Getting New Staff to Stay: The Mediating Role of Organizational Identification
Bibliographic record
Abstract
Newcomer turnover is a major cost to organizations, and the quality of new employees' experiences in the first few months is critical in determining whether they decide to stay or leave. In a study that focused on the first stage of newcomer socialization, we investigate the impact of perceptions of social validation from the team and the team leader, and perceived fairness of treatment on newcomers' identification with their work team and the organization, specifically measuring the group self‐investment components of identification. The mediating role of these levels of group self‐investment and of the imbalance (i.e. difference) between levels of self‐investment on turnover intentions was also tested. New staff (N=569) joining a large public‐sector organization completed a questionnaire about their socialization experiences in their first 6 months of their employment. Structural equation modelling revealed that social validation by the team and team leaders, and fairness of treatment, predict increased investment with the organization and with the team. Organizational‐level self‐investment and an imbalance in favour of investment with the organization over that of the team mediated decreases in turnover intentions. We conclude that organizations should provide newcomers with validation that promotes identification with their organization during this critical stage of socialization.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".