Developing and Validating the Business Ethics Denial Scale
Bibliographic record
Abstract
Despite a renewed focus on ethics education in business schools, frequent and well-publicized corporate scandals have contributed to increasing pessimism about ethical behavior in business. Although largely theoretical, the suggestion that employees may deny that ethics have any place in a business context has received ample attention (Adler 2002; Ghoshal 2005; Heath 2014). However, little to no empirical work has focused on business ethics denial, in part, we argue, due to a lack of available scales with which to measure the construct. Using a sample of 815 employed participants across five separate samples we established the substantive validity of a Business Ethics Denial (BED) Scale, confirmed the theorized structure of the measure, the psychometric properties, and convergent and discriminant validity. The results suggest that the BED Scale assesses two separate but related factors of business ethics denial – pessimistic denial and normative denial – that can be used in future work to examine the antecedents and consequences of the construct.
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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 | 0.029 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".