Team-member and leader-member exchange, engagement, and turnover intentions: implications for human resource development
Bibliographic record
Abstract
This study investigated the underlying dynamics between team member exchange (TMX) quality, leader-member exchange (LMX) quality, engagement, and turnover intentions through the lens of Job Demands-Resources (JD-R) theory. Data were collected from 407 employees working at United States and Israeli firms via a questionnaire. Findings indicated that work engagement mediates the relationship between TMX quality and turnover intentions and that LMX moderates this mediation. Specifically, in high and moderate LMX quality conditions, the impact of TMX quality on work engagement was stronger; and LMX moderated the impact of work engagement on turnover intentions by lowering turnover intentions further. When leader-member relations were not of high or moderate quality, TMX quality did not associate significantly with employee work engagement. Findings contribute to human resource development (HRD) literature on work engagement and turnover by addressing a) the connection between TMX and engagement, b) the mediation effect of engagement in the relationship between TMX and turnover intentions, and c) moderating effect of LMX. Implications for human resource development practices, in particular, for managerial training and leadership development and for performance appraisal, were discussed with the focus on team building to promote individual work engagement and reduce turnover.
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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 | 0.004 | 0.012 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".