The 100 Most-Cited Research Publications on Servant Leadership: A Bibliometric Analysis
Bibliographic record
Abstract
This study analyzed the 100 most-cited servant leadership publications in the Scopus database from 1991 to 2021 by using bibliometric analysis. The analysis includes visualization, bibliographic coupling, co-occurrence, and co-authorship analysis. Country contributions were examined, with the United States emerging as the dominant contributor, followed by the Netherlands, Australia, China, and Canada. van Dierendonck and Liden being the most influential contributors. Their work has focused on areas related to servant leadership scales, organizational behavior, and the conceptual development of servant leadership. The analysis of top keywords revealed a diverse range of research interests, underscoring the breadth and complexity of servant leadership as a concept. The study highlights the importance of international collaborations in advancing servant leadership research and emphasizes the need for increased research engagement from non-dominant countries to address the existing knowledge imbalance. The findings shed light on research trends, country contributions, influential authors, and important research themes, helping researchers identify gaps and future directions in servant leadership research. The findings could promote the development and application of servant leadership theory to enhance leadership practices and organizational outcomes.
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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 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.226 | 0.250 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".