Trust in the supervisor and the development of employees’ social capital during organizational entry: a conservation of resources approach
Bibliographic record
Abstract
This article aims to understand how trust in the supervisor contributes to the development of employees’ social capital using Conservation of Resources theory as a theoretical framework and networking ability as an indicator of social capital development. We hypothesize that the relationship between newcomers’ trust in the supervisor and networking ability will be mediated by feedback seeking from the supervisor and moderated by emotional exhaustion. Based on a three-wave time-lagged study of newcomers (N = 224), we found trust in the supervisor to be indirectly and positively related to networking ability through the mediating influence of feedback seeking from the supervisor. In addition, feedback seeking interacted with emotional exhaustion in predicting networking ability such that it was more positively related to it at high levels of emotional exhaustion. The indirect relationship of trust to networking ability as mediated by feedback seeking was also stronger at high levels of emotional exhaustion. We discuss this study’s implications for our understanding of supervisors’ role and newcomers’ experience during entry, as well as for social capital research.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".