(How) Will I Socialize You? The Impact of Supervisor Initial Evaluations and Subsequent Support on the Socialization of Temporary Newcomers
Bibliographic record
Abstract
This study proposes and tests a new theoretical model explaining whether, and how, supervisors socialize “temporary newcomers,” defined as new organizational members who join an organization on a temporary basis, with a potential, but uncertain, opportunity of receiving a long-term job offer in the future. We suggest that under specific conditions, supervisors first evaluate temporary newcomers’ proactivity based on whether they positively stand out by proposing new feasible ideas and by promoting their achievements. On the basis of these initial evaluations, supervisors then decide whether to increase their support of newcomers’ creativity (using an investiture approach) or to intensify newcomers’ socialization by attempting to change their behavior (using a divestiture approach). When supervisors adopt an investiture approach, it positively influences temporary newcomers’ socialization adjustment outcomes, as indicated by increased newcomer job satisfaction, social integration, task performance, organizational and task socialization, challenge stress, and reduced hindrance stress. When supervisors instead adopt a divestiture approach, it has an opposite (thus negative) effect on the same socialization outcomes. We tested our theoretical model using a mix-method design, based on a three-wave longitudinal sample of 325 newcomer–supervisor dyads spanning a wide range of companies and industries, complemented with interviews of 41 supervisors.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".