Changes in relationship conflict as a mediator of the longitudinal relationship between changes in role ambiguity and turnover intentions
Bibliographic record
Abstract
Purpose – This study aims to clarify the relationship between changes in role ambiguity and turnover intentions. The authors propose that increases in role ambiguity over time can bias employees’ interpretations such that they come to view more relationship conflict at work. Because of the importance of social relationships at work, the authors propose that these increases in perceptions of relationship conflict mediate the positive effect of increases in role ambiguity on turnover intentions. Design/methodology/approach – This study is a two-wave longitudinal analysis of survey responses obtained from 146 employees working in the health-care sector over a three-year period. Structural equation modeling of cross-lagged correlations was used to test the hypothesized model. Findings – The positive relationship between increases in role ambiguity and turnover intentions over time is mediated by increases in relationship conflict. Results provide an integrative explanation of the phenomenon, uniting role theory, conflict theory and turnover theory. Research limitations/implications – Measures were all self-reported, and the non-experimental nature of the research design precludes causal interpretations. Future research should incorporate sources of measurement other than the focal employee and include additional variables presumed to operate in explaining these effects. Practical implications – Results highlight the need to monitor changes in employees’ role ambiguity beliefs over time. They also point to conflict management interventions as a potential means of reducing turnover intentions among employees who experience role ambiguity increases. Originality/value – The longitudinal examination of changes in these variables yields new insight into the nature of the relationships between role ambiguity, conflict and turnover intentions.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".