Supervisory Mentoring and Affective Commitment and Turnover: The Critical Role of Contextual Factors
Bibliographic record
Abstract
Current mentoring research offers little insight into the contextual factors that influence the relationships between mentoring and individual and organizational outcomes. In order to fill this void, we investigate how job scope and career and development opportunities, two critical contextual factors, moderate the relationship of supervisory mentoring to turnover as mediated by affective commitment. Integrating social exchange theory with insights from situational approaches to leadership, we hypothesized that (a) job scope would interact with supervisory mentoring and (b) career and development opportunities would interact with affective commitment such that the supervisory mentoring-turnover relationship, as mediated by affective commitment, would be stronger at high levels of these contextual moderators. Results of a study conducted with a sample of 228 business alumni, using 15-month voluntary turnover as outcome, supported our predictions. We discuss the implications of our findings for mentoring research and practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".