Servant Leadership: Antecedent Factors, Impact, and Education Theories Used as Researcher's Perspective
Bibliographic record
Abstract
A comprehensive understanding of the antecedent factors, and the impact of servant leadership and also about the education theories used as a perspective are so essential for leaders and researchers. However, there is not enough information about it. This paper was made to fill this gap by using the literature review approach. It was done to 71 Scopus indexed articles, which were published in the 2015 – 2020. There are several results of the review, those are: (1) servant leadership is influenced by the emotional intelligence, self-efficacy, motivation to serve, non-calculative as one dimension of motivation-to-lead, and mindfulness; (2) servant leadership have an impact on 38 dependent variables in individual level and 16 dependent variables in the organizational level both directly and indirectly; (3) there are 31 theories, which are used as a researcher's perspective, and two between them, which are mostly used are the social exchange theory and social learning theory. The result of this research gives contribution, which enriches the theoretical scope of servant leadership. This academic contribution is for sure will be so beneficial for leaders who commit to developing the best potential owned by their staff for a better organization. The result of this research will also be essential for future researchers because it shows a state of the art and research gap about servant leadership.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".