Moral Socialization and Newcomer Ethicality and Adjustment: Change in Newcomer Values Over Time
Bibliographic record
Abstract
In this study, we examine the role that organizations play in (re-)shaping the moral values of newcomers during the critical early months of employment, a period when organizations aim to orient new employees to the norms and expectations of the work environment. Drawing on the organizational socialization literature, we develop a conceptual model that links moral divestiture (versus investiture) socialization to changes in the moral values of newcomers over time and subsequent outcomes, including indicators of newcomer workplace morality and traditional newcomer adjustment indicators. In a longitudinal study of newcomers and their supervisors, we find that moral divestiture tactics are associated with changes (i.e., decline) in newcomers’ moral values over the first 6 months of employment. In turn, changes in moral values mediate the positive associations between moral divestiture tactics and changes (i.e., increase) in newcomers’ willingness to engage in unethical pro-organizational behaviors and supervisor-observed unethical work behaviors. Changes in moral values further mediated the negative effects of moral divestiture tactics on newcomer organizational citizenship behaviors, task performance, and job satisfaction. We conclude with implications for theory and research on organizational moral socialization in the workplace.
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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 | 0.014 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".