An Inconvenient Truth: A Comprehensive Examination of the Added Value (or Lack Thereof) of Leadership Measures
Bibliographic record
Abstract
Abstract The leadership literature encompasses a bewildering array of leadership styles, with most studies focussing on the nature and consequences of a single leadership style in isolation. This isolationist approach has led researchers to mostly ignore the similarities between supposedly different leadership styles, and few studies have examined these overlaps empirically. To understand the extent of this problem, we use bifactor exploratory structural equation modelling to examine whether 12 dominant leadership measures capture shared variance and whether any variance unique to a particular style is related to theoretically and empirically established covariates. Moreover, we explore what the shared variance of these leadership measures may represent. Across seven samples, five countries, multiple organizational contexts, and 4000 respondents, the 12 leadership measures shared significant amounts of variance and did not systematically capture unique leadership‐related variance. Further analyses indicated this shared variance mainly represented the affective quality of the leader–follower relationship. The results reveal an inconvenient truth for leadership researchers who wish to differentiate styles, as the styles have much more in common than differences. Contrasting with previous recommendations to refine styles, we argue that a taxonomic leadership behaviour categories approach to leadership research is the most parsimonious way forward.
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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 | 0.172 | 0.480 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".