Mapping a Decade of Authentic Leadership Research: A Structural Topic Modeling Approach
Bibliographic record
Abstract
Authentic leadership, characterized by self-awareness, relational transparency, and ethical behavior, has emerged as a vital construct in leadership studies, addressing the complexities of volatile business environments. However, conceptual overlap with other leadership styles and limited cross-cultural applicability pose challenges to its theoretical and practical advancement. This study employs Structural Topic Modeling (STM) to analyze 99 peer-reviewed articles published between 2013 and 2023, uncovering key thematic patterns and evolutionary trends in authentic leadership research. Our findings reveal 20 topics central to defining authentic leadership, emphasizing its core dimensions while identifying emerging elements such as emotional intelligence, adaptability, and resilience. Additionally, 15 topics trace the theoretical evolution of authentic leadership across three phases: definitional clarity (2013–2015), contextual expansion (2016–2019), and dynamic integration (2020–2023). These phases highlight its progression from a static construct to a relational and context-sensitive model, integrating cultural adaptability and ethical decision-making. This study advances the understanding of authentic leadership as a dynamic and relational process shaped by organizational and cultural contexts. By validating its foundational dimensions and identifying new components, it offers theoretical clarity and practical insights for navigating complex and diverse leadership challenges.
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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 | 0.023 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.014 | 0.021 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".