The creation of an unethical work environment: organisational outcome‐based control systems
Bibliographic record
Abstract
While research on outcome‐based control systems (and rewards) have been shown to lead to unethical behaviour, the same cannot be said when the research focuses on specific outcome‐based control systems. Both the positive and negative research focused on the effects of moderators on the outcome‐based control system and unethical behaviour link. The relationship was dependent on ethical climate, personality traits, and internal communications. While research yielded a slightly positive result, it was shown that ethical climate was likely a stronger contributor to ethical behaviour. The influence of client fee expectations, the influence of an audit program and unethical auditor behaviour was examined. Further research was necessary to see how people of different ages responded in terms of ethics. While the empirical research did not provide a complete positive link between outcome‐based control systems and unethical behaviour, it did show that the relationship could exist and was dependent often upon other factors, such as the ethical environment of the organisation. The purpose of this article was not to show that outcome‐based control systems are always going to drive employees to behave unethically, but that these systems can lead to unethical behaviour.
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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 | 0.019 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".