with Process by Hayes for moderated mediation analysis. The results indicate that self-efficacy leads to organizational citizenship behavior (OCB) and pro-social motivation, moderated by incivility and mediated by pro-social motivation. In a nutshell, this study demonstrates self-efficacy enhances pro-social motivation and OCB within academic settings with reference to Pakistan, advocating that if teachers are confident to perform a task, they can also demonstrate their extra-role behavior. The present study contributes to the literature by analyzing the novel framework within the Pakistan context. The mediating effect of prosocial motivation between teachers’ self-efficacy and OCB has not been discussed in the prior studies. The implications, discussion, and conclusion are also 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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".