The effect of leader risk-taking on subordinate felt trust
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine how leaders’ behaviour and subordinates’ personality can impact subordinates’ feelings of being trusted. Feeling trusted by one’s leader is associated with increased performance, organisational citizenship behaviours and job satisfaction (Baer et al., 2015; Lester and Brower, 2003). Design/methodology/approach Participants read a vignette in which a leader’s behaviour was manipulated and then rated the extent to which they felt trusted. Participants in Sample 1 consisted of 726 undergraduate students with work experience, and Sample 2 consisted of 1,196 people with work experience recruited over CrowdFlower, a crowd-sourcing website. Findings Results from both samples indicate that a leader delegating a task increases subordinate felt trust, for Sample 1p<0.001,d=0.75 and for Sample 2p<0.001,d=0.90. Further, subordinate felt trust increases when the task delegated is of high importance, for Sample 1p<0.001,d=0.42 and for Sample 2p<0.001,d=0.58. The likelihood of the delegated task resulting in negative outcomes and subordinate propensity to trust have negligible effects on felt trust. Originality/value Despite the organisational benefits of felt trust, it is still unclear how to elicit subordinates’ felt trust. This study is one of the first to empirically examine leader behaviour that may lead subordinates to feel trusted in the workplace. These findings support theoretical underpinnings of the relational leadership model and the risk-based model of trust.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".