Authentic leadership, proactive goal regulation and help-seeking behavior: a multilevel moderated mediation
Bibliographic record
Abstract
Purpose Based on self-regulation theory, this study aims to investigate the relationship between authentic leadership and help-seeking behavior, as well as the mediating effect of proactive goal regulation and the moderating effect of leader identification. Design/methodology/approach We conducted a questionnaire survey on 489 employees from 94 teams and tested our research model through multi-level pathway analysis. Findings The analysis results suggest that (1) authentic leadership positively relates to employees’ proactive goal regulation; (2) employees’ proactive goal regulation positively relates to their autonomous (dependent) help-seeking behavior; (3) employees’ proactive goal regulation plays an intermediary role between authentic leadership and help-seeking behavior; (4) leader identification positively moderates the influence of authentic leadership on employees’ proactive goal regulation and (5) leader identification positively moderates the indirect relationship between authentic leadership and employees’ help-seeking behavior through employees’ proactive goal regulation. Practical implications Based on the findings of this study, organizations should foster authentic leadership in workplace to promote employees’ help-seeking behavior. In addition, managers should also attach importance to proactive goal regulation in promoting help-seeking behavior and leader identification in enhancing the positive influence of authentic leadership on employees’ proactive goal regulation. Originality/value This study finds that proactive goal regulation plays a key mediating role between authentic leadership and help-seeking behavior, and reveals the role of leader identification in reinforcing the positive impact of authentic leadership on help-seeking behavior.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".