Transformational leadership effectiveness: an evidence-based primer
Bibliographic record
Abstract
The proliferation of leadership models can leave practitioners confused about which model to use in guiding their leadership development initiatives. Although ‘new’ leadership models (e.g. authentic, ethical, and servant leadership) suggest theoretical differences, empirical research has shown considerable overlap among these models and longer standing ones such as transformational leadership. With extant literature questioning the added empirical value of these newer models, this paper aims to distil the best evidence about transformational leadership into a ‘primer’ that can help practitioners use evidence-led practices in their leadership development. To do so, we briefly review major leadership models, highlight evidence for empirical redundancy between new leadership models and transformational leadership, and discuss meta-analytic findings between transformational leadership and outcomes of leadership. Our review suggests that these newer leadership models add little incremental validity beyond transformational leadership in predicting various leadership outcomes. Moreover, transformational leadership demonstrates medium to large effect sizes on a range of individual, team, and organisational outcomes. Taken together, our findings suggest that organisations can benefit by focusing their resources on transformational leadership development, rather than on the latest leadership fad.
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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 | 0.128 | 0.210 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.021 | 0.013 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.015 | 0.020 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 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".