On the relation between felt trust and actual trust: Examining pathways to and implications of leader trust meta-accuracy.
Bibliographic record
Abstract
Research has long emphasized that being trusted is a central concern for leaders (Dirks & Ferrin, 2002), but an interesting and important question left unexplored is whether leaders feel trusted by each employee, and whether their felt trust is accurate. Across 2 field studies, we examined the factors that shape the accuracy of leaders' felt trust-or, their trust meta-accuracy-and the implications of trust meta-accuracy for the degree of relationship conflict between leaders and their employees. By integrating research on trust and interpersonal perception, we developed and tested hypotheses based on 2 theoretical mechanisms-an external signaling mechanism and an internal presumed reciprocity mechanism-that theory suggests shape leaders' trust meta-accuracy. In contrast to the existing literature on felt trust, our results reveal that leader trust meta-accuracy is shaped by an internal mechanism and the presumed reciprocity of trust relationships. We further find that whether trust meta-accuracy is associated with positive relational outcomes for leaders depends upon the level of an employee's actual trust in the leader. Our research contributes to burgeoning interest in felt trust by elucidating the mechanisms underlying trust meta-accuracy and suggesting practical directions for leaders who seek to accurately understand how much their employees trust them. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
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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 | 0.016 | 0.133 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".