Why is it difficult to be virtuous in business ethics?
Bibliographic record
Abstract
This paper presents a theoretical analysis based of virtue ethics. We examine the individual internal predispositions related to non-virtuous behavior in the context of an ethical dilemma. For Aristotle, the virtuous state of being requires certain dispositions, but the difficult context of a “genuine dilemma” can generate interference and obstacles to achieving a virtuous state. The genuine dilemma is a symptomatic situation that disrupts our ethical identity by the potential biases that affect our personality traits and moral acts. These disturbances cause a phenomenon described by philosophers as “akrasia”. We propose that this “weakness of will” influences the ethical decision-making process, thereby leading to non-virtuous acts. Within the empirical literature, we identify three types of disturbances that feed akrasia: bounded ethicality, denial, and moral cowardice. These ethical biases disrupt one’s moral conscience by moving the individual away from the pursuit of virtue. Understanding these ethical errors contributes to enhancing ethical decision-making models, especially in terms of examining the failure of one’s will to act according to one’s values. We propose a conceptual model that explains non-virtuous attitudes to ethical dilemmas in management.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.043 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| 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".