The role of trust: Implications for psychological capital and authentic leadership
Bibliographic record
Abstract
The need for positivity and transparency in organizations has perhaps never been greater. Organizational scandals are widespread and organizational demands on leaders and employees at all levels are at an all time high. Drawing from positive psychology and past leadership research, this dissertation explores the impact that leader positive psychological capital, or PsyCap (hope, optimism, efficacy, and resiliency), and level of transparency affect perceptions of trust in them and their overall effectiveness as an authentic leader. Utilizing an on-line experimental study with random assignment of 304 adult, fully employed subjects into four conditions (high leader PsyCap/high leader transparency, low leader PsyCap/high leader transparency, high leader PsyCap/low leader transparency, and low leader PsyCap/low leader transparency), results indicated strong support for the hypothesized relationships. Specifically, even after controlling for a wide variety of subject variables (whether the subject has been through an organizational downsizing, subjects' gender, subjects' age, subjects' propensity to trust, subjects' overall years of work experience, whether the subject was based in the United States, subjects' job type, and subjects' job level), it was found that both the leader's level of PsyCap and level of transparency impact followers' trust in that leader and followers' evaluation of the effectiveness of that leader. Limitations, practical implications, and future research are noted. Results contribute to the better understanding and positive impact of authentic leadership.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".