The relationship of ethical climate to deviant workplace behaviour
Bibliographic record
Abstract
Purpose The purpose of this article is to perform a literature review of the existing body of empirically‐based studies relating to the causes and implications of how the ethical climate of a company ultimately affects the incidence of workplace deviance. Design/methodology/approach The article examines the issue of ethical contexts and climates within organizations, as measured by the Ethical Climate Questionnaire developed in 1987 by Victor and Cullen , and their implications in the daily work lives of participants. The causes of unethical behaviour, including the presence of counter norms, the environment in which a firm operates, and organizational commitment, as well as the manifestation of this behaviour in the form of workplace deviance, are reviewed. Finally, current trends in preventing workplace deviance are investigated, including promoting a strong culture of ethics, and the use of “toxic handlers”, individuals who take it upon themselves to handle the frustrations of fellow employees. Findings Clearly, unethical and deviant behaviour problems are of great concern to organizations, which must take steps to solve them, at the same time as fostering strong positive ethical cultures. Feels that further studies are needed using more definitive and qualitative measurements to learn more about these behaviours. Originality/value This article would be useful to those who wish to obtain an overview of the current literature, specifically readers who do not specialize in the subject area.
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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 | 0.006 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 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".