Leader–member participation! Shared leadership, organizational support and employee positive behaviors in higher educational institutions (HEIs)
Bibliographic record
Abstract
Purpose This study examines the direct and indirect effects of shared leadership on job crafting and knowledge sharing, in turn, on employee performance, commitment and creativity via organizational support among Pakistani HEI faculty members. Design/methodology/approach Drawing on shared leadership theory, data from 311 faculty members were collected. Structural equation modeling (SEM) was employed to test the hypotheses. Findings The findings revealed a significant positive influence of shared leadership on job crafting and knowledge sharing among faculty members. Job crafting and knowledge sharing also significantly and positively influenced employee performance, commitment and creativity. Shared leadership includes support dynamics, so organizational support with shared leadership failed to influence job crafting and knowledge sharing. Practical implications Institutions promoting collaborative and innovative educational environments should consider strategies to nurture shared leadership practices, facilitate job crafting and bolster knowledge-sharing. Originality/value This study underscores the importance of fostering a shared leadership culture in HEIs, emphasizing the role of job crafting and knowledge sharing in employee performance, commitment and creativity.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".