Is the role attributed to value congruence in transformational leadership theory a case of missing the forest for the trees? An exploratory study
Bibliographic record
Abstract
Value congruence between followers and leaders is considered to be a keystone of transformational leadership. However, we do not know whether congruence is important regardless of the content of values, of the leadership behavior assessed, and whether the patterns are stable across leaders. To address these gaps, we recruited a sample of 300 participants, representative of the U.S. population in terms of age, sex, and race, five days before the 2020 U.S. presidential elections. Participants assessed their own values as well as the values and transformational leadership of two presidential candidates. We explored the relationships between variables through multiple specifications of polynomial regressions and lasso regressions. Our results do not suggest that value congruence is particularly importantly related to transformational leadership; however, they do point to an important contribution by perceived leader benevolence. Based on these results, we conclude that the focus on value congruence in the leadership literature might be a case of missing the forest for the trees.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".