Character matters: The network structure of leader character and its relation to follower positive outcomes
Bibliographic record
Abstract
We investigated the relationship between self-ratings of leader character and follower positive outcomes-namely, subjective well-being, resilience, organizational commitment, and work engagement-in a public-sector organization using a time-lagged cross-sectional design involving 188 leader-follower dyads and 22 offices. Our study is an important step forward in the conceptual development of leader character and the application of character to enhance workplace practices. We combined confirmatory factor analysis and network-based analysis to determine the factorial and network structure of leader character. The findings revealed that a model of 11 inter-correlated leader character dimensions fit the data better than a single-factor model. Further, judgment appeared as the most central dimension in a network comprising the 11 character dimensions. Moreover, in a larger network of partial correlations, two ties acted as bridges that link leader character to follower positive outcomes: judgment and drive. Implications for theory and practice are discussed.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".