Bibliographic record
Abstract
This research examined the process of employee trust building, as influenced by the behavior and practices of leaders, during a period of organizational renewal. Specifically, the research answered the following questions: (a) what behaviors did leaders report undertaking to build trust? (b) did High and Low Trust leaders differ in the practices they undertook to build trust, based on Ghoshal and Bartlett's (1997) model? and (c) are the variables equity, competence, and involvement, identified by Ghoshal and Bartlett, a sufficient account of High Trust leaders' trust building? A case study approach was used in the context of a large Canadian financial institution's technology division. Initially, secondary analysis was applied to employee survey data in order to identify High and Low Trust leader groups. The primary research consisted of qualitative interviews conducted with both the High Trust and Low Trust groups. The findings indicated that the High Trust leaders reported engaging in more trust-building practices than did the Low Trust group. The research elaborated specific components of involvement, competence, and equity related practices. Involvement practices comprised of informing, identifying issues, problem solving, decision making, and future planning. Competence building involved leaders setting clear accountabilities, assessing, providing feedback, assigning, mentoring, coaching, developing, and training staff. Equity was evidenced through consistency, fair assessment in downsizing, career support, and fair compensation practices. Further, the research proposed additions to the Ghoshal and Bartlett (1997) model. Organizational context building through collaboration and communication, as well as relationship building through integrity, caring, and consistency, were identified as High Trust leader practices supporting trust. Practice implications suggest leadership development in the specific practices supporting high levels of trust. As well, this research recommends attention to leaders' trust-building practices to build and sustain trust during organizational change and renewal.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".