Validation of the Wise Leadership Questionnaire (WLQ)
Bibliographic record
Abstract
Purpose The purpose of this study was to validate the psychometric properties of the Wise Leadership Questionnaire (WLQ). Design/methodology/approach Data were collected from three independent samples from Canada, China and Morocco (n = 616). Factor analysis, first- and second-order confirmatory factor analyses, structural equation modeling and Bayesian approach were used. Findings Study 1 confirmed that the WLQ higher-order factor structure is the most adequate theoretical model to capture the four-factor structure of the wise leadership scale, namely, intellectual shrewdness, spurring action, moral conduct and cultivating humility which are essential for a leader to qualify as wise. Study 2 assessed and supported the criterion-related validity by approving that the higher-order wise leadership construct constituted a predictor of work outcomes such as followers’ subordinates’ performance and job satisfaction. Confirmatory factor analysis results yielded a second-order factor of the wise leadership construct with four first-order factors, namely, the four wise leadership dimensions. The correlations between the four first-order factors (i.e. dimensions) and the second-order factor of the wise leadership are positive and statistically significant in both the China and Morocco samples. They are, respectively, as follows: intellectual shrewdness (β = 0.74; 0.62, p < 0.01), spurring action (β = 0.52; 0.76, p < 0.01), moral conduct (β = 0.76; 0.62, p < 0.01) and cultivating humility (β = 0.78; 0.69, p < 0.01). Originality/value Results suggest that the new wise leadership construct is positively associated with followers’ subordinates’ job performance and job satisfaction directly and indirectly through supervisory support, emphasizing the added value and relevance of the WLQ.
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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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".