Ethical Approach Scales: From Moral Theory Foundations at Work to AI-Enhanced Factor Refinement (WITHDRAWN)
Bibliographic record
Abstract
The role of ethics in business is well-established, yet employees’ ethical approaches remain underexplored through the lens of moral philosophy. To address this, we developed the Ethical Approach Scales (EAS), grounded in deontology (duty), virtue ethics, and consequentialism, and tested their factor structure and validity in three cross-sectional studies (N = 2,949). A fourth study used large language models to optimize scale dimensionality via semantic factor analysis. The EAS demonstrated robust psychometric properties and strong construct and predictive validity, outperforming the Ethical Position Questionnaire (Forsyth, 1980) in explaining ethical evaluations across six business scenarios. Results showed that high-stakes utilitarian dilemmas often assess virtue rather than consequentialism, and the EAS complements descriptive measures like Honesty-Humility (from the HEXACO personality model) and moral disengagement in predicting organizational citizenship behaviors, deviance, and counterproductive work behaviors. Findings also illustrate the intersection of ethics and cultural values, supporting a dual-process model where ethical decisions involve both intuitive (System 1) and deliberative (System 2) processes, advancing our understanding of ethical decision-making in organizations.
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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 | 0.022 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".