The Effect of Ethical Leadership, Behavioural Integrity, and Moral Disengagement in Predicting Turnover Intentions During Newcomer Socialization
Bibliographic record
Abstract
One of the major reasons for newcomers voluntarily leaving organizations can be linked to inadequate socialization (Allen, 2006; Feldman, 1989; Fisher, 1986). Socialization has been described as a period of extensive learning where newcomers gather large amounts of information to reduce the uncertainty and complexity of their world. However, a unifying theory to explain how this process occurs is missing. Social learning theory offers a compelling framework to address this theoretical gap and, at the same time, it suggests that newcomers will pay attention to ethical information and that this learning will be important for turnover intentions. Socialization has been studied extensively from the process to the tactics of socialization (Ostroff & Kozlowski, 1992), but little research has examined the ethical side of socialization and its relation to turnover (Bauer & Erdogan, 2012). Drawing from the ethical leadership model, and using a sample of 297 first-year apprentices in Alberta’s oil and gas industry, this study sought to examine whether socialization influences the perceptions of organizational ethics and whether this leads to turnover intentions. A second follow-up study utilizing 800 newcomers examined whether ethical leadership perceptions explained the path between ethical socialization to turnover and eventually to deviant behaviours. Additionally, behavioural integrity of the leader and moral disengagement by the individual are examined as moderators of this model. Results showed that perceptions of organizational ethics (Study1) and perceptions of ethical leadership (Study 2) fully mediated the socialization and turnover path. The behavioural integrity of the leader was a significant moderator (such that when behavioural integrity was low, socialization had a stronger impact on turnover). Moreover, moral disengagement was also a significant moderator of organizational ethics in predicting turnover (such that when moral disengagement was high, ethical perceptions had a weaker relationship with turnover). Finally, moral disengagement interacted with turnover intentions to predict organizational deviant behaviours (when moral disengagement was high, turnover intentions was a stronger predictor of deviant behavior compared to when moral disengagement was low).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".