Beyond the Full-range Leadership:Incremental Effects of Machiavellian Leadership in Predicting Trust
Bibliographic record
Abstract
A perennial challenge facing leaders is gaining their followers' trust. The present research investigates the relationship between leadership style and trust in the leader as mediated by employees’ affective reactions and perceptions of leader trustworthiness. Leadership style was operationalized in terms of the full-range leadership model (FRLM, Bass & Avolio, 2004) augmented with an author-developed measure of Machiavellian leadership to aid in distinguishing between transformational and pseudo-transformational leadership (Bass & Steidlmeier, 1999; Christie et al., 2011). Following a preliminary measure development study, we conducted a field survey study with employees (N = 292) and an experimental study with working students (N = 392) to test our mediation model. Both studies provided evidence for the mediating effect of perceived trustworthiness and the incremental contribution of Machiavellian leadership in the prediction of trust; positive affect also mediated the link between leadership and leader trust in the experimental study. The experimental study also extended earlier findings by Christie et al. by demonstrating that Machiavellian leadership behaviors complement the facets of transformational leadership identified in the FRLM in distinguishing the behavioral profiles of transformational and pseudo-transformational leaders. Implications for the leadership theory and practice (e.g., leader selection and development) are discussed.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".