On the motivational nature of authentic leadership practices: a latent profile analysis based on self-determination theory
Bibliographic record
Abstract
Purpose Although one of the central premises of authentic leadership theory is that authentic leaders mobilize their followers, the underlying motivational mechanisms of this process remain poorly understood. Drawing on self-determination theory, this study aims to fill that gap by examining authentic leadership practices (ALP) as theoretical antecedents of employees' motivation profiles. Design/methodology/approach Latent profile analyses conducted on a sample of 501 employees revealed four profiles: self-determined, unmotivated, highly motivated and moderately motivated. Findings ALP were associated with a higher likelihood of membership into the most adaptive motivation profiles. Employees in these profiles displayed more optimal job functioning: higher organizational commitment and performance, and lower intentions to leave their organization. Originality/value These findings underscore the predictive power of autonomous motivation for employee functioning and provide new insights into how ALP can improve work motivation, and hence job functioning. Our results account not only for how ALP affects the complete range of behavioral regulations at work but also the different patterns in which these regulations combine within employees.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".