Managing Workplace Ethical Dilemmas, Perceptual Ethical Leadership, Accountability, and Management Outcomes: A Critical Review and Future Directions
Bibliographic record
Abstract
The inquiry of ethical dilemmas and moral predicaments continues to be a significant problem for managers in most workplace environments (Brown & Trevino, 2006). Managers oftentimes find themselves in difficult situations where they must make decisions that uphold organizational ethics, policies, and honor their morality (Brown & Trevino, 2006; Trevino, 2018; Verschoor, 2018). Mostly, employee actions put managers in these compromising situations, where they may be required to make some trying ethical decisions. Considering these perspectives, this study discusses a variety of research on employee actions and other factors that may pose ethical dilemmas to managers. The study also investigates research done by other scholars about management ethical dilemmas and tries to establish the research gaps on what researchers might not have not wholly accomplished in the past. The study proposes to take a qualitative approach to investigate what managers have been doing in the past to address the question of what they must do in the future when they encounter real-world ethical quandaries.
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 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 | 0.016 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".