Change in Newcomers’ Perceived Insider Status Over Time: An Examination of its Relationships with Abusive Supervision and Well-Being
Bibliographic record
Abstract
In a time when organizations must cope with an increasingly volatile and spatially dispersed workforce, understanding how to facilitate newcomers’ perceptions of insider status is of both theoretical and practical importance. However, knowledge regarding how and why these desirable perceptions unfold over time during the socialization period is limited. Drawing on affective event theory and feelings-as-information theory, this research derives predictions about the influence of change in newcomers’ perceptions of abusive supervision and change in newcomer negative affect toward their supervisors on change in newcomers’ perceived insider status. Furthermore, considering perceived insider status through the lens of COR theory, its change is expected to have an impact on newcomers’ well-being. To test our predictions, we used a latent growth modeling (LGM) approach to analyze longitudinal data collected from newcomers working in a variety of organizations at four times across a year after organizational entry. Our results reveal a temporal process whereby change in perceptions of abusive supervision influence newcomers’ well-being and demonstrate that changes in newcomers’ negative affect toward the supervisor and in newcomers’ perceived insider status sequentially mediate these relationships. Overall, this research illustrates the temporal dynamics of the socialization process and highlights the key role of supervisors and newcomers’ affect on newcomers’ transition from outsiders to organizational insiders as well as the corresponding impact on their well-being.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".